Others titles

  • US Big Cities Behavioral Health Risks Surveillance
  • Prevalence Of Health Risk And Protection Factors In US Cities

Keywords

  • US Big Cities Risk Factors
  • Health Risk Factors Prevalence
  • Cigarette Smoking Prevalence
  • Obesity Prevalence
  • Prevalence Of Binge-Drinking
  • Prevalence Of Physical Activity

Big Cities Behavioural Health Risk And Protection Factors

This dataset contains estimates of health risk and protection factors prevalence, shared by the Big Cities Health Coalition members represented by the largest metropolitan health departments in the United States. The estimated values of prevalence cover the 2010-2016 period and are described by gender and race/ethnicity.

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Description

The source of data is represented by The Big Cities Health Coalition (BCHC), a forum for the leaders of America’s largest metropolitan health departments to exchange strategies and jointly address issues to promote and protect the health and safety of the people they serve. BCHC is a project of the National Association of County and City Health Officials (NACCHO), which represents the nation’s 2,800 local governmental health departments.

Most of the data came directly from cities, while some were secured from the U.S. Census or other similar publicly available dataset where city data were available. For the most part, jurisdictions reported their three most recent years of data, which were 2014, 2015, and 2016. Data prior to 2010 were not included, even if it meant a jurisdiction only had two years of data available. The nature of the data varies considerably. When data were not provided or available, the appropriate cell was left blank. Not all health departments were able to provide data for all indicators and, in cases where denominators were too small, certain rates for subpopulations were not displayed.

Most data were reviewed by individual cities as well. Where sample sizes allow, indicators are broken down into subpopulations for race and ethnicity categories. For most jurisdictions, the default options were White (Non-Hispanic), Black (Non-Hispanic), Hispanic, Asian/Pacific Islander, American Indian/Alaska Native, and Other. In areas where certain populations were too small, the various subpopulations were included in the “other” category with any additional racial/ethnic minorities. In many of the California cities, as well as Seattle, reported numbers only represent Asians; Pacific Islanders are not included. Some jurisdictions also report mixed-race numbers, and where they do, those numbers are reported as “Multi-racial”.

The health risk or protection factors for which prevalence level estimated values are included, are as follows:

– health risk factors: binge-drinking, obesity and cigarette smoking;

– health protection factors: physical activity level according to CDC recommendations.

Although the health risk and health protection factors are generally denominated briefly health risk factors, in order to underline the fact that physical activity level assessment determined the number of persons which are compliant with the CDC recommendations (and not the persons who are not compliant), the denomination used for the dataset in this case is more detailed and it specifies that includes a health protection factor. Exposure to a health protection factor has a positive effect on health.

The percentage of adults who binge drank is based (in most cases) on the Behavioral Risk Factor Surveillance System. (BRFSS) question about how many drinks a person had on one occasion in the past 30 days. Women who answered “four” and men who answered “five” are considered binge drinkers. Similarly, percent of high school students who binge drank is based (in most cases) on the Youth Risk Behavior Surveillance System (YRBS/YRBSS) question regarding five or more drinks within a couple hours on one or more occasions in the past 30 days. As with other indicators, if BRFSS or YRBS/YRBSS data were not available, a comparable survey was used, or the data were left out if not completely comparable.

Where possible, the adult obesity figure in this report is the percentage of the population 18 years or over that is considered obese, generally with a body mass index (BMI) of 30 or above, and in most cases is taken from BRFSS. Similarly, obesity rates for children are difficult to collect, though many jurisdictions know the percent of high school students that are obese, particularly in large urban school districts. In children, the definition is a BMI at or above the 95th percentile of children of the same age or sex. Physical activity data were taken from BRFSS or YRBS/YRBSS based on CDC-recommended activity levels. For adults: at least 2 hours, 30 minutes of moderate-intensity aerobic activity every week for good health; 1 hour, 15 minutes of vigorous-intensity aerobic activity; or an equivalent mix of moderate and vigorous. For high school students: physically active for a total of at least 60 minutes per day. Where possible, this publication relied on BRFSS or YRBS/YRBSS so that data were comparable. In most cases, if data were not comparable, they were excluded.

Data on cigarette use among both adults (over 18 years of age) and youth (for the most part, high school students) are also hard to obtain at the city level. The most frequently used sources of data are the BRFSS, the Youth Risk Behavioral Survey (YRBS), or the Youth Risk Behavioral Surveillance System (YRBSS). Many cities/counties oversample to have accurate data for the jurisdictions, but some do not. Sample sizes vary, as do years of data available. Youth tobacco numbers were included only if they were secured via YRBS or YRBSS or a comparable survey, both in terms of population (high school students) and question text. Readers should take note of both source and year of data availability when using the tobacco- related data in this publication.

Dataset contains the BCHC requested methodology for every indicator, along with sources of data used by the BCHC member and notes about the methods and data.

About this Dataset

Data Info

Date Created

2015-11

Last Modified

2019-03-04

Version

2019-03-04

Update Frequency

Irregular

Temporal Coverage

2010-2016

Spatial Coverage

United States

Source

John Snow Labs; Big Cities Health Coalition;

Source License URL

Source License Requirements

N/A

Source Citation

N/A

Keywords

US Big Cities Risk Factors, Health Risk Factors Prevalence, Cigarette Smoking Prevalence, Obesity Prevalence, Prevalence Of Binge-Drinking, Prevalence Of Physical Activity

Other Titles

US Big Cities Behavioral Health Risks Surveillance, Prevalence Of Health Risk And Protection Factors In US Cities

Data Fields

Name Description Type Constraints
Behavioural_Health_Risk_Or_Protection_FactorOne of the health or protection factors and the population age groupstringrequired : 1
YearThe year in the interval 2010-2015 to which the estimated value of prevalence correspondsdaterequired : 1
GenderValue: Male, Female, Bothstringrequired : 1
Race_EthnicityThe race/ethnicity (White Non-Hispanic, Black Non-Hispanic, Hispanic, Asian/Pacific Islander, American Indian/Alaska Native or Other) to which the estimated value of prevalence corresponds or all of themstringrequired : 1
Health_Risk_Or_Protection_Factor_PrevalenceThe estimated value of prevalence level (percent of exposure) for a health risk or protection factor, among the population of an age group, location, gender, race/ethnicity or all from the interval years between 2010-2015numberlevel : Ratio
Lower_Limit_95_Percent_Confidence_IntervalThe lower limit of the confidence interval, for the estimated value of prevalence, determined with a degree of 95% confidencenumberlevel : Ratio
Upper_Limit_95_Percent_Confidence_IntervalThe upper limit of the confidence interval, for the estimated value of prevalence, determined with a degree of 95% confidencenumberlevel : Ratio
City_Or_County_NameThe city or county to which the estimated value of prevalence level corresponds or US levelstring-
StateThe name of US state where the city or county, to which the estimated value of prevalence level corresponds, is locatedstring-
State_AbbreviationThe abbreviated name of US state where the city or county, to which the estimated value prevalence corresponds, is locatedstring-
BCHC_Requested_Methodology_For_DataThe Big Cities Health Coalition requested methodology for the estimation of prevalence levels for a type of health risk or protection factor and an age groupstringrequired : 1
Data_SourcesThe sources of the data used to estimate the prevalence level of a health risk or protection factor, among the population of a specific age, for a specific location and yearstring-
Methods_And_Data_NotesNotes about the method used to estimate the level of prevalence and about the data used in the processstring-

Data Preview

Behavioural Health Risk Or Protection FactorYearGenderRace EthnicityHealth Risk Or Protection Factor PrevalenceLower Limit 95 Percent Confidence IntervalUpper Limit 95 Percent Confidence IntervalCity Or County NameStateState AbbreviationBCHC Requested Methodology For DataData SourcesMethods And Data Notes
Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)2010BothAll1.7Washington DCAge-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 usedD.C. Department of Health, Center for Policy, Planning and Evaluation, Vital Statistics mortality files 2010-2014 (Final as of October 27, 2016)
Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)2010BothAll2.21.53.0Tarrant CountyFort Worth TXAge-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, 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
Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)2010BothAll2.31.63.2Alameda CountyOakland CAAge-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.CDC WonderAge-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.
Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)2010BothAll3.02.23.9San Antonio TXAge-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, 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
Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)2010BothAll4.44.44.5U.S. TotalAge-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 usedCDC WONDER
Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)2010BothAll5.24.46.0San Diego County CAAge-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, 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 PMAll data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:"http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population."The method used to calculate age-adjusted rates is documented here: More information:"http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates." Non-stated ethnicities were not included in the analysis.
Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)2010BothAll5.4Kansas City MOAge-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.
Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)2010BothAll6.3Denver COAge-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, 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
Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)2010BothAll11.39.812.8Clark CountyLas Vegas NVAge-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, 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
Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)2010BothAll11.89.114.9Columbus OHAge-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