US 500 Cities Project Local Data for Better Health

$179 / year

This is the complete dataset for the 500 Cities project. It includes 2013 and 2014 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9).

Complexity

The data in this dataset were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The 500 Cities project was funded by the Robert Wood Johnson Foundation (RWJF) in collaboration with the CDC Foundation. It represents a first-of-its-kind effort to release information on a large scale for cities and for small areas within those cities.

The purpose of the 500 Cities Project is to provide city- and census tract-level small area estimates for chronic disease risk factors, health outcomes, and clinical preventive service use for the largest 500 cities in the United States. These small area estimates will allow cities and local health departments to better understand the burden and geographic distribution of health-related variables in their jurisdictions, and assist them in planning public health interventions.

It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2013, 2014), Census Bureau 2010 census population data, and American Community Survey (ACS) 2009-2013, 2010-2014 estimates.

The 27 Chronic Disease measures for each of the three categories are defined as follows:
– Unhealthy Behaviors
– Binge drinking among adults aged ≥ 18 years
– Current smoking among adults aged ≥18 years
– No leisure-time physical activity among adults aged ≥18 years
– Obesity among adults aged ≥18 years
– Sleeping less than 7 hours among adults aged ≥18 years

– Health Outcomes
– Arthritis among adults aged≥18 years
– Current asthma among adults aged≥18 years
– High blood pressure among adults aged≥18 years
– Cancer (excluding skin cancer) among adults aged≥18 years
– High cholesterol among adults aged≥18 years who have been screened in the past 5 years
– Chronic kidney disease among adults aged≥18 years
– Chronic obstructive pulmonary disease among adults aged≥18 years
– Coronary heart disease among adults aged≥18 years
– Diagnosed diabetes among adults aged≥18 years
– Mental health not good for≥14 days among adults aged≥18 years
– Physical health not good for≥14 days among adults aged≥18 years
– All teeth lost among adults aged≥65 years
– Stroke among adults aged≥18 years

– Prevention
– Current lack of health insurance among adults aged 18–64 years
– Visits to doctor for routine checkup within the past year among adults aged≥18 years
– Visits to dentist or dental clinic among adults aged ≥18 years
– Taking medicine for high blood pressure control among adults aged≥18 years with high blood pressure
– Cholesterol screening among adults aged≥18 years
– Mammography use among women aged 50–74 years
– Papanicolaou smear use among adult women aged 21–65 years
– Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50–75 years
– Older adults aged≥65 years who are up to date on a core set of clinical preventive services by age and sex

In this dataset, the data value unit used is percentage. Also the estimates for population less than 50 have been suppressed.

Date Created

2016-10-28

Last Modified

2016-12-01

Version

2016-12-01

Update Frequency

Irregular

Temporal Coverage

2013-2014

Spatial Coverage

United States

Source

John Snow Labs; Centers for Disease Control and Prevention;

Source License URL

Source License Requirements

N/A

Source Citation

N/A

Keywords

Local Data in Largest Cities in US, Local Data in Biggest Cities in the US, Health Problems in Major Cities in the US, Census Tracts in Cities in America, US Chronic Diseases, US Health Outcomes, US Health Prevention

Other Titles

500 U.S. Cities Data for Better and Effective Health, Preventing Chronic Diseases in U.S. Cities Through Implementing Public Targeted Health Activities

NameDescriptionTypeConstraints
YearIt indicates the year of data.daterequired : 1
State_AbbreviationIt indicates the abbreviation of different states.stringrequired : 1
StateIt includes the description of different state.stringrequired : 1
City_NameIt contains the name of different cities.string-
Geographic_LevelIt Identifies either US, City or Census Tract.stringrequired : 1
Measure_CategoryIt includes the measure category like Prevention, Health Outcomes, Unhealthy Behaviors of different cities.stringrequired : 1
Unique_IDAt city-level, it is the FIPS code of CityFIPS. For tract-level data, it is a combined ID of CityFIPS and TractFIPS for tracts within the respective city with the exception of Honolulu, which only uses TractFIPS.stringrequired : 1
Measure_ID_FullIt includes the full form for measure identifiers.stringrequired : 1
Data_Value_Type_IDIt Identifies for the data value type.stringrequired : 1
Data_Value_TypeThe data type, such as age-adjusted prevalence or crude prevalence.stringrequired : 1
Data_ValueIt identifies the Data Value, such as 14.7.numberlevel : Nominal
Low_Confidence_LimitIt identifies the lower confidence limit of the number.numberlevel : Nominal
High_Confidence_LimitIt identifies the high confidence limit of the number.numberlevel : Nominal
Population_CountIt identifies the Population count from census 2010.integerlevel : Ratio
Geo_Location_LatitudeIt indicates the Latitude of city or census tract centroid.number-
Geo_Location_LongitudeIt indicates the Longitude of city or census tract centroid.number-
Category_IDIt identifies the category ID such as PREVENT, HLTHOUT, UNHBEH.stringrequired : 1
Measure_ID_ShortIt refers to the short form for measure identifier.stringrequired : 1
City_FIPSIt indicates the FIPS code of different cities. The FIPS state code is a numeric Federal Information Processing Standards (FIPS) code which uniquely identifies state and certain other associated areas within U.S.integerlevel : Nominal
Tract_FIPSIt indicates the Tract FIPS codes that uniquely identify the data. The FIPS state code is a numeric Federal Information Processing Standards (FIPS) code which uniquely identifies state and certain other associated areas within U.S.integerlevel : Nominal
Short_Question_TextIt indicates the Measure short name.stringrequired : 1
YearState AbbreviationStateCity NameGeographic LevelMeasure CategoryUnique IDMeasure ID FullData Value Type IDData Value TypeData ValueLow Confidence LimitHigh Confidence LimitPopulation CountGeo Location LatitudeGeo Location LongitudeCategory IDMeasure ID ShortCity FIPSTract FIPSShort Question Text
2015OHOhioDaytonCensus TractHealth Outcomes3921000-39113080700High cholesterol among adults aged >=18 Years who have been screened in the past 5 YearsCrdPrvCrude prevalence56.254.358.154939.81228372-84.16677201HLTHOUTHIGHCHOL392100039113080700.0High Cholesterol
2015AZArizonaPhoenixCensus TractHealth Outcomes0455000-04013113400High cholesterol among adults aged >=18 Years who have been screened in the past 5 YearsCrdPrvCrude prevalence40.737.544.368433.45495925-112.02581119999999HLTHOUTHIGHCHOL4550004013113400.0High Cholesterol
2015NYNew YorkNew YorkCensus TractPrevention3651000-36005038000Taking medicine for high blood pressure control among adults aged >=18 Years with high blood pressureCrdPrvCrude prevalence80.179.181.1458540.87809354-73.86244152PREVENTBPMED365100036005038000.0Taking BP Medication
2015CACaliforniaMoreno ValleyCensus TractHealth Outcomes0649270-06065042214High blood pressure among adults aged >=18 YearsCrdPrvCrude prevalence26.125.426.9523933.96452735-117.2723592HLTHOUTBPHIGH6492706065042214.0High Blood Pressure
2015KYKentuckyLouisvilleCensus TractPrevention2148006-21111010318Cholesterol screening among adults aged >=18 YearsCrdPrvCrude prevalence82.280.983.3307138.30104518-85.60100646PREVENTCHOLSCREEN214800621111010318.0Cholesterol Screening
2015OHOhioToledoCensus TractPrevention3977000-39095006200Cholesterol screening among adults aged >=18 YearsCrdPrvCrude prevalence76.374.378.4250441.70046815-83.59540757PREVENTCHOLSCREEN397700039095006200.0Cholesterol Screening
2015HIHawaiiHonoluluCensus TractPrevention15003006000Taking medicine for high blood pressure control among adults aged >=18 Years with high blood pressureCrdPrvCrude prevalence77.776.778.7542121.33242188-157.882351PREVENTBPMED1500315003006000.0Taking BP Medication
2015CACaliforniaGarden GroveCensus TractPrevention0629000-06059088902Taking medicine for high blood pressure control among adults aged >=18 Years with high blood pressureCrdPrvCrude prevalence71.970.873.0514333.76321387-117.94631840000001PREVENTBPMED6290006059088902.0Taking BP Medication
2015AZArizonaPhoenixCensus TractPrevention0455000-04013116703Cholesterol screening among adults aged >=18 YearsCrdPrvCrude prevalence74.372.376.2444833.36791145-112.05764109999998PREVENTCHOLSCREEN4550004013116703.0Cholesterol Screening
2015FLFloridaTampaCityPrevention1271000Cholesterol screening among adults aged >=18 YearsCrdPrvCrude prevalence73.773.573.933570927.99619804-82.44503498PREVENTCHOLSCREEN1271000Cholesterol Screening