Others titles
- 500 U.S. Cities Data for Better and Effective Health
- Preventing Chronic Diseases in U.S. Cities Through Implementing Public Targeted Health Activities
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
US 500 Cities Project Local Data for Better Health
This is the complete dataset for the 500 Cities project. It includes 2017 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).
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Description
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 (2015, 2016,2017), 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.
About this Dataset
Data Info
Date Created | 2016-10-28 |
---|---|
Last Modified | 2023-08-25 |
Version | 2023-08-25 |
Update Frequency |
Irregular |
Temporal Coverage |
2017 |
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 |
Data Fields
Name | Description | Type | Constraints |
---|---|---|---|
Year | It indicates the year of data. | date | required : 1 |
State_Abbreviation | It indicates the abbreviation of different states. | string | required : 1 |
State | It includes the description of different state. | string | required : 1 |
City_Name | It contains the name of different cities. | string | - |
Geographic_Level | It Identifies either US, City or Census Tract. | string | required : 1 |
Measure_Category | It includes the measure category like Prevention, Health Outcomes, Unhealthy Behaviors of different cities. | string | required : 1 |
Unique_ID | At 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. | string | required : 1 |
Measure_ID_Full | It includes the full form for measure identifiers. | string | required : 1 |
Data_Value_Type_ID | It Identifies for the data value type. | string | required : 1 |
Data_Value_Type | The data type, such as age-adjusted prevalence or crude prevalence. | string | required : 1 |
Data_Value | It identifies the Data Value, such as 14.7. | number | level : Nominal |
Low_Confidence_Limit | It identifies the lower confidence limit of the number. | number | level : Nominal |
High_Confidence_Limit | It identifies the high confidence limit of the number. | number | level : Nominal |
Population_Count | It identifies the Population count from census 2010. | integer | level : Ratio |
Geo_Location_Latitude | It indicates the Latitude of city or census tract centroid. | number | - |
Geo_Location_Longitude | It indicates the Longitude of city or census tract centroid. | number | - |
Category_ID | It identifies the category ID such as PREVENT, HLTHOUT, UNHBEH. | string | - |
Measure_ID_Short | It refers to the short form for measure identifier. | string | - |
City_FIPS | It 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. | integer | level : Nominal |
Tract_FIPS | It 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. | integer | level : Nominal |
Short_Question_Text | It indicates the Measure short name. | string | - |
Data Preview
Year | State Abbreviation | State | City Name | Geographic Level | Measure Category | Unique ID | Measure ID Full | Data Value Type ID | Data Value Type | Data Value | Low Confidence Limit | High Confidence Limit | Population Count | Geo Location Latitude | Geo Location Longitude | Category ID | Measure ID Short | City FIPS | Tract FIPS | Short Question Text |
2017 | CA | California | Hawthorne | Census Tract | Health Outcomes | 0632548-06037602504 | Arthritis among adults aged >=18 Years | CrdPrv | Crude prevalence | 14.6 | 13.9 | 15.2 | 4407 | 33.90554792 | -118.33733229999999 | HLTHOUT | ARTHRITIS | 632548 | 6037602504.0 | Arthritis |
2017 | CA | California | Hawthorne | City | Unhealthy Behaviors | 632548 | Current smoking among adults aged >=18 Years | CrdPrv | Crude prevalence | 15.4 | 15.0 | 15.9 | 84293 | 33.9146677 | -118.34766770000002 | UNHBEH | CSMOKING | 632548 | Current Smoking | |
2017 | CA | California | Hayward | City | Health Outcomes | 633000 | Coronary heart disease among adults aged >=18 Years | AgeAdjPrv | Age-adjusted prevalence | 4.8 | 4.7 | 4.8 | 144186 | 37.63295916 | -122.07705109999999 | HLTHOUT | CHD | 633000 | Coronary Heart Disease | |
2017 | CA | California | Hayward | City | Unhealthy Behaviors | 633000 | Obesity among adults aged >=18 Years | CrdPrv | Crude prevalence | 24.2 | 24.1 | 24.4 | 144186 | 37.63295916 | -122.07705109999999 | UNHBEH | OBESITY | 633000 | Obesity | |
2017 | CA | California | Hemet | City | Prevention | 633182 | Cholesterol screening among adults aged >=18 Years | AgeAdjPrv | Age-adjusted prevalence | 78.0 | 77.6 | 78.3 | 78657 | 33.73522773 | -116.994605 | PREVENT | CHOLSCREEN | 633182 | Cholesterol Screening | |
2017 | CA | California | Indio | Census Tract | Health Outcomes | 0636448-06065045213 | Arthritis among adults aged >=18 Years | CrdPrv | Crude prevalence | 22.0 | 21.1 | 22.8 | 5006 | 33.71446171 | -116.2582463 | HLTHOUT | ARTHRITIS | 636448 | 6065045213.0 | Arthritis |
2017 | CA | California | Indio | City | Unhealthy Behaviors | 636448 | Binge drinking among adults aged >=18 Years | AgeAdjPrv | Age-adjusted prevalence | 17.7 | 17.5 | 17.9 | 76036 | 33.72980678 | -116.2372581 | UNHBEH | BINGE | 636448 | Binge Drinking | |
2017 | CA | California | Indio | City | Health Outcomes | 636448 | Chronic obstructive pulmonary disease among adults aged >=18 Years | AgeAdjPrv | Age-adjusted prevalence | 6.0 | 5.8 | 6.2 | 76036 | 33.72980678 | -116.2372581 | HLTHOUT | COPD | 636448 | COPD | |
2017 | CA | California | Inglewood | Census Tract | Health Outcomes | 0636546-06037601801 | Diagnosed diabetes among adults aged >=18 Years | CrdPrv | Crude prevalence | 12.7 | 12.0 | 13.5 | 2472 | 33.94397113 | -118.34993770000001 | HLTHOUT | DIABETES | 636546 | 6037601801.0 | Diabetes |
2016 | CA | California | Inglewood | City | Prevention | 636546 | Mammography use among women aged 50â74 Years | CrdPrv | Crude prevalence | 82.5 | 82.0 | 83.0 | 109673 | 33.9565748 | -118.3444449 | PREVENT | MAMMOUSE | 636546 | Mammography |