VEHSS Composite Prevalence Estimates
2017, 2019, 2021. This dataset contains estimates of the prevalence of visual acuity loss and major eye diseases generated using a Bayesian meta-analytic modeling approach that combines information from multiple data sources to produce comprehensive estimates of prevalence by age, race, and gender at the national, state and county levels. These composite prevalence estimates are the primary surveillance measures developed by the Centers for Disease Control and Prevention’s Vision & Eye Health Surveillance System (VEHSS).

For more information about these estimates including summary tables and maps, methods, and links to related publications visit https://www.cdc.gov/visionhealth/vehss/estimates/index.html
To view this data in the VEHSS interactive data visualization application, visit https://ddt-vehss.cdc.gov/ and search for “VEHSS Composite Prevalence Estimate”.

Visual Acuity Loss:
Visual acuity loss prevalence estimates represent best-corrected visual acuity in the better-seeing eye and are included in rows where Category=’Measured Visual Acuity’. Rows with Subgroup = ‘Any vision loss' represents any impairment or blindness of 20/40 or worse; rows with Subgroup = 'US-defined blindness' refers to the subset of vision loss that is 20/200 or worse.

Age Related Macular Degeneration:
The age-related macular degeneration (AMD) estimates represent AMD as measured with retinal imaging examination, and are included in rows where Category = ‘Age Related Macular Degeneration’. The Subgroup ‘Vision threatening AMD’ includes patients with geographic atrophy, wet-form AMD, or choroidal neovascularization in either eye. The Subgroup ‘Non-vision threatening AMD’ includes patients with early or intermediate dry-form AMD defined as retinal pigment epithelium abnormalities or drusen ≥125 µm in the worse-affected eye, and do not have vision threatening AMD.

Diabetic Retinopathy:
The diabetic retinopathy (DR) estimates represent DR as measured with retinal imaging examination, and are included in rows where Category=’Diabetic Eye Diseases’. The Subgroup ‘Vision threatening DR’ includes patients with severe non-proliferative DR, proliferative DR, and diabetic macular edema. The Subgroup ‘Non-vision threatening DR’ is defined as patients with mild-moderate non-proliferative DR or unspecified DR, and do not have vision threatening DR.

Glaucoma:
VEHSS Composite Prevalence Estimates for glaucoma are forthcoming.

Age Groups:
The VEHSS Composite Prevalence Estimates are available by major age groups (All ages, ages 0-17, 18-39, 40-64, 65-84, 85+) and detailed (5-year) age groups, which are indicated by the text “by detailed age groups” in the ‘Indicator’ field.

Prevalence Data Type:
These estimates are also available as crude (Data_Value_Type = ‘Crude Prevalence’) or adjusted data (Data_Value_Type=’Adjusted Prevalence). Crude Prevalence is the estimate of the actual number and percentage of people living with each condition. Adjusted Prevalence estimates are adjusted to match the national population by age, race/ethnicity, and gender. Adjusted prevalence estimates can be used to help identify disparities in prevalence between geographic areas that are not explained by differences in demographic characteristics.

Data Sources:
Data sources for VEHSS Composite Prevalence Estimates include the National Health and Nutrition Examination Survey (NHANES), the American Community Survey (ACS), the National Survey of Children’s Health (NSCH), the Behavioral Risk Factor Surveillance System (BRFSS), Medicare Fee-For-Service claims, the Transformed Medicaid Statistical Information System, MarketScan commercial insurance claims, the Health Resources & Service Administration’s Area Resources File, and published examination study results from the Baltimore Pediatric Eye Disease Study (BPEDS), the Chinese American Eye Study (CHES), the Eye Diseases Prevalence Research Group (EDPRG), the Los Angeles Latino E

2014, 2013. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project census city-level data in GIS-friendly format can be joined with city spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-City-Boundaries/n44h-hy2j) in a geographic information system (GIS) to produce maps of 27 measures at the city-level.

This is the complete dataset for the 500 Cities project 2016 release. This dataset includes 2013, 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). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction 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. 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. More information about the methodology can be found at www.cdc.gov/500cities.
Note: During the process of uploading the 2015 estimates, CDC found a data discrepancy in the published 500 Cities data for the 2014 city-level obesity crude prevalence estimates caused when reformatting the SAS data file to the open data format. . The small area estimation model and code were correct. This data discrepancy only affected the 2014 city-level obesity crude prevalence estimates on the Socrata open data file, the GIS-friendly data file, and the 500 Cities online application. The other obesity estimates (city-level age-adjusted and tract-level) and the Mapbooks were not affected. No other measures were affected. The correct estimates are update in this dataset on October 25, 2017.

2014, 2013. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project census tract-level data in GIS-friendly format can be joined with census tract spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-Census-Tract-Boundaries/x7zy-2xmx) in a geographic information system (GIS) to produce maps of 27 measures at the census tract level.

This dataset contains model-based census tract-level estimates for the PLACES project 2020 release. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. The dataset includes estimates for 27 measures: 5 chronic disease-related unhealthy behaviors, 13 health outcomes, and 9 on use of preventive services. 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 model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2010 population data, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS because the relevant questions are only asked every other year in the BRFSS. More information about the methodology can be found at www.cdc.gov/places.

This dataset contains model-based census tract estimates. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 40 measures: 12 for health outcomes, 7 for preventive services use, 4 for chronic disease-related health risk behaviors, 7 for disabilities, 3 for health status, and 7 for health-related social needs. These estimates can be used to identify emerging health problems and to help develop and carry out 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 model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2020 population data, and American Community Survey 2018–2022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. More information about the methodology can be found at www.cdc.gov/places.

CDC is working with commercial laboratories to conduct large-scale geographic seroprevalence surveys to estimate the percentage of people who were previously infected with SARS-CoV-2, the virus that causes COVID-19 disease. The strategy involves working with state, local, territorial, academic, and commercial partners to better understand COVID-19 in the United States using serology (antibody) testing for surveillance (“seroprevalence surveys” or “serosurveys”). For the surveys, de-identified clinical blood samples are tested for antibodies to SARS-CoV-2.

This dataset contains the data used to by the Nationwide Commercial Laboratory Seroprevalence Survey interactive visualization available at https://covid.cdc.gov/covid-data-tracker/#national-lab.

Additional information is available at https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/commercial-lab-surveys.html.

2017, 2016. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project census tract-level data in GIS-friendly format can be joined with census tract spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-Census-Tract-Boundaries/x7zy-2xmx) in a geographic information system (GIS) to produce maps of 27 measures at the census tract level. There are 7 measures (all teeth lost, dental visits, mammograms, Pap tests, colorectal cancer screening, core preventive services among older adults, and sleep less than 7 hours) in this 2019 release from the 2016 BRFSS that were the same as the 2018 release.

2016, 2015. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project city-level data in GIS-friendly format can be joined with city spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-City-Boundaries/n44h-hy2j) in a geographic information system (GIS) to produce maps of 27 measures at the city-level. There are 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, cholesterol screening) in this 2018 release from the 2015 BRFSS that were the same as the 2017 release.