Correct. We are not producing estimates for individuals, only aggregated results for counties, places, census tracts or ZCTAs. There is one estimate per measure for the entire population of each county, place, tract or ZCTA. The modeling process uses individual-level responses and includes county- and state-level contextual effects (random effects) to estimate the probability of developing an outcome at the individual level, given their age, race/ethnicity, sex, education, and county-level poverty. We apply these probabilities to the target population (i.e., county, place, census tract or ZCTA) to derive the estimated prevalence. So, the project uses a combination of individual characteristics and responses, as well as county and state context.
There is one estimate per measure for the entire population of each county, place, census tract or ZCTA.
It is a priority for CDC to ensure that the data are made available in a manner that facilitates local use; therefore, we have provided “GIS friendly” data files for all four levels of geography. In addition, we have provided the GIS boundary files (also known as shapefiles) that enable users of geographic information systems to create their own maps. The files can be accessed at the Chronic Data Portal. Since the 2019 release, we have started to provide services on Esri’s Living Atlas of World. In the 2020 release, our interactive maps are based on ArcGIS Online service. Users should be able to add this service to their desktop application (e.g., ArcGIS Pro) or when making maps online at ArcGIS.com web map and overlay their own local data with it. They can also utilize services from other organizations, for example the Census Bureau American Community Survey (ACS) social economic data, to access the impact of social determinants of health.
Confidence intervals are presented alongside the data estimates.
Details on the validation are available in the articles that are cited on the PLACES Project website. Sensitivity and specificity analyses were not applicable to this type of modeling procedure and thus were not conducted.
There are no individual-level data. The data estimates are aggregated to the county, place, census tract, or ZCTA levels.
The SAE for each local area is dependent mainly upon the demographic characteristics of that local area, but they also are affected by the county- and state-level context that was included as random effects in the modeling procedure.
We cannot include census tract as a random effect. However, we do not assume that there is no variation across census tracts. In the prediction step, we incorporate tract-level poverty; in addition, differences in the population demographics of the blocks that make up the census tracts are also considered in the prediction step.
County-level poverty was used in the first step of the modeling procedure, because that is the smallest geographic level that corresponds to the geocode available for the BRFSS survey respondent. In the prediction step of the modeling process we do use census-tract poverty estimates.
We hope individuals in local areas, more familiar with local definitions and conceptualizations of neighborhoods, make use of these data in their own public health and outreach efforts. It would be technically possible, for instance, for place-level data to be downloaded from PLACES and then incorporated into a local website—perhaps even GIS-enabled maps that include overlays of the boundaries of local neighborhoods as defined by the community. The PLACES Project now also includes census ZCTA-level estimates. Usually census ZCTA can be viewed as equivalent to postal ZIP Code, but be aware that postal ZIP Code changes over the time whereas census ZCTA remains same for 10 years. Find a ZCTA to ZIP Code crosswalk fileexternal icon.
The preventive measures and core unhealthy behaviors were selected based on the following factors:
- Amenable to public health intervention.
- Reflect public health priorities to address leading causes of morbidity and mortality.
- Uses preventive services to be consistent with US Preventive Services Task Force recommendations.
- Exhibit substantial, meaningful variation at the local level.
- Can be estimated for small area levels from existing, regularly-collected surveillance data—BRFSS.
- Fills a niche for health data at the local level, which are not presently available, although not duplicating health-related data available elsewhere.
- Compliments similar state-level measures available elsewhere.