Exploring Public Health with CDC's PLACES Data
The CDC, in collaboration with the Robert Wood Johnson Foundation, has developed an innovative tool called PLACES that provides access to local health data across the United States. By providing detailed health data for small areas—such as ZIP codes, census tracts, and cities- PLACES helps local health departments, regardless of area size or population, to better understand and manage health risks.
Utilizing advanced multilevel regression and poststratification (MRP) methodology, PLACES models health-related measures from extensive surveys like the Behavioral Risk Factor Surveillance System (BRFSS). This approach ensures that even the smallest communities have reliable data on health behaviors, disease risks, and preventative practices, aiding in targeted public health interventions.
With PLACES, policymakers and public health professionals can now access a breadth of data, including health measures ranging from chronic disease prevalence to health risk behaviors, allowing for more precise and effective public health strategies. This data not only enhances understanding but also supports the development of programs tailored to specific community needs, ultimately aiming to reduce healthcare disparities and improve overall public health outcomes.
Measures are grouped into seven categories: Health Outcomes (13), Prevention (9), Health Risk Behaviors (4), Disabilities (7), Health Status (3), Health-Related Social Needs (7), and Social Determinants of Health (SDOH) (9).
Methodology
Source: CDC PLACES methodology
PLACES uses a multilevel regression and poststratification (MRP) method to generate estimates of each measure at the county, place (incorporated and census designated), ZIP Code Tabulation Area (ZCTA), and census tract levels for adults ≥18 years in the US.
A multilevel logistic regression model is constructed for each measure. It includes some or all of the following variables based on model performance and final prediction: individual-level age, sex, race/ethnicity, and education level from CDC’s Behavioral Risk Factor Surveillance System (BRFSS); county-level percentage of adults below 150% of the federal poverty level from the 5-year American Community Survey (ACS); and state- and county-level random effects. The model is applied to annual county-level census population estimates to compute a predicted probability of having each outcome. The county-level estimates are obtained by multiplying the probability by the total adult population of each county.
The model is applied to decennial 2020 (2010 for releases 2023 and before) census block-level population counts to compute a predicted probability as well. The estimated prevalence can be obtained by multiplying the probability by the total adult population for each block, which can be aggregated to place, census tract, and ZCTA levels.
Monte Carlo simulation is used to generate 1,000 simulated datasets for the point estimate, the final estimates are reported as the mean and 95% confidence interval (the 2.5th, 97.5th percentiles) over 1,000 draws.
The MRP approach is flexible by modeling nationally and predicting locally and can be used to provide modeled estimates at any geography above the census blocks (the smallest census geography).
Both internal and external validation studies showed strong/moderate correlations between model-based estimates and direct survey estimates at state, county, and place levels.
The primary data sources for PLACES are CDC’s BRFSS, decennial census 2020/2010 population counts, annual (intercensal) county-level census population estimates, and 5-year ACS data.
For further details on how PLACES works and its impact, visit the CDC's official page on PLACES.
