• The method of generating small area estimation (SAE) of the measures is a multilevel statistical modeling framework.
  • Specifically, CDC uses an innovative peer-reviewed multilevel regression and poststratification (MRP) approach that links geocoded health surveys and high spatial resolution population demographic and socioeconomic data.
  • The approach predicts individual disease risk and health behaviors in a multilevel modeling framework and estimates the geographic distributions of population disease burden and health behaviors.
  • A multilevel logistic regression model for each outcome is constructed, which includes individual-level age, sex, race/ethnicity, and education 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 MRP approach is flexible and will help CDC provide modeled estimates of the prevalence for each indicator at the census tract and city levels.
  • Small area estimates using this MRP approach have been published using data from CDC’s BRFSS and the National Survey of Children’s Health.
  • CDC’s internal and external validation studies confirm the strong consistency between MRP model-based SAEs and direct BRFSS survey estimates at both state and county levels.
  • The primary data sources for this project are CDC’s BRFSS, Census 2010 population counts, annual Census county population estimates, and ACS estimates.
  • The 36 measures include 13 health outcomes, 9 prevention practices, 4 health risk behaviors, 7 disability measures (new for the 2023 release), and 3 health status measures.
  • The measures include major risk behaviors that lead to illness, suffering, and early death related to chronic diseases and conditions, as well as the conditions and diseases that are the most common, costly, and preventable of all health problems.
  • Each measure has a comprehensive definition that includes the background, significance, limitations of the indicator, data source, and limitations of the data resources.
  • Measures complement existing sets of surveillance indicators that report state, metropolitan area, and county data, including County Health Rankings and Chronic Disease Indicators.
  • The 95% confidence intervals (CIs) of modelled estimates are generated using a Monte Carlo simulation.
List of Measures