Identifying Natural Groupings of Small Areas Based on Health Outcomes to Support Population Health

Music City Center 

Population health rankings can be a catalyst for the improvement of health by drawing attention to areas in need of relative improvement and summarizing complex information in a manner understood by almost everyone. We explore data-informed grouping (cluster analysis) as an easier-to-understand, empirical technique to account for rank imprecision that can be effectively communicated both numerically and visually. We consider k-means clustering as a simple approach to identify natural and meaningful groupings and gaps in health outcomes using a bias-corrected Wasserstein (earth mover's) distance to select the number of groups. Application to the 2022 County Health Rankings and Roadmaps identified 30 groupings (clusters) with sizes ranging from 9 to 184 counties. The method helped address many of the issues that arise from providing rank estimates alone. Public health practitioners can use this information to understand uncertainty in ranks, visualize distances between county ranks, have context around which counties are not meaningfully different from one another, and compare county performance to peer counties.

Ranking

Clustering

Small area estimation 

Section on Statistics in Epidemiology