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Cluster Analysis Methods to Support Population Health Improvement Among US Counties
Elizabeth A Pollock1, Ronald E Gangnon, Keith P Gennuso
1Department of Population Health Sciences, University of Wisconsin Population Health Institute, University of Wisconsin-Madison, Madison, Wisconsin (Drs Pollock, Gennuso and Givens); and Department of Population Health Sciences, University of Wisconsin-Madison, Madison, Wisconsin (Dr Gangnon).
Cluster analysis groups counties into 30 health categories, improving understanding of rank uncertainty. This method helps visualize county performance and identify peer groups for public health insights.
Area of Science:
- Health outcomes research
- Statistical analysis in public health
- Data visualization techniques
Background:
- Population health rankings aim to improve health by highlighting areas needing attention.
- Rankings can be misinterpreted as definitive, masking significant variations and uncertainty.
- Complex statistical models for rank uncertainty are difficult for broad audiences to understand.
Purpose of the Study:
- To explore cluster analysis as an accessible method for addressing rank imprecision.
- To create data-informed groupings that are easily communicated numerically and visually.
- To improve the interpretation of health outcome rankings.
Main Methods:
- K-means clustering with Wasserstein distance was applied to 2022 County Health Rankings (CHR) health outcomes data.
- Analysis included 3082 US counties.
- The goal was to identify natural groupings and data distribution gaps.
Main Results:
- Thirty distinct health groupings (clusters) were identified nationwide, with 9 to 184 counties per cluster.
- States averaged 16 clusters, varying by state size and population.
- This approach mitigated issues associated with using only rank estimates.
Conclusions:
- Cluster analysis offers a way to understand uncertainty in health outcome rankings.
- It enables visualization of differences and similarities between county ranks.
- Public health practitioners can use these groupings to compare county performance with similar counties.
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