Abstract 15076: County-level Subgroups With Unique Cardiovascular Outcomes: An Unsupervised Clustering Analysis

Abstract

Introduction: Regional heterogeneity in cardiovascular disease (CVD) risk has been demonstrated in the United States (US), however associations between county-level social and demographic risk factors and CVD mortality have not previously been described. To address this, we evaluated whether unsupervised machine learning-based clustering could identify distinct US county subgroups with unique CVD outcomes. Methods: The study included 2,676 counties from the 2020 County Health Rankings program. Unsupervised hierarchical clustering of 46 candidate variables encompassing demographic, health behaviors, socioeconomic factors, and healthcare access domains was used to derive phenogroups. The association of phenogroups and age-adjusted CVD mortality was assessed by linear regression. Results: Clustering identified 4 phenogroups based on within-cluster inertia. Cluster 1 (N=924; 24.5%) counties were largely white, suburban households with high income and access to healthcare. Cluster 2 counties (N=451; 16.9%) were large with predominantly Hispanic residents and below average prevalence of CVD risk factors. Cluster 3 (N=951; 35.5%) counties included rural, white residents with the lowest levels of healthcare access. Cluster 4 (350; 13.1%) counties were comprised of predominantly black residents with substantial cardiovascular comorbidities and physical and socioeconomic burdens. Age-adjusted cardiovascular mortality increased in a stepwise fashion from 247 in cluster 1 to 349 per 100,000 residents in cluster 4 (FigA) with the phenogroups demonstrating regionality across the US (FigB). Addition of phenogroup in linear regression improved model performance with a R 2 of 0.70. Conclusions: Unsupervised machine learning clustering based on demographic and behavior data can identify unique county phenogroups with differential risk of CVD mortality and may aid in identifying communities at highest risk for CVD-related adverse events.