Disentangling regional heterogeneity in social determinants–cardiovascular disease associations in the US: a high-dimensional county clustering approach

Social determinants of health (SDOH) are key drivers of cardiovascular disease (CVD), but their relationships differ substantially across regions, necessitating the design of locally tailored intervention strategies. In this work, we propose a novel clustering approach to partition US counties into different clusters based on similarities in their SDOH-CVD associations, providing a data-driven regional partition to inform region-specific interventions. To enhance the interpretability of the clustering result, we adopt the SDOH framework from the Agency for Healthcare Research and Quality to group SDOH variables into 5 domains, and build on this to develop an SDOH-domain-aware regularization (SDAR), which identifies the most influential variables within each SDOH domain tailored for each cluster. By embedding SDAR into the clustering process via a regularized expectation-maximization algorithm, our approach enables simultaneous clustering, parameter estimation, and variable selection, thereby mitigating error propagation across stages. We validate the method through theoretical developments and simulations. Applied to a national longitudinal dataset of US county-level SDOH and CVD mortality, our approach discovered 7 distinct clusters, each characterized by a unique set of SDOH drivers spanning multiple SDOH domains. Notably, 2 of these clusters exhibited elevated CVD mortality and inverse SDOH-CVD associations compared to the other 5. These findings reveal complex regional heterogeneity in SDOH-CVD associations, yielding insights to guide multi-domain, region-specific strategies for CVD management.