Functional Clustering for Longitudinal Associations between Social Determinants of Health and Stroke Mortality in the US

Understanding longitudinally changing associations between Social determinants of health (SDOH) and stroke mortality is crucial for timely stroke management. Previous studies have revealed a significant regional disparity in the SDOH -- stroke mortality associations. However, they do not develop data-driven methods based on these longitudinal associations for regional division in stroke control. To fill this gap, we propose a novel clustering method for SDOH -- stroke mortality associations in the US counties. To enhance interpretability and statistical efficiency of the clustering outcomes, we introduce a new class of smoothness-sparsity pursued penalties for simultaneous clustering and variable selection in the longitudinal associations. As a result, we can identify important SDOH that contribute to longitudinal changes in the stroke mortality, facilitating clustering of US counties into several regions based on how these SDOH relate to stroke mortality. The effectiveness of our proposed method is demonstrated through extensive numerical studies. By applying our method to a county-level SDOH and stroke mortality longitudinal data, we identify 18 important SDOH for stroke mortality and divide the US counties into two clusters based on these selected SDOH. Our findings unveil complex regional heterogeneity in the longitudinal associations between SDOH and stroke mortality, providing valuable insights in region-specific SDOH adjustments for mitigating stroke mortality.