FUNCTIONAL CLUSTERING FOR LONGITUDINAL ASSOCIATIONS BETWEEN SOCIAL DETERMINANTS OF HEALTH AND STROKE MORTALITY IN THE U.S.

Understanding the longitudinally changing associations between Social Determinants of Health (SDOH) and stroke mortality is essential for effective stroke management. Previous studies have uncovered significant regional disparities in the associations between SDOH and stroke mortality. However, existing studies have not utilized longitudinal associations to develop datadriven methods for regional division in stroke control. To fill this gap, we propose a novel clustering method to analyze SDOH-stroke mortality associations across U.S. counties. To enhance the interpretability of the clustering outcomes, we introduce a novel regularized expectation-maximization algorithm equipped with sparsity-and smoothness-pursued penalties, aiming at simultaneous clustering and variable selection in longitudinal associations. As a result, we can identify key SDOH that contributes to longitudinal changes in stroke mortality. This facilitates the clustering of U.S. counties based on the associations between these SDOH and stroke mortality. The effectiveness of our proposed method is demonstrated through extensive numerical studies. By applying our method to longitudinal data on SDOH and stroke mortality at the county level, we identify 18 important SDOH for stroke mortality and divide the U.S. 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 into region-specific SDOH adjustments for mitigating stroke mortality.

Duke Scholars

Author Jianbin Tan