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We consider the problem in precision health of grouping people intosubpopulations based on their degree of vulnerability to a risk factor. Thesesubpopulations cannot be discovered with traditional clustering techniquesbecause their quality is evaluated with a supervised metric: the ease ofmodeling a response variable over observations within them. Instead, we applythe supervised cadre model (SCM), which does use this metric. We extend the SCMformalism so that it may be applied to multivariate regression and binaryclassification problems. We also develop a way to use conditional entropy toassess the confidence in the process by which a subject is assigned theircadre. Using the SCM, we generalize the environment-wide association study(EWAS) workflow to be able to model heterogeneity in population risk. In ourEWAS, we consider more than two hundred environmental exposure factors and findtheir association with diastolic blood pressure, systolic blood pressure, andhypertension. This requires adapting the SCM to be applicable to data generatedby a complex survey design. After correcting for false positives, we found 25exposure variables that had a significant association with at least one of ourresponse variables. Eight of these were significant for a discoveredsubpopulation but not for the overall population. Some of these associationshave been identified by previous researchers, while others appear to be novel.We examine several discovered subpopulations in detail, and we find that theyare interpretable and that they suggest further research questions.

A Precision Environment-Wide Association Study of Hypertension via Supervised Cadre Models