Covariate-adaptive clustering of exposures for air pollution epidemiology cohorts

Abstract

Cohort studies in air pollution epidemiology aim to establish associations between health outcomes and air pollution exposures. Statistical analysis of such associations is complicated by the multivariate nature of the pollutant exposure data as well as the spatial misalignment that arises from the fact that exposure data are collected at regulatory monitoring network locations distinct from cohort locations. We present a novel clustering approach for addressing this challenge. Specifically, we present a method that uses geographic covariate information to cluster multi-pollutant observations and predict cluster membership at cohort locations. Our predictive $k$-means procedure identifies centers using a mixture model and is followed by multiclass spatial prediction. In simulations, we demonstrate that predictive $k$-means can reduce misclassification error by over 50% compared to ordinary $k$-means, with minimal loss in cluster representativeness. The improved prediction accuracy results in large gains of 30% or more in power for detecting effect modification by cluster in a simulated health analysis. In an analysis of the NIEHS Sister Study cohort using predictive $k$-means, we find that the association between systolic blood pressure (SBP) and long-term fine particulate matter (PM$_{2.5}$) exposure varies significantly between different clusters of PM$_{2.5}$ component profiles. Our cluster-based analysis shows that, for subjects assigned to a cluster located in the Midwestern U.S., a 10 $\mu$g/m$^{3}$ difference in exposure is associated with 4.37 mmHg (95% CI, 2.38, 6.35) higher SBP.