Bayesian Kernel Machine Regression Distributed Lag Models

Abstract

BACKGROUND AND AIM: Exposures to environmental chemicals during gestation can alter health status later in life. Most studies of maternal exposure to chemicals during pregnancy focus on a single chemical exposure observed at high temporal resolution. Recent research has turned to focus on exposure to mixtures of multiple chemicals, generally observed at a single time point. We consider statistical methods for analyzing data on chemical mixtures that are observed at high temporal resolution. METHODS: We propose a novel method, Bayesian kernel machine regression distributed lag model (BKMR-DLM), that simultaneously accounts for nonlinear associations and interactions among time-varying measures of exposure to mixtures. BKMR-DLM uses a functional weight for each exposure that parameterizes the window of susceptibility corresponding to that exposure within a kernel machine framework that captures non-linear and interaction effects of the multivariate exposure on the outcome. RESULTS:In a simulation, we show that the proposed method can better estimate the exposure-response function and, in high signal settings, can identify critical windows in time during which exposure has an increased association with the outcome. Applying the proposed methods to data from a birth cohort, we found evidence of a negative association between OC and birth weight and that nitrate modifies the OC, EC, and sulfate exposure-response functions. CONCLUSIONS:BKMR-DLM can estimate nonlinear association and higher-order interactions between repeated measures of exposure to a chemical mixture. It can be used to identify critical windows to components of a mixture. KEYWORDS: Air pollution, Birth outcomes, Mixtures, Modeling, Pregnancy outcomes