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Exposures to environmental chemicals during gestation can alter health statuslater in life. Most studies of maternal exposure to chemicals during pregnancyhave focused on a single chemical exposure observed at high temporalresolution. Recent research has turned to focus on exposure to mixtures ofmultiple chemicals, generally observed at a single time point. We considerstatistical methods for analyzing data on chemical mixtures that are observedat a high temporal resolution. As motivation, we analyze the associationbetween exposure to four ambient air pollutants observed weekly throughoutgestation and birth weight in a Boston-area prospective birth cohort. Toexplore patterns in the data, we first apply methods for analyzing data on (1)a single chemical observed at high temporal resolution, and (2) a mixturemeasured at a single point in time. We highlight the shortcomings of theseapproaches for temporally-resolved data on exposure to chemical mixtures.Second, we propose a novel method, a Bayesian kernel machine regressiondistributed lag model (BKMR-DLM), that simultaneously accounts for nonlinearassociations and interactions among time-varying measures of exposure tomixtures. BKMR-DLM uses a functional weight for each exposure thatparameterizes the window of susceptibility corresponding to that exposurewithin a kernel machine framework that captures non-linear and interactioneffects of the multivariate exposure on the outcome. In a simulation study, weshow that the proposed method can better estimate the exposure-responsefunction and, in high signal settings, can identify critical windows in timeduring which exposure has an increased association with the outcome. Applyingthe proposed method to the Boston birth cohort data, we find evidence of anegative association between organic carbon and birth weight and that nitratemodifies the organic carbon, ...

Kernel Machine and Distributed Lag Models for Assessing Windows of Susceptibility to Environmental Mixtures in Children's Health Studies