Environmental Exposure Mixtures: Questions and Methods to Address Them

Abstract

Purpose of this review This review provides a summary of statistical approaches that researchers can use to study environmental exposure mixtures. Two primary considerations are the form of the research question and the statistical tools best suited to address that question. Because the choice of statistical tools is not rigid, we make recommendations about when each tool may be most useful. Recent Findings When dimensionality is relatively low, some statistical tools yield easily interpretable estimates of effect (e.g., risk ratio, odds ratio) or intervention impacts. When dimensionality increases, it is often necessary to compromise this interpretablity in favor of identifying interesting statistical signals from noise; this requires applying statistical tools that are oriented more heavily towards dimension reduction via shrinkage and/or variable selection. Summary The study of complex exposure mixtures has prompted development of novel statistical methods. We suggest that further validation work would aid practicing researchers in choosing among existing and emerging statistical tools for studying exposure mixtures.

A Quantile-Based g-Computation Approach to Addressing the Effects of Exposure Mixtures

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Assessment of Multipollutant Exposures During Pregnancy Using Silicone Wristbands

“…Additionally, some chemicals may cross the placental barrier and directly expose the fetus to potentially toxic chemicals (8)(9)(10). Of particular concern are adverse impacts of exposures to multipollutant mixtures, as chemical exposures may act jointly, modify the effects of one another, and/or confound relations of one another (11)(12)(13)(14). However, the assessment of multipollutant exposures among pregnant women is challenging because it is especially important to minimize risks and burdens of exposure assessments among this sensitive population.…”

A family of partial-linear single-index models for analyzing complex environmental exposures with continuous, categorical, time-to-event, and longitudinal health outcomes

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