Statistical Approaches for Assessing Health Effects of Environmental Chemical Mixtures in Epidemiology: Lessons from an Innovative Workshop

Taylor, Kyla W.; Joubert, Bonnie R.; Braun, Joe M.; Dilworth, Caroline; Gennings, Chris; Hauser, Russ; Heindel, Jerry; Rider, Cynthia V.; Webster, Thomas F.; Carlin, Danielle J.

doi:10.1289/ehp547

Cited by 198 publications

(154 citation statements)

References 11 publications

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“…200,201 Several methods showed promise in addressing questions related to EDC mixtures. For instance, Least Absolute Shrinkage and Selection Operator and elastic net methods can identify individual EDCs associated with health outcomes and their interactions while controlling for co-pollutant confounding.…”

Section: Challenges To Making Stronger Inferences About Edcs and Chilmentioning

confidence: 99%

Early-life exposure to EDCs: role in childhood obesity and neurodevelopment

2016

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Endocrine disrupting chemicals (EDCs) may increase the risk of childhood diseases by disrupting hormonally mediated processes critical for growth and development during gestation, infancy, or childhood. The fetus, infant, and child may have enhanced sensitivity to environmental stressors like EDCs due to rapid development and greater exposure to some EDCs that results from their developmentally appropriate behavior, anatomy, and physiology. This review summarizes epidemiological studies examining the relations of early-life exposure to bisphenol A (BPA), phthalates, triclosan, and perfluoroalkyl substance (PFAS) with childhood neurobehavioral disorders and obesity. The available epidemiological evidence suggests that prenatal exposure to several of these ubiquitous EDCs is associated with adverse neurobehavior (BPA and phthalates) and excess adiposity or increased risk of obesity/overweight (PFAS). Quantifying the effects of EDC mixtures, improving EDC exposure assessment, reducing bias from confounding, identifying periods of heightened vulnerability, and elucidating the presence and nature of sexually dimorphic EDC effects would result in stronger inferences from epidemiological studies. Ultimately, better estimates of the causal effects of EDC exposures on child health could help identify susceptible sub-populations and lead to public health interventions to reduce these exposures.

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Section: Challenges To Making Stronger Inferences About Edcs and Chilmentioning

confidence: 99%

Early-life exposure to EDCs: role in childhood obesity and neurodevelopment

2016

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“…It has been of great interest to develop statistical analysis methods, such as Ridge regression (Hoerl & Kennard, 1988) and the least absolute shrinkage and selection operator (Lasso) technique (Tibshirani, 1996) which can reduce the variability of the estimates by shrinking the coefficients (Oyeyemi et al, 2015), to evaluate the effect of mixture of environmental exposures on different health outcomes (Carlin, Rider, Woychik, & Birnbaum, 2013;Taylor et al, 2014). However, these methods have some limitations.…”

Section: Introductionmentioning

confidence: 99%

A generalized weighted quantile sum approach for analyzing correlated data in the presence of interactions

et al. 2019

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A weighted quantile sum (WQS) regression has been used to assess the associations between environmental exposures and health outcomes. However, the currently available WQS approach, which is based on additive effects, does not allow exploring for potential interactions of exposures with other covariates in relation to a health outcome. In addition, the current WQS cannot account for clustering, thus it may not be valid for analysis of clustered data. We propose a generalized WQS approach that can assess interactions by estimating stratum‐specific weights of exposures in a mixture, while accounting for potential clustering effect of matched pairs of cases and controls as well as censored exposure data due to being below the limits of detection. The performance of the proposed method in identifying interactions is evaluated through simulations based on various scenarios of correlation structures among the exposures and with an outcome. We also assess how well the proposed method performs in the presence of the varying levels of censoring in exposures. Our findings from the simulation study show that the proposed method outperforms the traditional WQS, as indicated by higher power of detecting interactions. We also find no strong evidence that the proposed method falsely identifies interactions when there are no true interactive effects. We demonstrate application of the proposed method to real data from the Epidemiological Research on Autism Spectrum Disorder (ASD) in Jamaica (ERAJ) by examining interactions between exposure to manganese and glutathione S‐transferase family gene, GSTP1 in relation to ASD.

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“…23,24 Nonlinearity n the response surface is often expected in the modeling of exposures in the health effects evaluation and the sample dataset that was released as a beta-tester by National Institute of Environmental Health Sciences (NIEHS) describes a highly nonlinear dose-response function also demonstrates this. 12,25 Several authors 26,27 noted nonlinear effects of pollutant profiles on term low birth weight and other indicators of poverty. Nonlinear association between lead exposure and maternal stress among pregnant women has also been found.…”

Section: Introductionmentioning

confidence: 99%

Selection of nonlinear interactions by a forward stepwise algorithm: Application to identifying environmental chemical mixtures affecting health outcomes

Narisetty

Mukherjee

Chen

et al. 2018

Statistics in Medicine

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In this paper, we propose a stepwise forward selection algorithm for detecting the effects of a set of correlated exposures and their interactions on a health outcome of interest when the underlying relationship could potentially be nonlinear. Though the proposed method is very general, our application in this paper remains to be on analysis of multiple pollutants and their interactions. Simultaneous exposure to multiple environmental pollutants could affect human health in a multitude of complex ways. For understanding the health effects of multiple environmental exposures, it is often important to identify and estimate complex interactions among exposures. However, this issue becomes analytically challenging in the presence of potential nonlinearity in the outcome‐exposure response surface and a set of correlated exposures. Through simulation studies and analyses of test datasets that were simulated as a part of a data challenge in multipollutant modeling organized by the National Institute of Environmental Health Sciences (http://www.niehs.nih.gov/about/events/pastmtg/2015/statistical/), we illustrate the advantages of our proposed method in comparison with existing alternative approaches. A particular strength of our method is that it demonstrates very low false positives across empirical studies. Our method is also used to analyze a dataset that was released from the Health Outcomes and Measurement of the Environment Study as a benchmark beta‐tester dataset as a part of the same workshop.

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Statistical Approaches for Assessing Health Effects of Environmental Chemical Mixtures in Epidemiology: Lessons from an Innovative Workshop

Cited by 198 publications

References 11 publications

Early-life exposure to EDCs: role in childhood obesity and neurodevelopment

Early-life exposure to EDCs: role in childhood obesity and neurodevelopment

A generalized weighted quantile sum approach for analyzing correlated data in the presence of interactions

Selection of nonlinear interactions by a forward stepwise algorithm: Application to identifying environmental chemical mixtures affecting health outcomes

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