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

ABSTRACT: 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.

SUBMITTER: Narisetty NN

PROVIDER: S-EPMC7134269 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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

Narisetty Naveen N NN Mukherjee Bhramar B Chen Yin-Hsiu YH Gonzalez Richard R Meeker John D JD

Statistics in medicine 20181226 9

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 comple ...[more]

PMID: 30586682

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Project description:BackgroundUnderstanding the health effects of exposure to chemical mixtures is critically important given the broad range of concurrent exposures throughout the life-course. While investigations of environmental chemicals and components of the human microbiome are becoming more common, few have examined associations with chemical mixtures. This study assesses the association between exposure to mixtures of 66 different environmental chemicals and nasal colonization of Staphylococcus aureus (SA) and methicillin resistant SA (MRSA).MethodsData came from the National Health and Nutrition Examination Survey (NHANES) 2001-2004. The analytical sample consists of 10,312 participants, age 6 years and older, subdivided into 8 groups with different chemical exposure mixtures. Within each of 6 chemical classes (metals, phthalates, polycyclic aromatic hydrocarbons (PAHs), polybrominated diphenyl ethers (PBDEs), polyfluorochemicals (PFCs), and phenols), weighted quantile sum (WQS) regression was used to analyze the joint association of the component compounds and nasal SA colonization. WQS was also used to assess the joint association of 3 chemical mixtures (metals, metal and PAHs, and metal and triclosan) and nasal MRSA colonization. All regression models were adjusted for confounders.ResultsThe analytical sample was between ages 6-85, slightly more female, and predominantly non-smokers. Prevalence of SA carriage was 29.2%, and MRSA colonization prevalence was 1.2%. Within each chemical class, odds of SA colonization increased statistically significantly with exposure to mixtures of metals (OR = 1.11, 95% CI = 1.02-1.20), phthalates (OR = 1.09, 95% CI = 1.04-1.14), and phenols (OR = 1.08, 95% CI = 1.01-1.15). Exposure to a mixture of metals combined with PAHs was also associated with increased odds of MRSA carriage (OR = 1.38, 95% CI = 1.02-1.86).ConclusionResults indicate an association between multiple environmental chemical mixtures and SA colonization, including MRSA. These findings support the need for further analysis of associations between chemical mixtures and SA colonization, as well as other components of the human microbiome.

Dataset Information

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

Publications

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

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