Open Access
March 2020 Estimating the health effects of environmental mixtures using Bayesian semiparametric regression and sparsity inducing priors
Joseph Antonelli, Maitreyi Mazumdar, David Bellinger, David Christiani, Robert Wright, Brent Coull
Ann. Appl. Stat. 14(1): 257-275 (March 2020). DOI: 10.1214/19-AOAS1307


Humans are routinely exposed to mixtures of chemical and other environmental factors, making the quantification of health effects associated with environmental mixtures a critical goal for establishing environmental policy sufficiently protective of human health. The quantification of the effects of exposure to an environmental mixture poses several statistical challenges. It is often the case that exposure to multiple pollutants interact with each other to affect an outcome. Further, the exposure-response relationship between an outcome and some exposures, such as some metals, can exhibit complex, nonlinear forms, since some exposures can be beneficial and detrimental at different ranges of exposure. To estimate the health effects of complex mixtures, we propose a flexible Bayesian approach that allows exposures to interact with each other and have nonlinear relationships with the outcome. We induce sparsity using multivariate spike and slab priors to determine which exposures are associated with the outcome and which exposures interact with each other. The proposed approach is interpretable, as we can use the posterior probabilities of inclusion into the model to identify pollutants that interact with each other. We utilize our approach to study the impact of exposure to metals on child neurodevelopment in Bangladesh and find a nonlinear, interactive relationship between arsenic and manganese.


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Joseph Antonelli. Maitreyi Mazumdar. David Bellinger. David Christiani. Robert Wright. Brent Coull. "Estimating the health effects of environmental mixtures using Bayesian semiparametric regression and sparsity inducing priors." Ann. Appl. Stat. 14 (1) 257 - 275, March 2020.


Received: 1 January 2019; Revised: 1 October 2019; Published: March 2020
First available in Project Euclid: 16 April 2020

zbMATH: 07200171
MathSciNet: MR4085093
Digital Object Identifier: 10.1214/19-AOAS1307

Keywords: Bayesian inference , Environmental statistics , interaction model , spike and slab priors

Rights: Copyright © 2020 Institute of Mathematical Statistics

Vol.14 • No. 1 • March 2020
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