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September 2022 Critical window variable selection for mixtures: Estimating the impact of multiple air pollutants on stillbirth
Joshua L. Warren, Howard H. Chang, Lauren K. Warren, Matthew J. Strickland, Lyndsey A. Darrow, James A. Mulholland
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Ann. Appl. Stat. 16(3): 1633-1652 (September 2022). DOI: 10.1214/21-AOAS1560


Understanding the role of time-varying pollution mixtures on human health is critical as people are simultaneously exposed to multiple pollutants during their lives. For vulnerable subpopulations who have well-defined exposure periods (e.g., pregnant women), questions regarding critical windows of exposure to these mixtures are important for mitigating harm. We extend critical window variable selection (CWVS) to the multipollutant setting by introducing CWVS for mixtures (CWVSmix), a hierarchical Bayesian method that combines smoothed variable selection and temporally correlated weight parameters to: (i) identify critical windows of exposure to mixtures of time-varying pollutants, (ii) estimate the time-varying relative importance of each individual pollutant and their first order interactions within the mixture, and (iii) quantify the impact of the mixtures on health. Through simulation we show that CWVSmix offers the best balance of performance in each of these categories in comparison to competing methods. Using these approaches, we investigate the impact of exposure to multiple ambient air pollutants on the risk of stillbirth in New Jersey, 2005–2014. We find consistent elevated risk in gestational weeks 2, 16–17, and 20 for non-Hispanic Black mothers, with pollution mixtures dominated by ammonium (weeks 2, 17, 20), nitrate (weeks 2, 17), nitrogen oxides (weeks 2, 16), PM2.5 (week 2), and sulfate (week 20). The method is available in the R package CWVSmix.


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Joshua L. Warren. Howard H. Chang. Lauren K. Warren. Matthew J. Strickland. Lyndsey A. Darrow. James A. Mulholland. "Critical window variable selection for mixtures: Estimating the impact of multiple air pollutants on stillbirth." Ann. Appl. Stat. 16 (3) 1633 - 1652, September 2022.


Received: 1 April 2021; Revised: 1 October 2021; Published: September 2022
First available in Project Euclid: 19 July 2022

Digital Object Identifier: 10.1214/21-AOAS1560

Keywords: Bayesian variable selection , correlated weights , multivariate latent variables , pollution mixtures , reproductive health

Rights: Copyright © 2022 Institute of Mathematical Statistics


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Vol.16 • No. 3 • September 2022
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