Critical Window Variable Selection for Mixtures: Estimating the Impact of Multiple Air Pollutants on Stillbirth
Year: 2021
Type: conference-abstract
Abstract: BACKGROUND AND AIM: 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 sub-populations who have well-defined exposure periods (e.g., pregnant women), questions regarding critical windows of exposure to these mixtures are important for mitigating harm. METHODS: 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. RESULTS:Through simulation study, we show that CWVSmix outperforms competing methods in each of these categories. We use these approaches to investigate the impact of exposure to multiple gas and particulate 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). CONCLUSIONS:CWVSmix is a useful tool for quantifying the impact of time-varying mixtures on health when there is interest in identifying critical windows of exposure. The method is available in the R package CWVSmix (https://github.com/warrenjl/CWVSmix). KEYWORDS: Bayesian variable selection, Correlated weights, Pollution mixtures, Reproductive health.
Source: ISEE Conference Abstracts
Language: English
Subfield: Health, Toxicology and Mutagenesis
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W3195883515