Critical Window Variable Selection for Mixtures: Estimating the Impact of Multiple Air Pollutants on Stillbirth

TL;DR

This paper introduces CWVSmix, a hierarchical Bayesian method for identifying critical exposure windows and assessing the health impact of time-varying pollutant mixtures, demonstrated on stillbirth risk analysis.

Contribution

We extend Critical Window Variable Selection to handle multiple pollutants, enabling identification of critical exposure periods and pollutant importance within mixtures.

Findings

CWVSmix outperforms competing methods in simulations.

Identified critical windows for air pollution impact on stillbirth.

Pollutant mixtures like ammonium and nitrate are linked to increased risk.

Abstract

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. 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.