Novel Approach for Hierarchical Family Selection of an Ambient Air Pollutant Mixture With Application to Childhood Asthma

Abstract

Long-term exposure to ambient air pollution has previously been associated with childhood asthma, but endeavors have focused on single and pairwise pollutant models. We introduce a novel framework for selection of effect drivers from an environmental mixture, which is based on an entropy rank agreement measure. We apply the method in a nationwide study, relating prenatal exposure to ambient air pollution to asthma incidence in Danish children aged 0–19 years that are born from 1998 to 2016. Also, we estimate effects through population-wide G-estimation contrasts. We conclude that being exposed to the observed levels of ambient air pollution in contrast to the hypothetical case of the minimum of the observed subject-specific exposure levels and the 2.5% quantile levels is associated with relative risk increases that exceed 30% and absolute risk differences that exceed 2 percentage points across Danish municipalities. For selection we discover that SO${\hspace{0.17em}}_4^{2-}$ and primary organic aerosols appear the most important predictors of asthma amongst the included ambient air pollutants and that these are both associated with a risk increase. The developed methodology is a promising approach to handling an environmental mixture of exposures in statistical analyses, which allows for discovery of important drivers of associations.