A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth

TL;DR

This paper introduces a Bayesian kernel density estimation method to better incorporate exposure uncertainty in health outcome analyses, demonstrated through air pollution and stillbirth data, improving inference accuracy.

Contribution

It develops a flexible Bayesian KDE approach that fully utilizes posterior predictions to improve exposure-health association estimates, addressing limitations of existing methods.

Findings

Increased risk of stillbirth associated with higher air pollution levels three days prior to delivery.

The KDE method outperforms existing approaches in simulation studies.

Application to real data reveals significant exposure effects on health outcomes.

Abstract

Studies of the relationships between environmental exposures and adverse health outcomes often rely on a two-stage statistical modeling approach, where exposure is modeled/predicted in the first stage and used as input to a separately fit health outcome analysis in the second stage. Uncertainty in these predictions is frequently ignored, or accounted for in an overly simplistic manner, when estimating the associations of interest. Working in the Bayesian setting, we propose a flexible kernel density estimation (KDE) approach for fully utilizing posterior output from the first stage modeling/prediction to make accurate inference on the association between exposure and health in the second stage, derive the full conditional distributions needed for efficient model fitting, detail its connections with existing approaches, and compare its performance through simulation. Our KDE approach is shown to generally have improved performance across several settings and model comparison metrics. Using competing approaches, we investigate the association between lagged daily ambient fine particulate matter levels and stillbirth counts in New Jersey (2011-2015), observing an increase in risk with elevated exposure three days prior to delivery. The newly developed methods are available in the R package KDExp.