A causal exposure response function with local adjustment for confounding: Estimating health effects of exposure to low levels of ambient fine particulate matter

TL;DR

This paper introduces LERCA, a Bayesian causal inference method that estimates exposure-response curves for air pollution health effects, accounting for local confounding and model uncertainty, revealing health risks at low pollution levels.

Contribution

The paper presents a novel Bayesian framework, LERCA, for estimating causal exposure-response curves with local confounder adjustment, improving accuracy over existing methods.

Findings

Ambient PM2.5 increases cardiovascular hospitalizations at low exposure levels.

No safe threshold for PM2.5 effects on cardiovascular health was identified.

LERCA outperforms traditional methods in local confounding scenarios.

Abstract

The Clean Air Act mandates that the National Ambient Air Quality Standards (NAAQS) must be routinely assessed to protect populations based on the latest science. Therefore, researchers should continue to address whether exposure to levels of air pollution below the NAAQS is harmful to human health. The contentious nature surrounding environmental regulations urges us to cast this question within a causal inference framework. Parametric and semi-parametric regression approaches have been used to estimate the exposure-response (ER) curve between ambient air pollution and health outcomes. Most of these approaches are not formulated within a causal framework, adjust for the same covariates across all levels of exposure, and do not account for model uncertainty. We introduce a Bayesian framework for the estimation of a causal ER curve called LERCA (Local Exposure Response Confounding Adjustment), which allows for different confounders and different strength of confounding at the different exposure levels; and propagates uncertainty regarding confounders' selection and the shape of the ER. LERCA provides a principled way of assessing the covariates' confounding importance at different exposure levels, providing researchers with information regarding the variables to adjust for in regression models. Using simulations, we show that state of the art approaches perform poorly in estimating the ER curve in the presence of local confounding. LERCA is used to evaluate the relationship between exposure to ambient PM2.5 and cardiovascular hospitalizations for 5,362 zip codes in the US, while adjusting for a potentially varying set of confounders across the exposure range. Ambient PM2.5 leads to an increase in cardiovascular hospitalization rates when focusing at the low exposure range. Our results indicate that there is no threshold for the effect of PM2.5 on cardiovascular hospitalizations.