A CAUSAL EXPOSURE RESPONSE FUNCTION WITH LOCAL ADJUSTMENT FOR CONFOUNDING: ESTIMATING HEALTH EFFECTS OF EXPOSURE TO LOW LEVELS OF AMBIENT FINE PARTICULATE MATTER

• 成果类型: Article
• 署名作者: Papadogeorgou, Georgia; Dominici, Francesca
• 署名单位: Duke University; Harvard University; Harvard T.H. Chan School of Public Health
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/20-AOAS1330
• 发表日期: 2020
• 页码: 850-871
• 关键词: air-pollution exposure; canadian census health; term pm2.5 exposure; mortality; association; selection; curve; RISK; population; cohort

摘要

In the last two decades ambient levels of air pollution have declined substantially. At the same time 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 the following question: is exposure to levels of air pollution below the NAAQS harmful to human health? Furthermore, the contentious nature surrounding environmental regulations urges us to cast this question within a causal inference framework. Several parametric and semiparametric regression approaches have been used to estimate the exposure-response (ER) curve between long-term exposure to ambient air pollution concentrations and health outcomes. However, most of the existing approaches are not formulated within a formal framework for causal inference, adjust for the same set of potential confounders across all levels of exposure and do not account for model uncertainty regarding covariate selection and the shape of the ER. In this paper we introduce a Bayesian framework for the estimation of a causal ER curve called LERCA (Local Exposure Response Confounding Adjustment), which: (a) allows for different confounders and different strength of confounding at the different exposure levels, and (b) propagates model uncertainty regarding confounders' selection and the shape of the ER. Importantly, LERCA provides a principled way of assessing the observed covariates' confounding importance at different exposure levels, providing researchers with important information regarding the set of variables to measure and adjust for in regression models. Using simulation studies, 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 long-term exposure to ambient PM2.5, a key regulated pollutant, and cardiovascular hospitalizations for 5,362 zip codes in the continental U.S. and located near a pollution monitoring site, while adjusting for a potentially varying set of confounders across the exposure range. Our data set includes rich health, weather, demographic and pollution information for the years of 2011-2013. The estimated exposure-response curve is increasingly indicating that higher ambient concentrations lead to higher cardiovascular hospitalization rates, and ambient PM2.5 was estimated to lead 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.