Papadogeorgou Georgia, Dominici Francesca
Department of Statistical Science, Duke University, Durham NC 27710.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston MA 02115.
Ann Appl Stat. 2020 Jun;14(2):850-871. doi: 10.1214/20-aoas1330. Epub 2020 Jun 29.
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 semi-parametric 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 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 PM, 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 increasing indicating that higher ambient concentrations lead to higher cardiovascular hospitalization rates, and ambient PM 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 PM on cardiovascular hospitalizations.
在过去二十年中，空气污染的环境水平大幅下降。与此同时，《清洁空气法》规定必须根据最新科学对国家环境空气质量标准（NAAQS）进行定期评估，以保护民众。因此，研究人员应继续探讨以下问题：暴露于低于NAAQS的空气污染水平是否对人类健康有害？此外，围绕环境法规的争议性质促使我们在因果推断框架内提出这个问题。几种参数和半参数回归方法已被用于估计长期暴露于环境空气污染浓度与健康结果之间的暴露-反应（ER）曲线。然而，现有的大多数方法并非在正式的因果推断框架内制定，没有针对所有暴露水平调整同一组潜在混杂因素，也没有考虑协变量选择和ER形状方面的模型不确定性。在本文中，我们引入了一种用于估计因果ER曲线的贝叶斯框架，称为LERCA（局部暴露反应混杂调整），它：a）允许在不同暴露水平存在不同的混杂因素和不同强度的混杂；b）传播关于混杂因素选择和ER形状的模型不确定性。重要的是，LERCA提供了一种有原则的方法来评估在不同暴露水平下观察到的协变量的混杂重要性，为研究人员提供了关于回归模型中要测量和调整的变量集的重要信息。通过模拟研究，我们表明在存在局部混杂的情况下，现有方法在估计ER曲线方面表现不佳。LERCA用于评估长期暴露于环境细颗粒物（一种关键的受监管污染物）与美国大陆5362个邮政编码区域且位于污染监测站点附近的心血管住院之间的关系，同时针对整个暴露范围内潜在变化的一组混杂因素进行调整。我们的数据集包括2011 - 2013年丰富的健康、天气、人口统计和污染信息。估计的暴露-反应曲线呈上升趋势，表明更高的环境浓度会导致更高的心血管住院率，并且当关注低暴露范围时，环境细颗粒物估计会导致心血管住院率增加。我们的结果表明，细颗粒物对心血管住院的影响不存在阈值。
