Abstract

We develop a causal inference approach to estimate the number of adverse health events that were prevented due to changes in exposure to multiple pollutants attributable to a large-scale air quality intervention/regulation, with a focus on the 1990 Clean Air Act Amendments (CAAA). We introduce a causal estimand called the Total Events Avoided (TEA) by the regulation, defined as the difference in the number of health events expected under the no-regulation pollution exposures and the number observed with-regulation. We propose matching and machine learning methods that leverage population-level pollution and health data to estimate the TEA. Our approach improves upon traditional methods for regulation health impact analyses by formalizing causal identifying assumptions, utilizing population-level data, minimizing parametric assumptions, and collectively analyzing multiple pollutants. To reduce model-dependence, our approach estimates cumulative health impacts in the subset of regions with projected no-regulation features lying within the support of the observed with-regulation data, thereby providing a conservative but data-driven assessment to complement traditional parametric approaches. We analyze the health impacts of the CAAA in the US Medicare population in the year 2000, and our estimates suggest that large numbers of cardiovascular and dementia-related hospitalizations were avoided due to CAAA-attributable changes in pollution exposure.

摘要

我们开发了一种因果推断方法来估计由于大规模空气质量干预/监管导致的多种污染物暴露变化而被预防的不良健康事件的数量，重点是 1990 年清洁空气法案修正案 (CAAA) . 我们引入了一个因果估计，称为法规避免的总事件 (TEA)，定义为在无法规污染暴露下预期的健康事件数量与有法规观察到的数量之间的差异。我们提出了利用人口水平的污染和健康数据来估计 TEA 的匹配和机器学习方法。我们的方法通过正式化因果识别假设、利用人口水平数据、最小化参数假设，改进了监管健康影响分析的传统方法，并集中分析多种污染物。为了减少模型依赖性，我们的方法估计了在观察到的有监管数据的支持下具有预计无监管特征的区域子集中的累积健康影响，从而提供了一个保守但数据驱动的评估来补充传统的参数方法。我们分析了 2000 年 CAAA 对美国医疗保险人群的健康影响，我们的估计表明，由于 CAAA 可归因的污染暴露变化，避免了大量与心血管和痴呆相关的住院治疗。从而提供保守但数据驱动的评估，以补充传统的参数方法。我们分析了 2000 年 CAAA 对美国医疗保险人群的健康影响，我们的估计表明，由于 CAAA 可归因的污染暴露变化，避免了大量与心血管和痴呆相关的住院治疗。从而提供保守但数据驱动的评估，以补充传统的参数方法。我们分析了 2000 年 CAAA 对美国医疗保险人群的健康影响，我们的估计表明，由于 CAAA 可归因的污染暴露变化，避免了大量与心血管和痴呆相关的住院治疗。