Evaluation of the health impacts of the 1990 Clean Air Act Amendments using causal inference and machine learning.
Evaluation of the health impacts of the 1990 Clean Air Act Amendments using causal inference and machine learning.
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DOI：
10.1080/01621459.2020.1803883
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发表时间：
2020-09-16
影响因子：
3.7
通讯作者：
Dominici F
中科院分区：
文献类型：
--
作者：
Nethery RC;Mealli F;Sacks JD;Dominici F
我们开发了一种因果推理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预防的不良健康事件数量，重点关注1990年清洁空气法修正案（CAAA）。我们引入了一个因果被估量称为总事件避免（TEA）的规定，定义为在无管制污染暴露下预期的健康事件的数量和与监管观察到的数量的差异。我们提出了匹配和机器学习方法，利用人口水平的污染和健康数据来估计TEA。我们的方法改进了传统的监管健康影响分析方法，通过形式化因果识别假设，利用人口水平的数据，最大限度地减少参数假设，并共同分析多种污染物。为了减少模型依赖性，我们的方法估计累积的健康影响的子集的区域与预测的无监管功能躺在所观察到的监管数据的支持，从而提供了一个保守的，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引起的污染暴露变化，避免了大量心血管和痴呆相关的住院治疗。
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.
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影响因子：
1.8
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通讯作者：
Su, Yu-Sung
影响因子：
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作者：
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通讯作者：
RUBIN, DB
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作者：
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通讯作者：
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