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政策导致的空气污染健康差距:统计和数据科学的考虑。

Policy-induced air pollution health disparities: Statistical and data science considerations.

机构信息

Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA.

Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, USA.

出版信息

Science. 2024 Jul 26;385(6707):391-396. doi: 10.1126/science.adp1870. Epub 2024 Jul 25.

Abstract

Air pollution causes premature death and disease and disproportionately harms non-white and lower-income groups in the United States. Government policies are responsible for the racial disparity in air pollution exposure and related health outcomes. Investigating complex relationships between policies, air pollution, and health requires (i) harmonized data connecting policies, environmental exposures, socioeconomic characteristics, and health at the individual and area level; (ii) interpretable estimands accounting for the complex interplay between policies and disparities in exposures and health outcomes; and (iii) data science approaches that can elucidate direct and indirect policy effects on disparities to identify effective interventions. We review statistical considerations and new data science approaches needed to scrutinize the policy impacts on disparities in air pollution exposure and health outcomes.

摘要

空气污染导致过早死亡和疾病,而且不成比例地对美国的非白人和低收入群体造成伤害。政府政策是导致空气污染暴露和相关健康结果存在种族差异的原因。调查政策、空气污染和健康之间的复杂关系需要:(i) 协调的数据将政策、环境暴露、社会经济特征和个人及地区层面的健康联系起来;(ii) 可解释的估计量,以说明政策与暴露和健康结果差异之间的复杂相互作用;以及 (iii) 数据分析方法,可以阐明政策对差异的直接和间接影响,以确定有效的干预措施。我们审查了审查政策对空气污染暴露和健康结果差异的影响所需的统计考虑因素和新的数据分析方法。

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