Methods for Estimating the Exposure-Response Curve to Inform the New Safety Standards for Fine Particulate Matter.

IF 1.6 3区 数学 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS
Michael Cork, Daniel Mork, Francesca Dominici
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引用次数: 0

Abstract

Exposure to fine particulate matter (PM2.5) poses significant health risks and accurately determining the shape of the relationship between PM2.5 and health outcomes has crucial policy implications. Although various statistical methods exist to estimate this exposure-response curve (ERC), few studies have compared their performance under plausible data-generating scenarios. This study compares seven commonly used ERC estimators across 72 exposure-response and confounding scenarios via simulation. Additionally, we apply these methods to estimate the ERC between long-term PM2.5 exposure and all-cause mortality using data from over 68 million Medicare beneficiaries in the United States. Our simulation indicates that regression methods not placed within a causal inference framework are unsuitable when anticipating heterogeneous exposure effects. Under the setting of a large sample size and unknown ERC functional form, we recommend utilizing causal inference methods that allow for nonlinear ERCs. In our data application, we observe a nonlinear relationship between annual average PM2.5 and all-cause mortality in the Medicare population, with a sharp increase in relative mortality at low PM2.5 concentrations. Our findings suggest that stricter limits on PM2.5 could avert numerous premature deaths. To facilitate the utilization of our results, we provide publicly available, reproducible code on Github for every step of the analysis.

暴露-反应曲线估算方法为新细颗粒物安全标准提供依据。
暴露于细颗粒物(PM2.5)会带来重大的健康风险,准确确定PM2.5与健康结果之间的关系具有至关重要的政策意义。尽管存在各种统计方法来估计这种暴露-反应曲线(ERC),但很少有研究比较它们在合理的数据生成场景下的性能。本研究通过模拟比较了72种暴露-反应和混杂情景中7种常用的ERC估计器。此外,我们利用来自美国6800多万医疗保险受益人的数据,应用这些方法来估计长期PM2.5暴露与全因死亡率之间的ERC。我们的模拟表明,在预测异质暴露效应时,未置于因果推理框架内的回归方法是不合适的。在大样本量和未知ERC函数形式的情况下,我们建议使用允许非线性ERC的因果推理方法。在我们的数据应用中,我们观察到医疗保险人群的年平均PM2.5与全因死亡率之间存在非线性关系,低PM2.5浓度下的相对死亡率急剧上升。我们的研究结果表明,更严格的PM2.5限制可以避免许多过早死亡。为了方便使用我们的结果,我们在Github上为分析的每个步骤提供了公开可用的、可复制的代码。
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来源期刊
CiteScore
2.90
自引率
5.00%
发文量
136
审稿时长
>12 weeks
期刊介绍: Series A (Statistics in Society) publishes high quality papers that demonstrate how statistical thinking, design and analyses play a vital role in all walks of life and benefit society in general. There is no restriction on subject-matter: any interesting, topical and revelatory applications of statistics are welcome. For example, important applications of statistical and related data science methodology in medicine, business and commerce, industry, economics and finance, education and teaching, physical and biomedical sciences, the environment, the law, government and politics, demography, psychology, sociology and sport all fall within the journal''s remit. The journal is therefore aimed at a wide statistical audience and at professional statisticians in particular. Its emphasis is on well-written and clearly reasoned quantitative approaches to problems in the real world rather than the exposition of technical detail. Thus, although the methodological basis of papers must be sound and adequately explained, methodology per se should not be the main focus of a Series A paper. Of particular interest are papers on topical or contentious statistical issues, papers which give reviews or exposés of current statistical concerns and papers which demonstrate how appropriate statistical thinking has contributed to our understanding of important substantive questions. Historical, professional and biographical contributions are also welcome, as are discussions of methods of data collection and of ethical issues, provided that all such papers have substantial statistical relevance.
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