Cork Michael, Mork Daniel, Dominici Francesca
J R Stat Soc Ser A Stat Soc. 2025 Jan 16. doi: 10.1093/jrsssa/qnaf004.
Exposure to fine particulate matter (PM) poses significant health risks and accurately determining the shape of the relationship between PM 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 PM 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 PM and all-cause mortality in the Medicare population, with a sharp increase in relative mortality at low PM concentrations. Our findings suggest that stricter limits on PM 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.
接触细颗粒物（PM）会带来重大健康风险，准确确定PM与健康结果之间关系的形式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），但很少有研究在合理的数据生成情景下比较它们的性能。本研究通过模拟比较了七种常用的ERC估计方法在72种暴露-反应和混杂情景下的表现。此外，我们应用这些方法，利用来自美国超过6800万医疗保险受益人的数据，估计长期PM暴露与全因死亡率之间的ERC。我们的模拟表明，当预期存在异质暴露效应时，未置于因果推断框架内的回归方法并不适用。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的因果推断方法。在我们的数据应用中，我们观察到医疗保险人群中年平均PM与全因死亡率之间存在非线性关系，在低PM浓度下相对死亡率急剧上升。我们的研究结果表明，对PM实施更严格的限制可以避免大量过早死亡。为便于使用我们的结果，我们在Github上为分析的每一步提供了公开可用、可重现的代码。
