Exposure to fine particulate matter ($PM_{2.5}$) poses significant health risks and accurately determining the shape of the relationship between $PM_{2.5}$ and health outcomes has crucial policy ramifications. While 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 $PM_{2.5}$ 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 $PM_{2.5}$ 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 $PM_{2.5}$ limits 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.

翻译：细颗粒物（$PM_{2.5}$）暴露对健康构成显著风险，精确确定$PM_{2.5}$与健康结局之间的关系形态具有关键政策意义。尽管已有多种统计方法可用于估算这种暴露-反应曲线（ERC），但很少有研究在合理的数据生成情景下比较其性能。本研究通过模拟72种暴露-反应与混杂情景，比较了七种常用ERC估计量。此外，我们利用美国超过6800万Medicare受益人的数据，应用这些方法估算长期$PM_{2.5}$暴露与全因死亡率之间的ERC。模拟结果表明，在预期存在异质性暴露效应时，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ERC函数形式未知的情况下，我们建议采用允许非线性ERC的因果推断方法。在数据应用中，我们观察到Medicare人群中年度平均$PM_{2.5}$与全因死亡率之间存在非线性关系，低$PM_{2.5}$浓度下相对死亡率急剧上升。我们的发现表明，更严格的$PM_{2.5}$限值可避免大量过早死亡。为便于结果应用，我们在Github上公开提供了分析每一步的可复现代码。