Non-PM2.5 risk factor effects on supralinearity.

A growing number of studies have produced results that suggest the shape of the concentration-response (C-R) relationship between PM2.5 exposure and mortality is “supralinear” such that incremental risk is higher at the lowest exposure levels than at the highest exposure levels. If the C-R function is in fact supralinear, then there may be significant health benefits associated with reductions in PM2.5 below the current US National Ambient Air Quality Standards (NAAQS), as each incremental tightening of the PM2.5 NAAQS would be expected to produce ever-greater reductions in mortality risk. In this paper we undertake a series of tests with simulated cohort data to examine whether there are alternative explanations for apparent supralinearity in PM2.5 C-R functions. Our results show that a linear C-R function for PM2.5 can falsely appear to be supralinear in a statistical estimation process for a variety of reasons, such as spatial variation in the composition of total PM2.5 mass, the presence of confounders that are correlated with PM2.5 exposure, and some types of measurement error in estimates of PM2.5 exposure. To the best of our knowledge, this is the first simulation-based study to examine alternative explanations for apparent supralinearity in C-R functions.越来越多的研究得出结论认为，细颗粒物（PM2.5）暴露与死亡率之间的浓度-反应（C-R）关系呈“超线性”特征，即在最低暴露水平下的增量风险高于最高暴露水平下的增量风险。若该C-R函数确实呈超线性特征，则将PM2.5浓度降至当前美国国家环境空气质量标准（NAAQS）以下或可带来显著健康收益，因为每一次收紧PM2.5的NAAQS标准，预计都能进一步大幅降低死亡风险。 本文基于模拟队列数据开展了一系列检验，旨在探究PM2.5的C-R函数呈现表观超线性特征的其他可能解释。研究结果表明，受多种因素影响，在统计估计过程中，细颗粒物的线性C-R函数可能会被错误地识别为超线性特征，这些因素包括总PM2.5质量组分的空间异质性、与PM2.5暴露相关的混杂因素的存在，以及PM2.5暴露估计值存在的部分类型测量误差。据我们所知，本研究是首个基于模拟实验的研究，旨在探究C-R函数呈现表观超线性特征的其他可能解释。