评估波士顿细颗粒物与每日死亡之间的因果关联。

Estimating Causal Associations of Fine Particles With Daily Deaths in Boston.

作者信息

Schwartz Joel, Austin Elena, Bind Marie-Abele, Zanobetti Antonella, Koutrakis Petros

出版信息

Am J Epidemiol. 2015 Oct 1;182(7):644-50. doi: 10.1093/aje/kwv101. Epub 2015 Sep 6.

Abstract

Many studies have reported associations between daily particles less than 2.5 µm in aerodynamic diameter (PM2.5) and deaths, but they have been associational studies that did not use formal causal modeling approaches. On the basis of a potential outcome approach, we used 2 causal modeling methods with different assumptions and strengths to address whether there was a causal association between daily PM2.5 and deaths in Boston, Massachusetts (2004-2009). We used an instrumental variable approach, including back trajectories as instruments for variations in PM2.5 uncorrelated with other predictors of death. We also used propensity score as an alternative causal modeling analysis. The former protects against confounding by measured and unmeasured confounders and is based on the assumption of a valid instrument. The latter protects against confounding by all measured covariates, provides valid estimates in the case of effect modification, and is based on the assumption of no unmeasured confounders. We found a causal association of PM2.5 with mortality, with a 0.53% (95% confidence interval: 0.09, 0.97) and a 0.50% (95% confidence interval: 0.20, 0.80) increase in daily deaths using the instrumental variable and the propensity score, respectively. We failed to reject the null association with exposure after the deaths (P =0.93). Given these results, prior studies, and extensive toxicological support, the association between PM2.5 and deaths is almost certainly causal.

摘要

许多研究报告了空气动力学直径小于2.5微米的每日颗粒物(PM2.5)与死亡之间的关联,但这些都是未使用正式因果建模方法的关联性研究。基于潜在结果方法,我们使用了两种具有不同假设和优势的因果建模方法,以探讨马萨诸塞州波士顿市(2004 - 2009年)每日PM2.5与死亡之间是否存在因果关联。我们使用了一种工具变量方法,包括将后向轨迹作为PM2.5变化的工具,这些变化与其他死亡预测因素不相关。我们还使用倾向评分作为另一种因果建模分析。前者可防止测量和未测量的混杂因素造成的混杂,并且基于有效工具的假设。后者可防止所有测量的协变量造成的混杂,在存在效应修正的情况下提供有效估计,并且基于不存在未测量混杂因素的假设。我们发现PM2.5与死亡率之间存在因果关联,使用工具变量和倾向评分时,每日死亡分别增加0.53%(95%置信区间:0.09,0.97)和0.50%(95%置信区间:0.20,0.80)。我们未能拒绝死亡后与暴露的零关联(P = 0.93)。鉴于这些结果、先前的研究以及广泛的毒理学支持,PM2.5与死亡之间的关联几乎肯定是因果关系。

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