Higbee Joshua D, Lefler Jacob S, Burnett Richard T, Ezzati Majid, Marshall Julian D, Kim Sun-Young, Bechle Matthew, Robinson Allen L, Pope C Arden
Department of Economics, University of Chicago, Chicago, Illinois.
Department of Agricultural and Resource Economics, University of California - Berkeley, Berkeley, California.
Environ Epidemiol. 2020 Apr 9;4(2):e085. doi: 10.1097/EE9.0000000000000085. eCollection 2020 Apr.
Fine particulate matter (PM) is associated with negative health outcomes in both the short and long term. However, the cohort studies that have produced many of the estimates of long-term exposure associations may fail to account for selection bias in pollution exposure as well as covariate imbalance in the study population; therefore, causal modeling techniques may be beneficial.
Twenty-nine years of data from the National Health Interview Survey (NHIS) was compiled and linked to modeled annual average outdoor PM concentration and restricted-use mortality data. A series of Cox proportional hazards models, adjusted using inverse probability weights, yielded causal risk estimates of long-term exposure to ambient PM on all-cause and cardiopulmonary mortality.
Covariate-adjusted estimated relative risks per 10 μg/m increase in PM exposure were estimated to be 1.117 (1.083, 1.152) for all-cause mortality and 1.232 (1.174, 1.292) for cardiopulmonary mortality. Inverse probability weighted Cox models provide relatively consistent and robust estimates similar to those in the unweighted baseline multivariate Cox model, though they have marginally lower point estimates and higher standard errors.
These results provide evidence that long-term exposure to PM contributes to increased mortality risk in US adults and that the estimated effects are generally robust to modeling choices. The size and robustness of estimated associations highlight the importance of clean air as a matter of public health. Estimated confounding due to measured covariates appears minimal in the NHIS cohort, and various distributional assumptions have little bearing on the magnitude or standard errors of estimated causal associations.
细颗粒物（PM）在短期和长期均与负面健康结果相关。然而，生成许多长期暴露关联估计值的队列研究可能未考虑污染暴露中的选择偏倚以及研究人群中的协变量失衡；因此，因果建模技术可能会有所帮助。
收集了来自美国国家健康访谈调查（NHIS）的29年数据，并将其与模拟的年平均室外PM浓度及受限使用的死亡率数据相链接。一系列使用逆概率权重进行调整的Cox比例风险模型得出了长期暴露于环境PM对全因死亡率和心肺死亡率的因果风险估计值。
PM暴露每增加10μg/m³，经协变量调整后的全因死亡率估计相对风险为1.117（1.083，1.152），心肺死亡率估计相对风险为1.232（1.174，1.292）。逆概率加权Cox模型提供的估计值相对一致且稳健，与未加权的基线多变量Cox模型中的估计值相似，不过其点估计值略低，标准误差略高。
这些结果表明，长期暴露于PM会增加美国成年人的死亡风险，并且估计效应通常对建模选择具有稳健性。估计关联的大小和稳健性凸显了清洁空气作为公共卫生问题的重要性。在NHIS队列中，由于测量的协变量导致的估计混杂似乎最小，并且各种分布假设对估计因果关联的大小或标准误差影响不大。
