Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Environ Health Perspect. 2018 Dec;126(12):127002. doi: 10.1289/EHP3130.
Many cohort studies have reported associations between PM and the hazard of dying, but few have used formal causal modeling methods, estimated marginal effects, or directly modeled the loss of life expectancy.
Our goal was to directly estimate the effect of PM on the distribution of life span using causal modeling techniques.
We derived nonparametric estimates of the distribution of life expectancy as a function of PM using data from 16,965,154 Medicare beneficiaries in the Northeastern and mid-Atlantic region states (129,341,959 person-years of follow-up and 6,334,905 deaths). We fit separate inverse probability-weighted logistic regressions for each year of age to estimate the risk of dying at that age given the average PM concentration at each subject's residence ZIP code in the same year, and we used Monte Carlo simulations to estimate confidence intervals.
The estimated mean age at death for a population with an annual average PM exposure of 12 μg/m (the 2012 National Ambient Air Quality Standard) was 0.89 y less (95% CI: 0.88, 0.91) than estimated for a counterfactual PM exposure of 7.5 μg/m. In comparison, life expectancy at 65 y of age increased by 0.9 y between 2004 and 2013 in the United States. We estimated that 23.5% of the Medicare population would die before 76 y of age if exposed to PM at 12 μg/m compared with 20.1% if exposed to an annual average of 7.5 μg/m.
We believe that this is the first study to directly estimate the effect of PM on the distribution of age at death using causal modeling techniques to control for confounding. We find that reducing PM concentrations below the 2012 U.S. annual standard would substantially increase life expectancy in the Medicare population. https://doi.org/10.1289/EHP3130.
许多队列研究报告了 PM 与死亡风险之间的关联，但很少使用正式的因果建模方法、边际效应估计或直接对预期寿命损失进行建模。
我们的目标是使用因果建模技术直接估计 PM 对寿命分布的影响。
我们使用来自东北和大西洋中部地区 16965154 名医疗保险受益人的数据，推导出 PM 作为寿命分布函数的非参数估计值（129341959 人年随访和 6334905 人死亡）。我们为每个年龄组拟合单独的逆概率加权逻辑回归，以估计给定每个受试者居住邮政编码所在年份的平均 PM 浓度下该年龄的死亡风险，我们使用蒙特卡罗模拟来估计置信区间。
每年 PM 暴露 12μg/m（2012 年国家环境空气质量标准）的人群的估计平均死亡年龄比每年 PM 暴露 7.5μg/m 的人群低 0.89 岁（95%CI：0.88，0.91）。相比之下，2004 年至 2013 年，美国 65 岁人群的预期寿命增加了 0.9 岁。我们估计，如果暴露于 12μg/m 的 PM，医疗保险人群中有 23.5%的人会在 76 岁之前死亡，而如果暴露于每年平均 7.5μg/m 的 PM，这一比例为 20.1%。
我们认为，这是第一项使用因果建模技术直接估计 PM 对死亡年龄分布影响的研究，以控制混杂因素。我们发现，将 PM 浓度降低到 2012 年美国年度标准以下，将大大提高医疗保险人群的预期寿命。
