BACKGROUND Cohort studies have documented associations between fine particulate matter air pollution (PM2.5) and mortality risk. However, there remains uncertainty regarding the contribution of co-pollutants and the stability of pollution-mortality associations in models that include multiple air pollutants. Furthermore, it is unclear whether the PM2.5-mortality relationship varies spatially, when exposures are decomposed according to scale of spatial variability, or temporally, when effect estimates are allowed to change between years. METHODS A cohort of 635,539 individuals was compiled using public National Health Interview Survey (NHIS) data from 1987 to 2014 and linked with mortality follow-up through 2015. Modelled air pollution exposure estimates for PM2.5, other criteria air pollutants, and spatial decompositions (< 1 km, 1-10 km, 10-100 km, > 100 km) of PM2.5 were assigned at the census-tract level. The NHIS samples were also divided into yearly cohorts for temporally-decomposed analyses. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) in regression models that included up to six criteria pollutants; four spatial decompositions of PM2.5; and two- and five-year lagged mean PM2.5 exposures in the temporally-decomposed cohorts. Meta-analytic fixed-effect estimates were calculated using results from temporally-decomposed analyses and compared with time-independent results using 17- and 28-year exposure windows. RESULTS In multiple-pollutant analyses, PM2.5 demonstrated the most robust pollutant-mortality association. Coarse fraction particulate matter (PM2.5-10) and sulfur dioxide (SO2) were also associated with excess mortality risk. The PM2.5-mortality association was observed across all four spatial scales of PM2.5, with higher but less precisely estimated HRs observed for local (< 1 km) and neighborhood (1-10 km) variations. In temporally-decomposed analyses, the PM2.5-mortality HRs were stable across yearly cohorts. The meta-analytic HR using two-year lagged PM2.5 equaled 1.10 (95% CI 1.07, 1.13) per 10 μg/m3. Comparable results were observed in time-independent analyses using a 17-year (HR 1.13, CI 1.09, 1.16) or 28-year (HR 1.09, CI 1.07, 1.12) exposure window. CONCLUSIONS Long-term exposures to PM2.5, PM2.5-10, and SO2 were associated with increased risk of all-cause and cardiopulmonary mortality. Each spatial decomposition of PM2.5 was associated with mortality risk, and PM2.5-mortality associations were consistent over time.

中文翻译：

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美国一大批具有代表性的人群的空气污染和死亡率：多种污染物分析以及时空分解。

背景技术队列研究已记录了细颗粒物空气污染（PM2.5）与死亡风险之间的关联。但是，在包括多种空气污染物的模型中，关于共污染物的贡献以及污染死亡率关联性的稳定性仍然存在不确定性。此外，尚不清楚PM2.5死亡率关系是在空间上变化（当根据空间可变性的规模对暴露进行分解时）还是在时间上（当效果估计值在几年之间发生变化时）变化。方法使用1987年至2014年的国家健康公开访问（NHIS）公开数据，收集635,539名患者的队列，并与至2015年的死亡率随访相关。模型化的PM2.5，其他标准空气污染物和空间分解的空气污染暴露量估算值（<1公里，1-10公里，在普查范围内分配了10-100公里（> 100公里）的PM2.5。NHIS样本也被分为每年的队列，以进行时间分解分析。在包含多达六种标准污染物的回归模型中，使用Cox比例危害模型估算危害比（HRs）和95％置信区间（CIs）。PM2.5的四个空间分解；在经过时间分解的人群中，平均PM2.5暴露量为两年和五年。使用时间分解分析的结果计算荟萃分析的固定效应估计值，并使用17年和28年的暴露时间窗与不依赖时间的结果进行比较。结果在多污染物分析中，PM2.5表现出最强的污染物死亡率关联性。粗颗粒物（PM2。5-10）和二氧化硫（SO2）也与过度死亡风险相关。在PM2.5的所有四个空间尺度上都观察到了PM2.5死亡率关联，对于局部（<1 km）和邻域（1-10 km）变化观察到的HR较高，但估计精度较低。在时间分解分析中，PM2.5死亡率HR在每年的队列中均保持稳定。使用两年滞后的PM2.5进行的荟萃分析HR等于每10μg/ m3 1.10（95％CI 1.07，1.13）。在时间独立的分析中，使用17年（HR 1.13，CI 1.09，1.16）或28年（HR 1.09，CI 1.07，1.12）暴露时间窗，可以观察到可比较的结果。结论长期暴露于PM2.5，PM2.5-10和SO2与全因和心肺死亡率的风险增加有关。PM2.5的每个空间分解都与死亡风险和PM2相关。