PM source apportionment and health effects: 2. An investigation of intermethod variability in associations between source-apportioned fine particle mass and daily mortality in Washington, DC
PM source apportionment and health effects: 2. An investigation of intermethod variability in associations between source-apportioned fine particle mass and daily mortality in Washington, DC
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DOI：
10.1038/sj.jea.7500464
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发表时间：
2006-07-01
影响因子：
4.5
通讯作者：
Thurston, George D.
中科院分区：
文献类型：
--
作者：
Ito, Kazuhiko;Christensen, William F.;Thurston, George D.
源解析可能是有用的PM健康影响的流行病学调查，但这些方法的变化和选择留下不确定性。EPA主办的研讨会调查了来源分配和健康影响分析，通过检查每日死亡率和研究人员估计的华盛顿，DC 1988-1997年来源分配的PM2.5之间的关联。使用泊松广义线性模型（GLM）估计延迟0-4天的总非意外、心血管和心肺死亡率的源特异性相对风险，并根据天气、季节/时间趋势和星期几进行调整。源相关效应估计值及其滞后关联模式在研究者/方法之间相似。不同来源类型之间的关联的不同滞后结构，加上周三/周六的采样频率，使得很难以简单的方式比较特定来源的效应大小。对于总死亡率，PM2.5每5-95（第）百分位数增量的最大（和最显著）超额死亡百分比是二次硫酸盐（方差加权平均超额死亡百分比= 6.7%（95%CI：1.7，11.7）），但具有特殊的滞后结构（滞后3天）。与煤有关的主要PM2.5（只有三个小组）与总死亡率有类似的显著相关性，与硫酸盐有相同的3天滞后。对交通相关PM2.5的风险估计虽然在某些情况下很重要，但变化更大。土壤相关的PM显示较小的效应大小估计，但他们更一致的积极在多个滞后。心血管和心肺死亡率的相关性与总死亡率的相关性基本相似。其他天气模型通常给出类似的模式，但有时会影响滞后结构（例如，对于硫酸盐）。总体而言，研究者/方法之间的相对风险变化远小于估计来源类型之间或这些数据的滞后日之间的相对风险变化。这种一致性表明，健康影响分析的源解析的鲁棒性，但仍然存在的问题，包括源解析的准确性和特定源对天气模型的敏感性，需要进行调查。
Source apportionment may be useful in epidemiological investigation of PM health effects, but variations and options in these methods leave uncertainties. An EPA-sponsored workshop investigated source apportionment and health effects analyses by examining the associations between daily mortality and the investigators' estimated source-apportioned PM2.5 for Washington, DC for 1988-1997. A Poisson Generalized Linear Model (GLM) was used to estimate source-specific relative risks at lags 0-4 days for total non-accidental, cardiovascular, and cardiorespiratory mortality adjusting for weather, seasonal/temporal trends, and day-of-week. Source-related effect estimates and their lagged association patterns were similar across investigators/methods. The varying lag structure of associations across source types, combined with the Wednesday/Saturday sampling frequency made it difficult to compare the source-specific effect sizes in a simple manner. The largest (and most significant) percent excess deaths per 5-95(th) percentile increment of apportioned PM2.5 for total mortality was for secondary sulfate (variance-weighted mean percent excess mortality = 6.7% (95% Cl: 1.7, 11.7)), but with a peculiar lag structure (lag 3 day). Primary coal-related PM2.5 (only three teams) was similarly significantly associated with total mortality with the same 3-day lag as sulfate. Risk estimates for traffic-related PM2.5, while significant in some cases, were more variable. Soil-related PM showed smaller effect size estimates, but they were more consistently positive at multiple lags. The cardiovascular and cardiorespiratory mortality associations were generally similar to those for total mortality. Alternative weather models generally gave similar patterns, but sometimes affected the lag structure (e.g., for sulfate). Overall, the variations in relative risks across investigators/methods were found to be much smaller than those across estimated source types or across lag days for these data. This consistency suggests the robustness of the source apportionment in health effects analyses, but remaining issues, including accuracy of source apportionment and source-specific sensitivity to weather models, need to be investigated.