Estimating the mortality impacts of particulate matter: what can be learned from between-study variability?
Estimating the mortality impacts of particulate matter: what can be learned from between-study variability?
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DOI：
10.1289/ehp.00108109
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发表时间：
2000-02
影响因子：
10.4
通讯作者：
Spengler JD
中科院分区：
文献类型：
--
作者：
Levy JI;Hammitt JK;Spengler JD
对颗粒物(PM)浓度和死亡率之间联系的流行病学研究得出了一系列估计，导致对这种联系的大小和因果联系的强度存在分歧。以前对这篇文献的荟萃分析提供了综合效应估计，但没有解决可能与分析模型、污染模式和暴露人群相关的研究之间的变异性。为了确定特定研究因素是否能解释空气动力学直径[小于/等于]10微米颗粒物(PM(10))引起的死亡率时间序列研究中的一些变异性，我们应用了经验性贝叶斯荟萃分析。我们估计，PM(10)浓度每增加10微克/立方米，死亡率平均增加0.7%，在空气动力学直径(PM(2.5))/PM(10)的颗粒物[小于/等于]2.5微米比率较高的地点，影响更大。尽管有一些证据表明PM的影响受到气候、住房特征、人口统计以及二氧化硫和臭氧的存在的影响，但这一发现并没有随着一些潜在的混杂因素和效果改进剂的加入而改变。虽然还需要进一步的分析来确定哪些因素对PM(10)和死亡率之间的关系产生因果影响，但这些发现可以帮助指导未来的流行病学调查和政策决策。
Epidemiologic studies of the link between particulate matter (PM) concentrations and mortality rates have yielded a range of estimates, leading to disagreement about the magnitude of the relationship and the strength of the causal connection. Previous meta-analyses of this literature have provided pooled effect estimates, but have not addressed between-study variability that may be associated with analytical models, pollution patterns, and exposed populations. To determine whether study-specific factors can explain some of the variability in the time-series studies on mortality from particulate matter [less than/equal to] 10 microm in aerodynamic diameter (PM(10)), we applied an empirical Bayes meta-analysis. We estimate that mortality rates increase on average by 0.7% per 10 microg/m(3) increase in PM(10) concentrations, with greater effects at sites with higher ratios of particulate matter [less than/equal to] 2.5 microm in aerodynamic diameter (PM(2.5))/PM(10). This finding did not change with the inclusion of a number of potential confounders and effect modifiers, although there is some evidence that PM effects are influenced by climate, housing characteristics, demographics, and the presence of sulfur dioxide and ozone. Although further analysis would be needed to determine which factors causally influence the relationship between PM(10) and mortality, these findings can help guide future epidemiologic investigations and policy decisions.