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首页> 外文期刊>Risk analysis >Meta-Analysis Methods to Estimate the Shape and Uncertainty in the Association Between Long-Term Exposure to Ambient Fine Particulate Matter and Cause-Specific Mortality Over the Global Concentration Range
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Meta-Analysis Methods to Estimate the Shape and Uncertainty in the Association Between Long-Term Exposure to Ambient Fine Particulate Matter and Cause-Specific Mortality Over the Global Concentration Range

机译：荟萃分析方法，用于估计长期暴露于环境细颗粒物与特定浓度下的全球浓度范围内的死亡率之间的关系的形状和不确定性

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摘要

Estimates of excess mortality associated with exposure to ambient concentrations of fine particulate matter have been obtained from either a single cohort study or pooling information from a small number of studies. However, standard frequentist methods of pooling are known to underestimate statistical uncertainty in the true risk distribution when the number of studies pooled is small. Alternatively, Bayesian pooling methods using noninformative priors yield unrealistically large amounts of uncertainty in this case. We present a new hybrid frequentist-bayesian framework for meta-analysis that incorporates features of both frequentist and Bayesian approaches, yielding estimated uncertainty distributions that are more useful for burden estimation. We also present an example of mortality risk due to long-term exposure to ambient fine particulate matter obtained from a small number of cohort studies conducted in the United States and Europe. We compare our new risk uncertainty distribution to that obtained by the integrated exposure-response (IER) model used in the Global Burden of Disease 2010 project for which risk was modeled over the entire global concentration range. We suggest a method to incorporate our new risk uncertainty distribution based on the relatively low concentrations observed in the United States and western Europe into the IER model, thus extending risk estimation to the global concentration range.
机译：与单项队列研究或少数研究汇总的信息均得出了与暴露于环境浓度的细颗粒物质相关的超额死亡率的估计值。但是，众所周知，标准的归集法在合并的研究数量较少时会低估真实风险分布中的统计不确定性。或者，在这种情况下，使用非信息先验的贝叶斯合并方法会产生不切实际的大量不确定性。我们提出了一种用于荟萃分析的新的混合贝叶斯混合框架，该框架结合了贝叶斯方法和贝叶斯方法的特征，产生了对不确定性分布更有用的估计负担。我们还提供了一个因在美国和欧洲进行的少量队列研究而长期接触环境细颗粒物质而导致死亡的风险的例子。我们将新的风险不确定性分布与“全球疾病负担2010”项目中使用的综合暴露-响应（IER）模型获得的分布进行了比较，该模型在整个全球浓度范围内对风险进行了建模。我们建议一种方法，将基于在美国和西欧观察到的相对较低浓度的新风险不确定性分布纳入IER模型，从而将风险估计范围扩展到全球浓度范围。

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