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特定细颗粒物与死亡率之间的空间关联。

Spatial association between speciated fine particles and mortality.

作者信息

Fuentes Montserrat, Song Hae-Ryoung, Ghosh Sujit K, Holland David M, Davis Jerry M

机构信息

Statistics Department, North Carolina State University, Box 8203, Raleigh, North Carolina 27695, USA.

出版信息

Biometrics. 2006 Sep;62(3):855-63. doi: 10.1111/j.1541-0420.2006.00526.x.

Abstract

Particulate matter (PM) has been linked to a range of serious cardiovascular and respiratory health problems, including premature mortality. The main objective of our research is to quantify uncertainties about the impacts of fine PM exposure on mortality. We develop a multivariate spatial regression model for the estimation of the risk of mortality associated with fine PM and its components across all counties in the conterminous United States. We characterize different sources of uncertainty in the data and model the spatial structure of the mortality data and the speciated fine PM. We consider a flexible Bayesian hierarchical model for a space-time series of counts (mortality) by constructing a likelihood-based version of a generalized Poisson regression model that combines methods for point-level misaligned data and change of support regression. Our results seem to suggest an increase by a factor of two in the risk of mortality due to fine particles with respect to coarse particles. Our study also shows that in the Western United States, the nitrate and crustal components of the speciated fine PM seem to have more impact on mortality than the other components. On the other hand, in the Eastern United States, sulfate and ammonium explain most of the fine PM effect.

摘要

颗粒物(PM)与一系列严重的心血管和呼吸系统健康问题相关,包括过早死亡。我们研究的主要目的是量化细颗粒物暴露对死亡率影响的不确定性。我们开发了一个多元空间回归模型,用于估计美国本土所有县与细颗粒物及其成分相关的死亡风险。我们刻画了数据中不同的不确定性来源,并对死亡率数据和特定细颗粒物的空间结构进行建模。通过构建基于似然的广义泊松回归模型的版本,该版本结合了点级未对齐数据的方法和支持回归的变化,我们考虑了一个针对时空计数序列(死亡率)的灵活贝叶斯层次模型。我们的结果似乎表明,细颗粒物导致的死亡风险相对于粗颗粒物增加了两倍。我们的研究还表明,在美国西部,特定细颗粒物中的硝酸盐和地壳成分对死亡率的影响似乎比其他成分更大。另一方面,在美国东部,硫酸盐和铵解释了大部分细颗粒物的影响。

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