Spatial Modeling of Air Pollution in Studies of Its Short-Term Health Effects
Spatial Modeling of Air Pollution in Studies of Its Short-Term Health Effects
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DOI：
10.1111/j.1541-0420.2009.01376.x
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发表时间：
2010-12-01
期刊：
影响因子：
1.9
通讯作者：
Shaddick, Gavin
中科院分区：
文献类型：
--
作者：
Lee, Duncan;Shaddick, Gavin
在估计空气污染对健康的短期影响的研究中，通常可以从研究区域内的多个监测点获取污染浓度的日常测量结果。然而，健康数据通常仅以整个区域的每日计数形式提供，这意味着需要相应的每日污染测量。标准方法是对监测位置观察到的测量值进行平均，并将其用于对数线性健康模型。然而，由于污染表面在空间上存在变化，这种简单的总结不太可能准确估计整个地区的平均污染浓度，这可能会导致所产生的健康影响出现偏差。在本文中，我们提出了一种替代方法，使用贝叶斯时空模型对污染浓度及其与健康数据的关系进行联合建模。我们通过模拟研究，通过调查污染数据中空间变化、监测器放置和测量误差的影响，将这种方法与简单空间平均值进行比较。随后介绍了大伦敦地区的一项流行病学研究，该研究估计了呼吸道死亡率与四种不同污染物之间的关系。
P>In studies that estimate the short-term effects of air pollution on health, daily measurements of pollution concentrations are often available from a number of monitoring locations within the study area. However, the health data are typically only available in the form of daily counts for the entire area, meaning that a corresponding single daily measure of pollution is required. The standard approach is to average the observed measurements at the monitoring locations, and use this in a log-linear health model. However, as the pollution surface is spatially variable this simple summary is unlikely to be an accurate estimate of the average pollution concentration across the region, which may lead to bias in the resulting health effects. In this article, we propose an alternative approach that jointly models the pollution concentrations and their relationship with the health data using a Bayesian spatio-temporal model. We compare this approach with the simple spatial average using a simulation study, by investigating the impact of spatial variation, monitor placement, and measurement error in the pollution data. An epidemiological study from Greater London is then presented, which estimates the relationship between respiratory mortality and four different pollutants.