Inference from ecological models: Estimating the relative risk of stroke from air pollution exposure using small area data
Inference from ecological models: Estimating the relative risk of stroke from air pollution exposure using small area data
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DOI：
10.1016/j.sste.2010.03.006
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发表时间：
2010-07-01
影响因子：
3.4
通讯作者：
Richardson, Sylvia
中科院分区：
文献类型：
--
作者：
Haining, Robert;Li, Guangquan;Richardson, Sylvia
Maheswaran等人（2006年）使用具有空间随机效应的泊松贝叶斯分层模型分析了室外模拟的NOx水平（分为五分位数）对卒中死亡率的影响。在小区域（枚举区）水平上观察到较高水平的NOx和中风死亡率之间的关联。由于该模型是从生态学角度构建的，因此相对风险估计存在生态偏差。在本文中，我们使用基于杰克逊等人（2008）的不同模型规范，将卒中死亡病例数建模为二项式随机变量，其中p（i）是区域i中卒中死亡的概率。室外模拟NOx水平的区域内变化用于确定区域i中属于五个暴露类别中的每一个的人口比例，以估计在假设整个研究区域的均匀效应的情况下，第k个NOx暴露水平下个人死于中风的概率。在生态回归模型中纳入区域内变异性已被证明有助于减少生态偏差（杰克逊等人，2006年、2008年）的报告。获得相对风险的修订估计值，并与以前的估计值进行比较。(C)2010年爱思唯尔公司All rights reserved.
Maheswaran et al. (2006) analysed the effect of outdoor modelled NOx levels, classified into quintiles, on stroke mortality using a Poisson Bayesian hierarchical model with spatial random effects. An association was observed between higher levels of NOx and stroke mortality at the small area (enumeration district) level.As this model is framed in an ecological perspective, the relative risk estimates suffer from ecological bias. In this paper we use a different model specification based on Jackson et al. (2008), modelling the number of cases of mortality due to stroke as a binomial random variable where p(i) is the probability of dying from stroke in area i. The within-area variation in outdoor modelled NOx levels is used to determine the proportion of the population in area i falling into each of the five exposure categories in order to estimate the probability of an individual dying from stroke given the kth level of NOx exposure assuming a homogeneous effect across the study region. The inclusion of within-area variability in an ecological regression model has been demonstrated to help reduce the ecological bias (Jackson et al., 2006, 2008). Revised estimates of relative risk are obtained and compared with previous estimates. (C) 2010 Elsevier Inc. All rights reserved.