Controlling for localised spatio-temporal autocorrelation in long-term air pollution and health studies.
Controlling for localised spatio-temporal autocorrelation in long-term air pollution and health studies.
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DOI：
10.1177/0962280214527384
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发表时间：
2014-12
影响因子：
2.3
通讯作者：
Mitchell R
中科院分区：
文献类型：
--
作者：
Lee D;Mitchell R
使用生态时空研究设计估计空气污染的长期健康影响是一项具有挑战性的任务，因为在调整协变量效应后，健康计数中存在残留的时空自相关。这种自相关通常由一组随机效应建模，这些随机效应由高斯马尔可夫随机场（GMRF）先验分布表示，作为分层贝叶斯模型的一部分。然而，GMRF模型通常假设随机效应在空间和时间上是全局平滑的，因此可能与任何空间和时间平滑的协变量（如空气污染）共线。这种共线性导致估计的固定效应的估计性能差，并出于这个流行病学问题，本文提出了新的GMRF方法，允许本地化的时空平滑。这意味着允许地理上或时间上相邻的随机效应是自相关的或条件独立的，这允许表示更灵活的自相关结构。与全局平滑模型相比，这种灵活性的增加导致固定效应估计的改善，这一点通过我们的模拟研究得到了证明。然后，该方法被应用到激励研究调查的长期影响，在大格拉斯哥，苏格兰在2007年和2011年之间的空气污染对呼吸系统疾病的健康。
Estimating the long-term health impact of air pollution using an ecological spatio-temporal study design is a challenging task, due to the presence of residual spatio-temporal autocorrelation in the health counts after adjusting for the covariate effects. This autocorrelation is commonly modelled by a set of random effects represented by a Gaussian Markov random field (GMRF) prior distribution, as part of a hierarchical Bayesian model. However, GMRF models typically assume the random effects are globally smooth in space and time, and thus are likely to be collinear to any spatially and temporally smooth covariates such as air pollution. Such collinearity leads to poor estimation performance of the estimated fixed effects, and motivated by this epidemiological problem, this paper proposes new GMRF methodology to allow for localised spatio-temporal smoothing. This means random effects that are either geographically or temporally adjacent are allowed to be autocorrelated or conditionally independent, which allows more flexible autocorrelation structures to be represented. This increased flexibility results in improved fixed effects estimation compared with global smoothing models, which is evidenced by our simulation study. The methodology is then applied to the motivating study investigating the long-term effects of air pollution on respiratory ill health in Greater Glasgow, Scotland between 2007 and 2011.
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DOI：
10.1111/rssc.12009
发表时间：
2013-08-01
影响因子：
1.6
作者：
Lee, Duncan;Mitchell, Richard
通讯作者：
Mitchell, Richard
影响因子：
3.4
作者：
Haining, Robert;Li, Guangquan;Richardson, Sylvia
通讯作者：
Richardson, Sylvia
影响因子：
5.4
作者：
Janes, Holly;Dominici, Francesca;Zeger, Scott L.
通讯作者：
Zeger, Scott L.
影响因子：
6.3
作者：
Kleinschmidt, I;Hills, M;Elliott, P
通讯作者：
Elliott, P
影响因子：
3.4
作者：
Lee, Duncan
通讯作者：
Lee, Duncan