A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution.
A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution.
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DOI：
10.1111/biom.12156
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发表时间：
2014-06
期刊：
影响因子：
1.9
通讯作者：
Sahu SK
中科院分区：
文献类型：
--
作者：
Lee D;Rushworth A;Sahu SK
估计空气污染的长期健康影响是一项具有挑战性的任务，特别是在生态研究设计中对空间小区域疾病发病率数据进行建模时。挑战来自于这些数据中未观察到的潜在空间自相关结构，这是使用全局平滑条件自回归模型建模的随机效应来解释的。这些平稳的随机效应混淆了空气污染的影响，而空气污染的影响在全球范围内也是平稳的。为了避免这种共线性的贝叶斯局部条件自回归模型的随机效应。这种局部化模型在空间上是灵活的，在这个意义上，它不仅能够对空间平滑的区域进行建模，而且还能够捕获随机效应表面中的阶跃变化。与使用传统的条件自回归模型相比，这种方法的发展使我们能够提高协变量效应的估计性能。这些结果是建立使用模拟研究，然后说明了我们的激励研究在大格拉斯哥，苏格兰在2011年的空气污染和呼吸道疾病的健康。该模型显示了颗粒物空气污染和二氧化氮对健康的重大影响，其影响一直被目前可用的全球平滑模型所削弱。
Estimation of the long-term health effects of air pollution is a challenging task, especially when modeling spatial small-area disease incidence data in an ecological study design. The challenge comes from the unobserved underlying spatial autocorrelation structure in these data, which is accounted for using random effects modeled by a globally smooth conditional autoregressive model. These smooth random effects confound the effects of air pollution, which are also globally smooth. To avoid this collinearity a Bayesian localized conditional autoregressive model is developed for the random effects. This localized model is flexible spatially, in the sense that it is not only able to model areas of spatial smoothness, but also it is able to capture step changes in the random effects surface. This methodological development allows us to improve the estimation performance of the covariate effects, compared to using traditional conditional auto-regressive models. These results are established using a simulation study, and are then illustrated with our motivating study on air pollution and respiratory ill health in Greater Glasgow, Scotland in 2011. The model shows substantial health effects of particulate matter air pollution and nitrogen dioxide, whose effects have been consistently attenuated by the currently available globally smooth models.
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影响因子：
1.9
作者：
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DOI：
10.1111/rssc.12009
发表时间：
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