Estimating constrained concentration-response functions between air pollution and health
Estimating constrained concentration-response functions between air pollution and health
复制标题
DOI：
10.1002/env.1150
复制
发表时间：
2012-05-01
期刊：
影响因子：
1.7
通讯作者：
Bowman, Adrian
中科院分区：
文献类型：
--
作者：
Powell, Helen;Lee, Duncan;Bowman, Adrian
与短期暴露于空气污染有关的健康风险是最近许多研究的重点，其中大多数研究考虑了环境污染浓度与健康反应之间的线性浓度响应函数（CRFs）。少数研究放宽了这种线性假设，并允许从数据中估计函数的形状。然而，这种灵活性的增加导致对CRFs的估计似乎不可行，往往显示随着浓度的增加对健康的风险降低。因此，本文提出了一种基于单调积分样条的贝叶斯层次模型来估计这种情况下的约束crf。由于相关的回归参数被约束为非负，这些样条产生非递减的CRFs，我们通过用slab和spike先验对后者进行建模来确保这一点。在将我们的方法应用于2000年至2005年期间大伦敦地区臭氧浓度和呼吸系统疾病的研究之前，通过模拟评估了我们方法的有效性。版权所有：John Wiley & Sons， Ltd
The health risks associated with short-term exposure to air pollution have been the focus of much recent research, most of which has considered linear concentrationresponse functions (CRFs) between ambient concentrations of pollution and a health response. A much smaller number of studies have relaxed this assumption of linearity and allowed the shape of the function to be estimated from the data. However, this increased flexibility has resulted in CRFs being estimated that appear unfeasible, often showing decreases in the risk to health with increasing concentrations. Therefore, this paper proposes a Bayesian hierarchical model for estimating constrained CRFs in this context, which is based on monotonic integrated splines. These splines produce non-decreasing CRFs, owing to the associated regression parameters being constrained to be non-negative, which we ensure by modelling the latter with a slab and spike prior. The efficacy of our approach is assessed via simulation before being applied to a study of ozone concentrations and respiratory disease in Greater London between 2000 and 2005. Copyright (C) 2012 John Wiley & Sons, Ltd.