School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8SQ, UK.
Stat Med. 2018 Mar 30;37(7):1134-1148. doi: 10.1002/sim.7570. Epub 2017 Dec 4.
The long-term health effects of air pollution are often estimated using a spatio-temporal ecological areal unit study, but this design leads to the following statistical challenges: (1) how to estimate spatially representative pollution concentrations for each areal unit; (2) how to allow for the uncertainty in these estimated concentrations when estimating their health effects; and (3) how to simultaneously estimate the joint effects of multiple correlated pollutants. This article proposes a novel 2-stage Bayesian hierarchical model for addressing these 3 challenges, with inference based on Markov chain Monte Carlo simulation. The first stage is a multivariate spatio-temporal fusion model for predicting areal level average concentrations of multiple pollutants from both monitored and modelled pollution data. The second stage is a spatio-temporal model for estimating the health impact of multiple correlated pollutants simultaneously, which accounts for the uncertainty in the estimated pollution concentrations. The novel methodology is motivated by a new study of the impact of both particulate matter and nitrogen dioxide concentrations on respiratory hospital admissions in Scotland between 2007 and 2011, and the results suggest that both pollutants exhibit substantial and independent health effects.
空气污染的长期健康影响通常使用时空生态区域单位研究进行估计，但这种设计会带来以下统计挑战：（1）如何为每个区域单位估计具有空间代表性的污染浓度；（2）如何在估计其健康影响时考虑这些估计浓度的不确定性；（3）如何同时估计多个相关污染物的联合影响。本文提出了一种新颖的两阶段贝叶斯层次模型来解决这 3 个挑战，推理基于马尔可夫链蒙特卡罗模拟。第一阶段是一个多元时空融合模型，用于从监测和建模的污染数据中预测多个污染物的区域平均浓度。第二阶段是一个时空模型，用于同时估计多个相关污染物的健康影响，同时考虑到估计污染浓度的不确定性。该新方法的动机是对苏格兰 2007 年至 2011 年间颗粒物和二氧化氮浓度对呼吸道疾病入院的影响进行的一项新研究，结果表明两种污染物都具有显著且独立的健康影响。
