Lee Duncan, Mukhopadhyay Sabyasachi, Rushworth Alastair, Sahu Sujit K
School of Mathematics and Statistics, University of Glasgow, 15 University Gardens, Glasgow G12 8QW,
School of Mathematics, University of Southampton, Building 54, Salisbury Road, Southampton SO17 1BJ, UK.
Biostatistics. 2017 Apr 1;18(2):370-385. doi: 10.1093/biostatistics/kxw048.
In the United Kingdom, air pollution is linked to around 40000 premature deaths each year, but estimating its health effects is challenging in a spatio-temporal study. The challenges include spatial misalignment between the pollution and disease data; uncertainty in the estimated pollution surface; and complex residual spatio-temporal autocorrelation in the disease data. This article develops a two-stage model that addresses these issues. The first stage is a spatio-temporal fusion model linking modeled and measured pollution data, while the second stage links these predictions to the disease data. The methodology is motivated by a new five-year study investigating the effects of multiple pollutants on respiratory hospitalizations in England between 2007 and 2011, using pollution and disease data relating to local and unitary authorities on a monthly time scale.
在英国，空气污染每年导致约40000人过早死亡，但在时空研究中估计其对健康的影响具有挑战性。这些挑战包括污染数据与疾病数据之间的空间错位；估计污染表面的不确定性；以及疾病数据中复杂的剩余时空自相关性。本文开发了一个两阶段模型来解决这些问题。第一阶段是一个时空融合模型，将模拟的污染数据与实测污染数据联系起来，而第二阶段则将这些预测结果与疾病数据联系起来。该方法的灵感来自于一项新的为期五年的研究，该研究调查了2007年至2011年期间多种污染物对英格兰呼吸道住院治疗的影响，使用了按月时间尺度的与地方和单一管理区相关的污染和疾病数据。
