A LOCALLY ADAPTIVE PROCESS-CONVOLUTION MODEL FOR ESTIMATING THE HEALTH IMPACT OF AIR POLLUTION
A LOCALLY ADAPTIVE PROCESS-CONVOLUTION MODEL FOR ESTIMATING THE HEALTH IMPACT OF AIR POLLUTION
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DOI：
10.1214/18-aoas1167
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发表时间：
2018-12-01
影响因子：
1.8
通讯作者：
Lee, Duncan
中科院分区：
文献类型：
--
作者：
Lee, Duncan
大多数流行病学空气污染研究关注的是住院或死亡等严重后果，但这低估了空气污染的影响，忽视了在初级保健中治疗的健康状况不佳。本文量化了空气污染对苏格兰初级保健中呼吸系统药物处方率的影响，这是不太严重的呼吸系统疾病患病率的替代指标。提出了一种新的双变量时空过程卷积模型，该模型（i）通过基于最近邻域的锥形函数提高了计算效率;（ii）具有优于传统距离衰减核的局部自适应权重。结果表明，颗粒物对呼吸预记录率有显著影响，这与重度终点研究一致。
Most epidemiological air pollution studies focus on severe outcomes such as hospitalisations or deaths, but this underestimates the impact of air pollution by ignoring ill health treated in primary care. This paper quantifies the impact of air pollution on the rates of respiratory medication prescribed in primary care in Scotland, which is a proxy measure for the prevalence of less severe respiratory disease. A novel bivariate spatiotemporal process-convolution model is proposed, which: (i) has increased computational effi-ciency via a tapering function based on nearest neighbourhoods; and (ii) has locally adaptive weights that outperform traditional distance-decay kernels. The results show significant effects of particulate matter on respiratory pre-scription rates which are consistent with severe endpoint studies.