IDENTIFYING BOUNDARIES IN SPATIALLY CONTINUOUS RISK SURFACES FROM SPATIALLY AGGREGATED DISEASE COUNT DATA

• 成果类型: Article
• 署名作者: Lee, Duncan
• 署名单位: University of Glasgow
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/23-AOAS1755
• 发表日期: 2023
• 页码: 3153-3172
• 关键词: space-time variation; MODEL; inference; criterion

摘要

Spatially aggregated disease-count data relating to a set of nonoverlap-ping areal units are often used to make inference on population-level disease risk. This includes the identification of risk boundaries, which are locations where there is a sizeable change in risk between geographically neighbouring areal units. Existing studies provide spatially discrete inference on the areal unit footprint, which forces the boundaries to coincide with the entire geo-graphical border between neighbouring units. This paper is the first to relax these assumptions by estimating disease risk and the locations of risk bound-aries on a grid of square pixels covering the study region that can be made arbitrarily small to approximate a spatially continuous surface. We propose a two-stage approach that first fits a Bayesian spatiotemporal realignment model to estimate disease risk at the grid level and then identifies bound-aries in this surface using edge detection algorithms from computer vision. This novel methodological fusion is motivated by a new study of respiratory hospitalisation risk in Glasgow, Scotland, between 2008 and 2017, and we identify numerous risk boundaries across the city.