Identifying boundaries in spatially continuous risk surfaces from spatially aggregated disease count data

Abstract

Spatially aggregated disease-count data relating to a set of nonoverlapping 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 geographical border between neighbouring units. This paper is the first to relax these assumptions by estimating disease risk and the locations of risk boundaries 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 boundaries 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.