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Statistical models used to estimate the spatio-temporal pattern in diseaserisk from areal unit data represent the risk surface for each time period withknown covariates and a set of spatially smooth random effects. The latter actas a proxy for unmeasured spatial confounding, whose spatial structure is oftencharacterised by a spatially smooth evolution between some pairs of adjacentareal units while other pairs exhibit large step changes. This spatialheterogeneity is not consistent with existing global smoothing models, in whichpartial correlation exists between all pairs of adjacent spatial randomeffects. Therefore we propose a novel space-time disease model with an adaptivespatial smoothing specification that can identify step changes. The model ismotivated by a new study of respiratory and circulatory disease risk across theset of Local Authorities in England, and is rigorously tested by simulation toassess its efficacy. Results from the England study show that the two diseaseshave similar spatial patterns in risk, and exhibit a number of common stepchanges in the unmeasured component of risk between neighbouring localauthorities.

An adaptive spatio-temporal smoothing model for estimating trends and step changes in disease risk