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

TL;DR

This paper introduces an adaptive spatio-temporal smoothing model that effectively captures both smooth variations and abrupt step changes in disease risk across regions, improving the accuracy of risk estimation.

Contribution

The paper presents a novel model that allows for local adaptation in spatial smoothing, addressing heterogeneity and step changes in disease risk estimation.

Findings

Identified common step changes in disease risk between neighboring regions.

Demonstrated the model's effectiveness through simulation studies.

Applied the model to respiratory and circulatory disease data in England.

Abstract

Statistical models used to estimate the spatio-temporal pattern in disease risk from areal unit data represent the risk surface for each time period with known covariates and a set of spatially smooth random effects. The latter act as a proxy for unmeasured spatial confounding, whose spatial structure is often characterised by a spatially smooth evolution between some pairs of adjacent areal units while other pairs exhibit large step changes. This spatial heterogeneity is not consistent with existing global smoothing models, in which partial correlation exists between all pairs of adjacent spatial random effects. Therefore we propose a novel space-time disease model with an adaptive spatial smoothing specification that can identify step changes. The model is motivated by a new study of respiratory and circulatory disease risk across the set of Local Authorities in England, and is rigorously tested by simulation to assess its efficacy. Results from the England study show that the two diseases have similar spatial patterns in risk, and exhibit a number of common step changes in the unmeasured component of risk between neighbouring local authorities.