Using prior information to identify boundaries in disease risk maps

TL;DR

This paper introduces an extension to CAR priors for disease risk maps that can detect both smooth regions and boundaries, using informative priors to improve boundary identification.

Contribution

It proposes a novel method to identify risk boundaries in disease maps by extending CAR priors with informative priors on boundary locations.

Findings

Effective boundary detection demonstrated in simulations

Improved risk mapping in Glasgow hospital admission study

Method enhances spatial risk analysis accuracy

Abstract

Disease maps display the spatial pattern in disease risk, so that high-risk clusters can be identified. The spatial structure in the risk map is typically represented by a set of random effects, which are modelled with a conditional autoregressive (CAR) prior. Such priors include a global spatial smoothing parameter, whereas real risk surfaces are likely to include areas of smooth evolution as well as discontinuities, the latter of which are known as risk boundaries. Therefore, this paper proposes an extension to the class of CAR priors, which can identify both areas of localised spatial smoothness and risk boundaries. However, allowing for this localised smoothing requires large numbers of correlation parameters to be estimated, which are unlikely to be well identified from the data. To address this problem we propose eliciting an informative prior about the locations of such boundaries, which can be combined with the information from the data to provide more precise posterior inference. We test our approach by simulation, before applying it to a study of the risk of emergency admission to hospital in Greater Glasgow, Scotland.