Boundary detection in disease mapping studies

TL;DR

This paper introduces a Bayesian method for detecting risk boundaries in disease mapping, addressing the challenge of identifying abrupt changes in disease risk in complex urban environments.

Contribution

It proposes a novel approach for boundary detection in disease risk surfaces within a Bayesian hierarchical framework, validated through simulations and real data application.

Findings

Effective boundary detection demonstrated in simulations

Successful application to lung cancer data in Glasgow

Improved understanding of spatial risk heterogeneity

Abstract

In disease mapping, the aim is to estimate the spatial pattern in disease risk over an extended geographical region, so that areas with elevated risks can be identified. A Bayesian hierarchical approach is typically used to produce such maps, which models the risk surface with a set of spatially smooth random effects. However, in complex urban settings there are likely to be boundaries in the risk surface, which separate populations that are geographically adjacent but have very different risk profiles. Therefore this paper proposes an approach for detecting such risk boundaries, and tests its effectiveness by simulation. Finally, the model is applied to lung cancer incidence data in Greater Glasgow, Scotland, between 2001 and 2005.