A Shared Component Model for Detecting Joint and Selective Clustering of Two Diseases

TLDR

Spatial epidemiology often uses clustering to identify disease hotspots, but recent advances have mainly addressed single diseases; by considering shared risk factors across diseases, joint spatial patterns can reveal stronger evidence of underlying risk surfaces. The study introduces a shared component model to jointly analyze the spatial distribution of two diseases. The model decomposes each disease’s risk into shared and disease‑specific components, estimates them simultaneously with reversible‑jump MCMC spatial cluster models, and demonstrates the approach on German oral and oesophageal cancer mortality data from 1986–1990.

Abstract

Summary The study of spatial variations in disease rates is a common epidemiological approach used to describe the geographical clustering of diseases and to generate hypotheses about the possible ‘causes’ which could explain apparent differences in risk. Recent statistical and computational developments have led to the use of realistically complex models to account for overdispersion and spatial correlation. However, these developments have focused almost exclusively on spatial modelling of a single disease. Many diseases share common risk factors (smoking being an obvious example) and, if similar patterns of geographical variation of related diseases can be identified, this may provide more convincing evidence of real clustering in the underlying risk surface. We propose a shared component model for the joint spatial analysis of two diseases. The key idea is to separate the underlying risk surface for each disease into a shared and a disease-specific component. The various components of this formulation are modelled simultaneously by using spatial cluster models implemented via reversible jump Markov chain Monte Carlo methods. We illustrate the methodology through an analysis of oral and oesophageal cancer mortality in the 544 districts of Germany, 1986–1990.