Bayesian cluster detection via adjacency modelling. (February 2016)

• Record Type: Journal Article
• Authors: Anderson, Craig; Lee, Duncan; Dean, Nema
• Is Part Of: Spatial and spatio-temporal epidemiology. Volume 16(2016)
• Page Start: 11
• Page End: 20
• Publication Date: 2016-02
• Subjects: Clustering -- Conditional autoregressive model -- Disease mapping -- Spatial modelling; Epidemiology -- Statistical methods -- Periodicals; Epidemiology -- Periodicals; 614.4072
• DOI: 10.1016/j.sste.2015.11.005
• Languages: English
• ISSNs: 1877-5845

Abstract

Highlights: We develop a two-stage spatial model for clustering disease risk. First stage produces potential cluster structures, second stage fits these within a CAR model. Model outperforms alternatives in a simulation study. Applied to COPD mortalities in English local authorities and identifies north–south divide. Graphical abstract: Abstract: Disease mapping aims to estimate the spatial pattern in disease risk across an area, identifying units which have elevated disease risk. Existing methods use Bayesian hierarchical models with spatially smooth conditional autoregressive priors to estimate risk, but these methods are unable to identify the geographical extent of spatially contiguous high-risk clusters of areal units. Our proposed solution to this problem is a two-stage approach, which produces a set of potential cluster structures for the data and then chooses the optimal structure via a Bayesian hierarchical model. The first stage uses a spatially adjusted hierarchical agglomerative clustering algorithm. The second stage fits a Poisson log-linear model to the data to estimate the optimal cluster structure and the spatial pattern in disease risk. The methodology was applied to a study of chronic obstructive pulmonary disease (COPD) in local authorities in England, where a number of high risk clusters were identified.