Simultaneous Estimation of Disease Risks and Spatial Clustering: A Hierarchical Bayes Approach

• Activity Number: 448
• Type: Topic Contributed
• Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Wenning Feng*+ and Chae Young Lim and Tapabrata Maiti
• Companies: Michigan State University and Michigan State University and Michigan State University
• Keywords: Spatial clustering ; Hierarchical modeling ; Reversible jump MCMC ; Disease risk estimation

Abstract

Clustering and estimation of incidence risks from diseases are important in public health and epidemiological research. The popular disease risks models often assume known spatial clustering structure. On the other hand the models that detect spatial cluster are not suitable for disease risk estimation. We propose a methodology that simultaneously estimates disease risks and detects high (low) risk clusters of geographically connected areas. The proposed model is very flexible in terms of local, regional and global shrinking and in terms of number of clusters, cluster memberships and cluster locations. We develop a reversible jump MCMC algorithm for model estimation. Numerical study shows effectiveness of the proposed methodology.