Using Hierarchical Spatial Models for Cancer Control Planning in Minnesota

• Activity Number: 354
• Type: Contributed
• Date/Time: Wednesday, August 14, 2002 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Stat. Sciences*
• Author(s): Margaret Short*+ and Bradley Carlin and Sally Bushhouse
• Affiliation(s): University of Minnesota and University of Minnesota and Minnesota Department of Health
• Keywords: Cancer control ; Cancer incidence ; Cancer mortality ; Disease mapping ; Statistical model

Abstract

Region-specific maps of cancer incidence, mortality, late detection rates, and screening rates can be very helpful in the planning, targeting, and coordination of cancer control activities. Unfortunately, past efforts have not used appropriate statistical models that account for the correlation of rates across both neighboring regions and different cancer types. Our objective is to develop such models and apply them to the problem of cancer control in the counties of Minnesota during the period 1993-97. We accomplish this using hierarchical Bayesian spatial statistical methods, implemented using modern Markov chain Monte Carlo computing techniques and software. This approach results in spatially smoothed maps emphasizing either cancer prevention or cancer outcome for breast, colorectal, and lung cancer, as well as an overall map which combines results from these three individual cancers. We conclude that our methods enable a more statistically accurate picture of the geographic distribution of important cancer prevention and outcome variables in Minnesota, and appear useful for making decisions regarding targeting cancer control resources within the state.