Margaret Short1, Bradley P Carlin, Sally Bushhouse. 1. Division of Biostatistics, School of Public Health, University of Minnesota, MMC 303, 420 Delaware St SE, Minneapolis, Minnesota 55455, USA.
Abstract
OBJECTIVE: 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 in this area have been few, and have not used appropriate statistical models that account for the correlation of rates across both neighboring regions and different cancer types. In this article we develop such models, and apply them to the problem of cancer control in the counties of Minnesota during the period 1993-1997. METHODS: We use hierarchical Bayesian spatial statistical methods, implemented using modern Markov chain Monte Carlo computing techniques and software. RESULTS: Our 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. CONCLUSIONS: Our methods enable us to produce 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.
OBJECTIVE: 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 in this area have been few, and have not used appropriate statistical models that account for the correlation of rates across both neighboring regions and different cancer types. In this article we develop such models, and apply them to the problem of cancer control in the counties of Minnesota during the period 1993-1997. METHODS: We use hierarchical Bayesian spatial statistical methods, implemented using modern Markov chain Monte Carlo computing techniques and software. RESULTS: Our 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. CONCLUSIONS: Our methods enable us to produce 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.