Using hierarchical spatial models for cancer control planning in Minnesota (United States)

Margaret Short, Bradley P. Carlin, Sally Bushhouse

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)903-916
Number of pages14
JournalCancer Causes and Control
Issue number10
StatePublished - Dec 2002

Bibliographical note

Funding Information:
The first two authors were supported in part by National Science Foundation (NSF)/Environmental Protection Agency (EPA) grant SES 99-78238, while the second author was also supported in part by National Institute of Environmental Health (NIEHS) grant 2-R01-ES07750. The authors thank Michael Malone and Jane Korn of the Minnesota Department of Health (MDH) for comments that greatly improved the presentation. The authors also thank Nagi Salem of the MDH for compiling and providing the health-related behavior data. Collection of the staging data utilized in this publication was supported by Cooperative Agreement Number U75/CCU510693 from the Centers for Disease Control and Prevention (CDC). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the NSF, EPA, NIEHS, MDH, or CDC.


  • Cancer control
  • Cancer incidence
  • Cancer mortality
  • Disease mapping
  • Statistical model


Dive into the research topics of 'Using hierarchical spatial models for cancer control planning in Minnesota (United States)'. Together they form a unique fingerprint.

Cite this