199646 Spatial cluster analysis of early stage breast cancer: A method for public health practice using cancer registry data

Tuesday, November 10, 2009: 10:50 AM

Jaymie R. Meliker, PhD , Graduate Program in Public Health, Stony Brook University Medical Center, Stony Brook, NY
Geoffrey Jacquez, PhD , BioMedware, Inc., Ann Arbor, MI
Pierre Goovaerts, PhD , BioMedware, Inc., Ann Arbor, MI
Glenn Copeland, MBA , Division for Vital Records and Health Statistics, Michigan Department of Community Health, Lansing, MI
May Yassine , Cancer Control Services Program, Okemos, MI
Objectives: Cancer registries are increasingly mapping residences of patients at time of diagnosis, however, an accepted protocol for spatial analysis of these data is lacking. We undertook a public health practice-research partnership to develop a strategy for detecting spatial clusters of early stage breast cancer using registry data. Methods: Spatial patterns of early stage breast cancer throughout Michigan were analyzed comparing several scales of spatial support, and different clustering algorithms. Results: Analyses relying on point data identified spatial clusters not detected using data aggregated into census block groups, census tracts, or legislative districts. Further, using point data, Cuzick-Edwards' nearest neighbor test identified clusters not detected by the SaTScan spatial scan statistic. Regression and simulation analyses lent credibility to these findings. Conclusions: In these cluster analyses of early stage breast cancer in Michigan, spatial analyses of point data are more sensitive than analyses relying on data aggregated into polygons, and the Cuzick-Edwards' test is more sensitive than the SaTScan spatial scan statistic, with acceptable Type I error. Cuzick-Edwards' test also enables presentation of results in a manner easily communicated to public health practitioners. The approach outlined here should help cancer registries conduct and communicate results of geographic analyses.

Learning Objectives:
Explain why spatial cluster analysis is useful with state cancer registries. Compare analytic results using different scales of spatial support. Identify a strategy for spatial cluster analysis of cancer registry data.

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: Designed, led analyses, and led write-up of analyses.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.