title: Spatial cluster analysis of early stage breast cancer: a method for public health practice using cancer registry data.
abstract: OBJECTIVE
  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.
authors.affiliation: Graduate Program in Public Health, Department of Preventive Medicine, Stony Brook University Medical Center, Stony Brook, NY 11794-8338, USA. jrmeliker@gmail.com
authors.forename: Jaymie R
authors.initials: JR
authors.lastname: Meliker
authors.forename: Geoffrey M
authors.initials: GM
authors.lastname: Jacquez
authors.forename: Pierre
authors.initials: P
authors.lastname: Goovaerts
authors.forename: Glenn
authors.initials: G
authors.lastname: Copeland
authors.forename: May
authors.initials: M
authors.lastname: Yassine
mesh_terms: D001943:Breast Neoplasms / Q000453:epidemiology*; D002277:Carcinoma / Q000453:epidemiology*; D016000:Cluster Analysis; D003710:Demography; D005260:Female; D040362:Geographic Information Systems; D005843:Geography; D006801:Humans; D011159:Population Surveillance / Q000379:methods*; D012042:Registries