title: Cancer incidence in men: a cluster analysis of spatial patterns.
abstract: BACKGROUND
  Spatial clustering of different diseases has received much less attention than single disease mapping. Besides chance or artifact, clustering of different cancers in a given area may depend on exposure to a shared risk factor or to multiple correlated factors (e.g. cigarette smoking and obesity in a deprived area). Models developed so far to investigate co-occurrence of diseases are not well-suited for analyzing many cancers simultaneously. In this paper we propose a simple two-step exploratory method for screening clusters of different cancers in a population.
  
  
  METHODS
  Cancer incidence data were derived from the regional cancer registry of Umbria, Italy. A cluster analysis was performed on smoothed and non-smoothed standardized incidence ratios (SIRs) of the 13 most frequent cancers in males. The Besag, York and Mollie model (BYM) and Poisson kriging were used to produce smoothed SIRs.
  
  
  RESULTS
  Cluster analysis on non-smoothed SIRs was poorly informative in terms of clustering of different cancers, as only larynx and oral cavity were grouped, and of characteristic patterns of cancer incidence in specific geographical areas. On the other hand BYM and Poisson kriging gave similar results, showing cancers of the oral cavity, larynx, esophagus, stomach and liver formed a main cluster. Lung and urinary bladder cancers clustered together but not with the cancers mentioned above. Both methods, particularly the BYM model, identified distinct geographic clusters of adjacent areas.
  
  
  CONCLUSIONS
  As in single disease mapping, non-smoothed SIRs do not provide reliable estimates of cancer risks because of small area variability. The BYM model produces smooth risk surfaces which, when entered into a cluster analysis, identify well-defined geographical clusters of adjacent areas. It probably enhances or amplifies the signal arising from exposure of more areas (statistical units) to shared risk factors that are associated with different cancers. In Umbria the main clusters were characterized by high risks for cancers with alcohol and tobacco both as risk factors. Tobacco-only related cancers formed a separate cluster to the alcohol- and tobacco-related sites. Joint spatial analysis or investigation of hypothesized exposures might be used for further investigation into interesting geographical clusters.
authors.affiliation: Umbrian Population Cancer Registry, Department Medical-surgical Specialties and Public Health, Public Health Section, University of Perugia, Italy. tiziana.cassetti@unipg.it
authors.forename: Tiziana
authors.initials: T
authors.lastname: Cassetti
authors.forename: Francesco
authors.initials: F
authors.lastname: La Rosa
authors.forename: Luca
authors.initials: L
authors.lastname: Rossi
authors.forename: Daniela
authors.initials: D
authors.lastname: D'Alò
authors.forename: Fabrizio
authors.initials: F
authors.lastname: Stracci
mesh_terms: D000428:Alcohol Drinking; D016000:Cluster Analysis; D004938:Esophageal Neoplasms / Q000453:epidemiology; D006801:Humans; D015994:Incidence; D007558:Italy / Q000453:epidemiology; D007822:Laryngeal Neoplasms / Q000453:epidemiology; D008113:Liver Neoplasms / Q000453:epidemiology; D008175:Lung Neoplasms / Q000453:epidemiology; D008297:Male; D015233:Models, Statistical; D009062:Mouth Neoplasms / Q000453:epidemiology; D009369:Neoplasms / Q000453:epidemiology*; D010610:Pharyngeal Neoplasms / Q000453:epidemiology; D012307:Risk Factors; D013274:Stomach Neoplasms / Q000453:epidemiology; D013964:Thyroid Neoplasms / Q000453:epidemiology; D001749:Urinary Bladder Neoplasms / Q000453:epidemiology; D014026:Nicotiana