Abstract
BackgroundSpatial 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.MethodsCancer 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.ResultsCluster 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.ConclusionAs 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.
Highlights
Spatial clustering of different diseases has received much less attention than single disease mapping
Cluster analysis of non-smoothed standardized incidence ratios (SIRs) showed only oral cavity and larynx cancers clustered at r = 0.8
D(Fbieogntutdroremog1)rraemspsehcotwiveinlyg the relationships among the thirteen cancer sites based on BYM modeling and Poisson kriging Dendrogram showing the relationships among the thirteen cancer sites based on BYM modeling and Poisson kriging respectively
Summary
Spatial clustering of different diseases has received much less attention than single disease mapping. A descriptive study of cancer incidence and mortality by municipality was conducted using data from the regional population cancer registry (RTUP) and from the regional nominative cause of death registry (ReNCaM) [1,2]. Since cancer data were aggregated at the municipal level, variability due to small areas hampered interpretation of observed SIRs in terms of underlying local cancer risks [3]. The widely used Besag, York, and Mollie spatial analysis method was adopted to produce regional maps by gender and cancer site [4]. These studies provided evidence of marked intra-regional variability in cancer distribution but did not analyze the incidence of diverse cancers simultaneously
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