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Cancer incidence in men: a cluster analysis of spatial patterns

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...

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Autores principales: Cassetti, Tiziana, La Rosa, Francesco, Rossi, Luca, D'Alò, Daniela, Stracci, Fabrizio
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2628926/
https://www.ncbi.nlm.nih.gov/pubmed/19032769
http://dx.doi.org/10.1186/1471-2407-8-344
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author Cassetti, Tiziana
La Rosa, Francesco
Rossi, Luca
D'Alò, Daniela
Stracci, Fabrizio
author_facet Cassetti, Tiziana
La Rosa, Francesco
Rossi, Luca
D'Alò, Daniela
Stracci, Fabrizio
author_sort Cassetti, Tiziana
collection PubMed
description 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. CONCLUSION: 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.
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spelling pubmed-26289262009-01-21 Cancer incidence in men: a cluster analysis of spatial patterns Cassetti, Tiziana La Rosa, Francesco Rossi, Luca D'Alò, Daniela Stracci, Fabrizio BMC Cancer Research Article 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. CONCLUSION: 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. BioMed Central 2008-11-25 /pmc/articles/PMC2628926/ /pubmed/19032769 http://dx.doi.org/10.1186/1471-2407-8-344 Text en Copyright © 2008 Cassetti et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Cassetti, Tiziana
La Rosa, Francesco
Rossi, Luca
D'Alò, Daniela
Stracci, Fabrizio
Cancer incidence in men: a cluster analysis of spatial patterns
title Cancer incidence in men: a cluster analysis of spatial patterns
title_full Cancer incidence in men: a cluster analysis of spatial patterns
title_fullStr Cancer incidence in men: a cluster analysis of spatial patterns
title_full_unstemmed Cancer incidence in men: a cluster analysis of spatial patterns
title_short Cancer incidence in men: a cluster analysis of spatial patterns
title_sort cancer incidence in men: a cluster analysis of spatial patterns
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2628926/
https://www.ncbi.nlm.nih.gov/pubmed/19032769
http://dx.doi.org/10.1186/1471-2407-8-344
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