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Permanent link (DOI): https://doi.org/10.7939/R3BC3T019

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An examination of five spatial disease clustering methodologies for the identification of childhood cancer clusters in Alberta, Canada Open Access

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Author or creator
Torabi, Mahmoud
Rosychuk, Rhonda J.
Additional contributors
Subject/Keyword
Bayesian statistic
cancer cases
geographic epidemiology
spatial cluster detection
Type of item
Journal Article (Published)
Language
English
Place
Time
Description
Cluster detection is an important part of spatial epidemiology because it may help suggest potential factors associated with disease and thus, guide further investigation of the nature of diseases. Many different methods have been proposed to test for disease clusters. In this paper, we study five popular methods for detecting spatial clusters. These methods are Besag- Newell (BN), circular spatial scan statistic (CSS), flexible spatial scan statistic (FSS), Tango’s maximized excess events test (MEET), and Bayesian disease mapping (BYM). We study these five different methods by analyzing a data set of malignant cancer diagnoses in children in the province of Alberta, Canada during 1983-2004. Our results show that the potential clusters are located in the south-central part of the province. Although, all methods performed very well to detect clusters, the BN and MEET methods identified local as well as general clusters.
Date created
2011
DOI
doi:10.7939/R3BC3T019
License information
Creative Commons Attribution-Non-Commercial-No Derivatives 3.0 Unported
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Citation for previous publication
Torabi M, Rosychuk RJ (2011). An Examination of Five Spatial Disease Clustering Methodologies for the Identification of Childhood Cancer Clusters in Alberta, Canada. Spatial and Spatio-temporal Epidemiology, 2(4), 321-330.
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