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Disease mapping and regression with count data in the presence of overdispersion and spatial autocorrelation: a Bayesian model averaging approach

Mohebbi, Mohammadreza, Wolfe, Rory and Forbes, Andrew 2014, Disease mapping and regression with count data in the presence of overdispersion and spatial autocorrelation: a Bayesian model averaging approach, International journal of environmental research and public health, vol. 11, no. 1, pp. 883-902, doi: 10.3390/ijerph110100883.

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Title Disease mapping and regression with count data in the presence of overdispersion and spatial autocorrelation: a Bayesian model averaging approach
Author(s) Mohebbi, MohammadrezaORCID iD for Mohebbi, Mohammadreza orcid.org/0000-0001-9713-7211
Wolfe, Rory
Forbes, Andrew
Journal name International journal of environmental research and public health
Volume number 11
Issue number 1
Start page 883
End page 902
Total pages 10
Publisher Multidisciplinary Digital Publishing Institute (MDPI)
Place of publication Basel, Switzerland
Publication date 2014
ISSN 1661-7827
Keyword(s) Bayesian variable selection
cancer
disease mapping
ecologic studies
Gibbs sampling
spatial epidemiology
Summary This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present. These considerations lead to nine regression models derived from using three probability distributions for count data: Poisson, generalised Poisson and negative binomial, and three different autocorrelation structures. We employ the framework of Bayesian variable selection and a Gibbs sampling based technique to identify significant cancer risk factors. The framework deals with situations where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. The evidence from applying the modelling methodology suggests that modelling strategies based on the use of generalised Poisson and negative binomial with spatial autocorrelation work well and provide a robust basis for inference.
Language eng
DOI 10.3390/ijerph110100883
Field of Research 179999 Psychology and Cognitive Sciences not elsewhere classified
Socio Economic Objective 970117 Expanding Knowledge in Psychology and Cognitive Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2014, Multidisciplinary Digital Publishing Institute (MDPI)
Persistent URL http://hdl.handle.net/10536/DRO/DU:30066326

Document type: Journal Article
Collections: Faculty of Health
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.