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Open AccessArticle

Disease Mapping and Regression with Count Data in the Presence of Overdispersion and Spatial Autocorrelation: A Bayesian Model Averaging Approach

1
Biostatistics Unit, Faculty of Health, Deakin University, Melbourne 3125, Australia
2
Department of Epidemiology and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne 3000, Australia
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2014, 11(1), 883-902; https://doi.org/10.3390/ijerph110100883
Received: 7 December 2013 / Revised: 4 January 2014 / Accepted: 6 January 2014 / Published: 9 January 2014
(This article belongs to the Special Issue Spatial Epidemiology)
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. View Full-Text
Keywords: Bayesian variable selection; cancer; disease mapping; ecologic studies; Gibbs sampling; spatial epidemiology Bayesian variable selection; cancer; disease mapping; ecologic studies; Gibbs sampling; spatial epidemiology
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MDPI and ACS Style

Mohebbi, M.; Wolfe, R.; Forbes, A. Disease Mapping and Regression with Count Data in the Presence of Overdispersion and Spatial Autocorrelation: A Bayesian Model Averaging Approach. Int. J. Environ. Res. Public Health 2014, 11, 883-902.

AMA Style

Mohebbi M, Wolfe R, Forbes A. 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. 2014; 11(1):883-902.

Chicago/Turabian Style

Mohebbi, Mohammadreza; Wolfe, Rory; Forbes, Andrew. 2014. "Disease Mapping and Regression with Count Data in the Presence of Overdispersion and Spatial Autocorrelation: A Bayesian Model Averaging Approach" Int. J. Environ. Res. Public Health 11, no. 1: 883-902.

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