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Title

APPLYING GAMMA-POISSON, LOGNORMAL, AND BYM MODELS IN COMPARING RELATIVE RISK OF SUICIDE AMONG PROVINCES OF ILAM, IRAN

Pages

 Start Page 86 | End Page 95

Abstract

 Background: DISEASE MAPPING includes a set of statistical techniques that provide detailed maps of rates based on estimated incidence, prevalence, and mortality. Bayesian models are the most important models in this field. They consider prior information on changes in the disease rates in the overall map and spatial pattern of the disease. These include a broad range of models with their own formulation, characteristics, strengths, and weaknesses. In the present study we explain and compare three important and widely-used models (Gamma-Poisson and lognormal as empirical Bayesian models, and Besag, York and Mollie (BYM) as a full Bayesian model) with regard to the RELATIVE RISK of SUICIDE in the Ilam Province, Iran.Methods: In this applied, ecological research, SUICIDE information of the Ilam Province for 2007 and first half of 2008 was analyzed using Gamma-Poisson, lognormal, and BYM Bayesian models. Models were fitted to data using WinBUGS software.Findings: Fitting the three models showed that Darrehshahr and Shirvan-Chrdavol had the highest and the lowest RELATIVE RISK of SUICIDE, respectively.Conclusion: Despite some differences in estimates, the ranks of RELATIVE RISKs in all three models are similar for all provinces. This result was in accordance with the results of the study by Clayton and Kaldor. The provinces from the highest to lowest RELATIVE RISK of SUICIDE are: Darrehshahr, Ilam, Dehloran, Eyvan, Abdanan, Mehran, Malekshahi, and Shirvan-Chrdavol.

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