Applying and comparing empirical and full Bayesian models in study of evaluating relative risk of suicide among counties of Ilam province.

Abstract

Introduction:Disease mapping includes a set of statistical techniques that provides maps based on estimates of diseases rates. Bayesian ones are the most important models in this field. They consider prior information on changes in the disease rates in 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 Bayesian models in the study of evaluating relative risk of suicide in Ilam province.Materials and Methods:In this applied-ecological research, suicide incidence in Ilam province in 2008 and 2009 was analyzed by use of Gamma-Poisson, Log-normal, and BYM Bayesian models. Models were fitted to data using WinBUGS software.Results:Fitting the three models showed that Darehshahr and Shirvan-Chrdavol had the highest and the lowest relative risk of suicide, respectively (relative risks based on Gamma-Poisson, Log-normal, and BYM models were 2.243, 2.275, and 2.279 for Dareshahr and 0.321, 0.321, and 0.319 for Shirvan-Chrdavol, respectively).Conclusion:Despite some differences in estimates, the ranks of relative risks in counties in all three models are the same. The counties based on the relative risks of suicide from the most to the least are: Darehshahr, Ilam, Dehloran, Eyvan, Abdanan, Mehran, Malekshahi, and Shirvan-Chrdavol.