Iranian Journal of

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Background and objective: Disease or mortality mapping are statistical methods aimed at providing precise estimates of rates across geographical maps. The aim of this research is to improve the precision of relative risk (RR) estimates of infant mortality (IM) for different rural areas, using empirical and full Bayesian methods.
Methods: Infant mortality data were extracted from the vital horoscope (Zij-Hayati) for years 2001 and 2006 across rural areas of Iran. Maximum Likelihood, Empirical Bayes with Poisson-Gamma model and full Bayesian models were used. Mont Carlo Markov Chain method was used for latter models. Deviance information criterion (DIC) was computed to check the models fittings. R, WinBUGS and Arc GIS software were employed.
Results: Based on the full Bayesian method, the highest RR of infant mortality was 1.73 (95%CI: 1.58-1.88) in year 2001 and 1.62 (95%CI: 1.50-1.75) in 2006 which belonged to Sistan-va-Blouchestan area in comparison to the whole country. In 2001, the rural areas of Birjand (1.45), Kordistan (1.23) and Khorasan (1.21) and in 2006, Birjand (1.42), Zanjan (1.39), Kordistan (1.36), Ardebil (1.32), Zabol (1.28), West Azerbaijan (1.18) and finally Golestan (1.14) had significant RR of IM (all p<0.05). The lowest RR of infant mortality for year 2001 were belong to rural areas of Tehran University (0.56) and for year 2006 to former Iran University (0.52).
Conclusion: To estimate the mortality map parameters, the full Bayesian method is preferred compared to empirical Bayes and maximum likelihood.

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Background & Objectives: The Cox proportional-hazards regression and other parametric models model have achieved widespread use in the analysis of time-to-event data with censoring and covariates. However employing Bayesian method has not been widely used or discussed. The aim of this study was to evaluate the prognostic factors in using Bayesian interval censoring analysis.
Methods: This cohort study was based on 178 patients with gastric cancer from January 2003 to December 2007 admitted to Taleghani teaching hospital in Tehran. Known prognostic risk factors were entered into the analysis using Bayesian Weibull and Exponential models. The term DIC was employed to find best model.
Results: The results were showed survival rate depended on age of diagnosis and tumor size. Those patients who had early diagnosis and/or had smaller tumor size were in lower risk of death.
Conclusion: The age of diagnosis and tumor size of patients are important prognostic factors related to survival of patients with gastric cancer. Based on DIC, Bayesian analysis of the Weilbull model performed better than the Exponential model. As a result, if this cancer has been diagnosed early, the relative risk of death would reduce.

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  Background and Objectives : Previous studies have reported contradictory results regarding the association of Shift Work (SW) and Blood Cholesterol (BC). In this paper, we studied the relationship between SW and BC.

  Methods: The data of this historical cohort study was extracted from annual observations of the workers of Esfahan’s Mobarakeh Steel Company selected through cluster random sampling between 1996 and 2011. In this research, we assessed the effect of SW on BC with controlling BMI, age, work experience, marital status, smoking, and educational status.

  Results : Five hundered and seventy four male workers participated in this study with a mean (SD) age of 41.89 (7.51) and mean (SD) work experience of 16.75 (7.16) years. In this study, after controlling confounding factors, we found no significant relationship between SW and BC.

  Conclusion: Because our study showed no relationship between SW and BC, we can state that this relationship does not exist with more certainty.