ABSTRACT
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. 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. 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]. To estimate the mortality map parameters, the full Bayesian method is preferred compared to empirical Bayes and maximum likelihood