Y Mehrabi, E Maraghi, H Alavi Majd, Me Motlagh,
Volume 6, Issue 3 (12-2010)
Abstract
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.
Am Mosadeghrad, A Pour Reza , N Abolhasan Beigi Galezan , Sh Shahebrahimi,
Volume 14, Issue 4 (3-2019)
Abstract
Background and Objectives: Human Development Index (HDI) is an important indicator of a country’s development. On the other hand, mortality indicators are the most important indicators of the health of a society. This study aimed to examine the association between HDI and maternal, neonatal, infant, and under-five mortality rates in Iran between 2005 and 2016.
Methods: This longitudinal study was conducted using data collected from Iran Statistics Center, World Health Organization, and United Nations Development Program. SPSS software version 22 was used for data analysis. Pearson correlation test was applied to examine the correlation between HDI and mortality rates. Regression analysis was used to measure the effect of HDI on mortality rates.
Results: HDI increased from 0.690 in 2005 to 0.774 in 2016 (12% rise). Maternal, neonatal, infant, and under-five mortality rates decreased by 26, 41, 52, and 42% in 2016 compared to 2005, respectively. HDI had a significant indirect association with maternal (-0.973), neonatal (-0.983), infant (-0.739), and under-five mortality (-0.987). An increase of 0.01 in HDI reduced 1 maternal death per 100,000 births. An increase of 0.014, 0.009, and 0.008 in HDI decreased one neonatal, infant, and under-five death per 1000 births.
Conclusion: The results showed that increased HDI correlated with decreased mortality rates. Therefore, policy-makers should pay more attention to socio
