Showing 2 results for Risk Map
A Mirzazadeh, B Hajarizadeh, B Mesgarpour, A Golozar, K Holakouie Naieni,
Volume 4, Issue 3 (3-2009)
Abstract
Background & Objectives: Recent reports indicated an increase in cutaneous Leishmaniosis (CL) cases. We designed the study in the context of community assessment process to identify and address the major public health related issues by explore the risk map of CL and assessing the environmental risk factors in Kerman.
Methods: All the registered CL in the only referral center for CL from 2002 to 2006, localized on Kerman digital map. The level of data dissemination was townships. Based on data from the national statistics organization, we determined the population and calculated the incidence of CL of each township. Secondly, the highest endemic townships were observed deeply with a specific checklist to determine the environmental risk factors.
Results: 771 cases were included. All the high endemic areas were located in the east part of Kerman. The eastern township, Sarasiyab, with 123 (15.9%) cases was the most infected region. The highest endemic townships were Sarasiyab, Emam and Sarbaz with 54.9, 52.8 and 51.2 cases per 10,000, respectively. Some minor endemic areas such as Shahab, Abouzar and Shahzadeh Mohammad (South and central regions) were going to be disappeared while Shariati, Naseriyeh-Seyedi (North and North-East regions) were the new high-risk townships (P<0.01).
Conclusions: the east and central part of Kerman, were always the high endemic regions. Some other new high-risk areas were also detected. The most environmental factors were the bare lands between the houses, ground passages and the timeworn architecture on the buildings.
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.
