Showing 3 results for Relative Risk
A Akbarzadeh Bagheban, A Beaji, Y Mehrabi, H Saadat,
Volume 5, Issue 3 (12-2009)
Abstract
Background and objective: Numerous studies have reported beneficial effects of smoking cessation in terms of decreased cardiovascular mortality in patients with coronary heart disease. This paper aimed to determine a valid estimate for the relative risk of mortality in subjects who quit smoking compared to those continued smoking.
Methods: All relevant prospective cohort studies of chronic heart disease published during 1975 to 2008 were considered. Studies with at least two years follow-up were eligible for analysis. The qualities of studies were assessed independently by two reviewers. In addition, to obtain a precise estimate, we used the sample size and the follow-up duration of each study as the covariates in the Bayesian meta-analysis model. The Winbugs and Boa softwares were utilized for fitting the Bayesian meta-analysis model.
Results: The estimate of relative risk of mortality for those who quit smoking compared to those continued smoking was 0.64 (95%CI: 0.57-0.70). We also did not find any significant relationship between the estimate of risk reduction and the described covariates.
Conclusions: Using this Bayesian meta-analysis, a 36% reduction in relative risk of mortality was found for those who quit smoking compared to those continued smoking, after eliminating the effects of study sample size and follow-up duration.
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.
A Hadianfar, S Rastaghi, A Saki,
Volume 16, Issue 5 (3-2021)
Abstract
Background and Objectives: The Covid-19 epidemic began in Wuhan, China in the late 2019 and became a global epidemic in March 2020. In this regard, one of the most important indicators of the healthcare systems is the in-hospital mortality rate, which occurs with a time lag of one to two weeks after hospitalization. The aim of this study was to investigate the relative risk of Covid-19 mortality considering this time lag according to the number of daily hospitalizations.
Methods: The data included the number of daily hospitalizations and deaths from Covid-19 from 15 May 2020 to 10 February 2021 in Iran, which was obtained from the Github database. A log-linear distributed lag model was used to evaluate the relationship and lag effect between daily hospitalization and relative risk of death.
Results: The mean number of daily hospitalizations and deaths were 1342.2 ± 7 731.5 and 190.6 11±118.6 in the study period, respectively. It was found that an increase in the number of daily hospitalizations had a significant relationship with an increase in the relative risk of death on the same day and in the following days. As the number of hospitalizations exceeded 2000 patients per day, the cumulative relative risk of death increased to more than one.
Conclusion: The results showed that the number of hospitalizations exceeding 2000 people per day was an alert for the country's healthcare system. Overall, prevention and observance of health protocols in the first level followed by early diagnosis of the disease, improving the hospitals facilities and preparedness of healthcare staff can reduce the relative risk of death in the possible future peaks.
