Showing 7 results for Methods
Z Pouransary , Z Sheikh , B Eshrati , P Kamali ,
Volume 3, Issue 3 (2-2008)
Abstract
Background & Objectives: Men (husbands) play a very important role in family planning programs, especially in developing countries. The objective of this study was to determine the prevalence of different methods of contraception in women of reproductive age in Iranshahr in 2004-2005 and the extent of their husbands' participation in family planning.
Methods: This was a cross-sectional study focused on women who were married but not pregnant at the time of the research. We used multi-stage cluster sampling and a pre-tested questionnaire to record the method of contraception and to see if the husband was actually participating in family planning. We used the SPSS (13th version) software to calculated measures of location and dispersion.
Results: The total sample of 700 married women in the (10-49y) age group included 400 subjects in rural areas and 300 in cities. Of these, 696 agreed to take part in the research. Overall, 65.5% of these women were using at least one form of contraception the remaining 34.5% did not use any contraceptive methods. The percentage of unwanted pregnancies was estimated at 16%. The mean number of pregnancies was 7, with a median of 4.5. Sixty-three percent of the subjects reported good cooperation by their husbands. Logistic regression analysis showed that the probability of using contraception was significantly related to the husband's cooperation.
Conclusions: Our results underline the importance of men's support and cooperation in the success of family planning efforts.
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.
Aa Haghdoost, Mr Baneshi, M Marzban,
Volume 7, Issue 2 (9-2011)
Abstract
In the previous paper, the basic concepts of sample size calculation were presented. This paper explores main post-calculation adjustments of the sample size calculation in special circumstances such as multiple group comparisons, unbalanced studies (with unequal number of subjects in different groups) sample size correction for missing data, and adjustment for finite population size. In addition, the concept of design effect in multi-stage sampling
M Shokouhi, E Mohebbi, A Rastegari, S Hajimaghsoudi, Aa Haghdoost, Mr Baneshi,
Volume 10, Issue 1 (6-2014)
Abstract
Knowing the population size of rare diseases or special subpopulations like injection drug users (IDUs) is one of the most important challenges in public health and health surveillance systems but it is difficult to estimate these groups. During the last few years, new methods have been suggested to estimate hidden or hard-to-reach populations, one of which is the network scale-up method (NSUM). The NSUM itself includes measuring the personal network size and estimating the prevalence of hidden and hard-to-count populations. In this paper, we basically discussed the indirect methods of calculating the population size, and the history of NSUM and its concepts, and then addressed the estimation of hidden populations with NSUM and the applicable notes for such populations.
M Vameghi, M Dejman, H Rafiey, P Roshanfekr, As Forouzan, Ar Shoghli, A Mirzazadeh,
Volume 11, Issue 1 (6-2015)
Abstract
Background & Objectives : Children who work or live on the street are one of key populations at risk for HIV and hard to reach for study or providing services. Here, as a methodological paper, we present the methods and steps of a rapid assessment and response (RARE) project conducted among street children in Tehran.
Methods : We applied a mixture of qualitative (literature review, focus group discussion, in-depth interview) and quantitative (structure interview) methods to collect data from key informants in non-governmental and governmental agencies as well as street children. We applied targeted sampling to recruit key informants and time location sampling to recruit street children.
Results: The study was conducted in eight steps to recognize the involved stakeholders, define target population (street children) and to guide on how to approach them (steps 1 to 2: Initial Consultation, Study Area Profile), to map the venues and gathering spots and ethnographic findings crucial for further recruitment into surveys (steps 3 to 5: Contextual Assessment, Population and Setting Assessment, Health Issues Assessment), assess the risk behaviors (step 6: Assessment Behavior Risk and Health), evaluate the response (step 7: Intervention Assessment), and develop an operational plan for improving the services (step 8: Developing an Action Plan).
Conclusion: RARE is a participatory mix research method with sufficient flexibility to study complex health problems such as stigmatized HIV risk behaviors among high risk and hard to reach populations and also to assess the health sector response.
M Saadati, A Bagheri,
Volume 12, Issue 2 (8-2016)
Abstract
Sampling hidden populations is challenging due to the lack of convenience statistical frames. Since most populations exposed to special diseases are hidden and hard to reach, sampling methods that produce representative and efficient samples from the populations have become a study subject for researches all over the world. Because of the unknown probability of selecting samples in conventional sampling methods and also invalidity of generalizing the results of non-probability sampling methods to the statistical population, the necessity of introducing probability chain-referral sampling methods, such as the respondent driven sampling method becomes imperative. In this article, besides introducing the respondent driven sampling method, some of the advantages of this method as relative decrease of the bias of estimates, declining the non-response rate by paying incentives and allocating weights proportional to reciprocal of the social network size of respondents to produce unbiased estimates are described. Moreover, some disadvantages of this method such as lack of producing differential samples by selecting similar seeds, lack of reaching more efficient method than snowball sampling by implementing this method improperly and lack of achieving to equilibrium by existing weak social networks among members of interested population are stated. Another aim of this article is to compare sampling methods of hidden population with the respondent driven sampling method which are the results of implementing this method in different surveys and existing simulations.
R Ali Akbari Khoei, E Bakhshi, A Azarkeivan, A Biglarian,
Volume 12, Issue 3 (10-2016)
Abstract
Background and Objectives: A small sample size can influence the results of statistical analysis. A reduction in the sample size may happen due to different reasons, such as loss of information, i.e. existing missing value in some variables. This study aimed to apply bootstrap and jackknife resampling methods in survival analysis of thalassemia major patients.
Methods: In this historical cohort study, the data of 296 patients with thalassemia major who were visited at Zafar Clinic, Tehran, from 1994 to 2013 were used. Parametric survival models were used to analyze the data. The log – normal survival model was selected as the best model and then the bootstrap and jackknife resampling algorithms were used for this model. Data analysis was carried out with the STATA 12.0 software.
Results: The results of the resampling methods showed that standard errors decreased and confidence intervals were shortened. In addition, the result of the bootstrap and jackknife resampling methods showed that age group and the relationship of the parents (P<0.001) were significant compared with the log-normal model (P>0.900).
Conclusion: Comparison of the confidence intervals suggests that the jackknife resampling method can be used when the sample size is small.
