Context:Hepatitis C virus (HCV) is a global public health problem and a major etiology of chronic liver disease, which may develop into cirrhosis and hepatocellular carcinoma. Genotypes of HCV indicate the route of acquisition, the clinical outcome, response to treatment, prognosis and control strategies.Objectives:The aim of this study was to estimate the overall prevalence and trend of HCV genotypes or subtypes in IranData Sources:A literature review was done for papers reporting HCV genotypes in Iranian patients in PubMed, Magiran, IranMedex, Scientific Information Databank, and Google scholar databases.Study Selection:Data were selected according to inclusion and exclusion criteria.Data Extraction:Data were abstracted by two independent authors. Data were analyzed based on random-effects model using the Meta R. Pooled statistical software. Prevalence of HCV genotypes in cities and provinces of Iran with 95% confidence interval (CI) were calculated.Results:Fifty-three articles published between 1999 and 31 June 2014 including 22952 HCV infected individuals were included in the meta-analysis. Subtype 1a was predominant with a rate of 39% (95% CI: 34-44%); followed by subtype 3a, 32% (95% CI: 26-39%); subtype 1b, 13% (95% CI: 10-15%); genotype 4, 5.18% (95% CI: 3.27-7.5%); and genotype 2, 3.6% (95% CI: 1.6-8.3%). Untypeable HCV had a rate of 0.11% (95% CI: 0.07-0.16%).Conclusions:The most frequent subtypes of HCV in Iran were 1a, 3a and 1b, respectively. This frequency differed in various provinces of Iran and fluctuated with time. It is important to determine the distribution of HCV genotypes in different geographical areas and its trend with time for epidemiological and patients’ management purposes.

Background Although mammography use has increased in developed countries, regular screening in developing countries including Iran remains low. Multiple frameworks, including the Health Belief Model (HBM) and the Theory of Planned Behavior (TPB), have been used to understand screening practices among Iranians. The HBM includes intrapersonal constructs such as perceptions of breast cancer and mammography. The TPB includes interpersonal and environmental constructs, such as perceived control and subjective norms. Objectives The current study had 2 objectives: (1) to examine changes in the HBM and TPB constructs and repeat mammography screening in women receiving either intervention and women in the control group and (2) to compare changes in the HBM and TPB constructs and repeat mammography screening across the 2 interventions. Methods One hundred eight-four women from 3 randomly selected health centers in Sanandaj, Iran, participated. Eligibility criteria were being 50 years or older, having received a mammogram in the past 2 to 3 years, and no intention to obtain a mammogram within the next year. Results The TPB and HBM participants exhibited greater changes in the HBM and TPB constructs and were more likely to have a mammogram relative to control participants. The TPB and HBM participants exhibited comparable changes in constructs and repeat mammography. Conclusion Findings suggest both interventions equally improved mammography screening. Additional studies are furthermore warranted to address nonadherent Iranian women’s needs in line with these conceptual models. Implications for Practice Use of the HBM and TPB constructs in clinical practice may be helpful to promote continued screening among this population.

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Background and Objective:Cox model is a popular model in survival analysis, which assumes linearity of the covariate on the log hazard function, While continuous covariates can affect the hazard through more complicated nonlinear functional forms and therefore, Cox models with continuous covariates are prone to misspecification due to not fitting the correct functional form for continuous covariates. In this study, a smooth nonlinear covariate effect would be approximated by different spline functions.Material and Methods:We applied three flexible nonparametric smoothing techniques for nonlinear covariate effect in the Cox models: penalized splines, restricted cubic splines and natural splines. Akaike information criterion (AIC) and degrees of freedom were used to smoothing parameter selection in penalized splines model. The ability of nonparametric methods was evaluated to recover the true functional form of linear, quadratic and nonlinear functions, using different simulated sample sizes. Data analysis was carried out using R 2.11.0 software and significant levels were considered 0.05.Results:Based on AIC, the penalized spline method had consistently lower mean square error compared to others to selection of smoothed parameter. The same result was obtained with real data.Conclusion:Penalized spline smoothing method, with AIC to smoothing parameter selection, was more accurate in evaluate of relation between covariate and log hazard function than other methods.