Background: This article is based on this view that by using the technology of satellite media, any related risk that can have impact on children and adolescents, are come about; consequently, some actions should be taken to deal with these risks for taking them under control.
Objective: In order to prevent any kind of direct and/or indirect negative impacts of satellite media on children and adolescents, such as self-harm and suicide (Blue Whale Challenge Game as a social network phenomena) and cyberbullying, the objective of this article was to display a technical way through which it is possible to identify risks of satellite media that children and adolescents are exposed to them as well as reaching to a result for Iran.
Methods: Basically, the research done was a comparative study; therefore, in nature, it called for a qualitative research. So, a descriptive research along with case studies as well as an analytical study were applied too. Distributing questionnaires to get to the final result led to an exploratory research and consequently applying the Mann-Whitney U Test using the SPSS statistics concluded to a quantitative research. The final result can be applied by the related authorities to protect children and adolescents. Therefore this research can be considered as an applied research too.
Findings: By comparing five countries which were randomly selected from almost each continent along with Iran, focused on Tehran as a metropolitan counting different ethnic groups, risks of satellite media which have impacts on children and adolescents and were in common among all those countries, were identified by risk management techniques. Then the comparative and analytical studies and appraisals as well as statistical processes revealed that Iran’s current media approach and its performance towards children and adolescents are quite different from the other five countries that Iran was compared to.
Conclusion: A new technical way to approach risks of satellite media to control them for preventing children and adolescents from any kind of media risks plus a new result for Iran obtained. As an applied research the new result showed that an immediate action is required to regulate media standards for children and adolescents and also to modify the current situation of Iran’s media approach towards them. 

Background: Ethical sensitivity refers to knowledge and practice of ethical issues in a contradictory situation highlighted with self-awareness of one’s role and duty in those particular situations. According to ethics of care, the caregiver is committed to continuous learning and practicing the right options. The present study aimed to compare the moral sensitivity of midwifery and nursing students with that of nurses and midwives in Sabzevar University of Medical Sciences, Iran.
Materials and methods: In this descriptive cross-sectional study, participants were nursing students (n=60), 
midwifery students (n=50), nurses (n=100) and midwives (n=38), who were selected by convenience sampling. A validated Persian version of Han’s (2010) Moral Sensitivity Questionnaire (0-100) was used for data collection (α=0.97). Descriptive and analytic statistics were applied for data analysis.
Results: The participants’ mean scores of ethical sensitivity were as follows: midwifery students (90.82±1.47), nursing students (93.06±1.12), midwives (89.76±1.21), and nurses (89.67±1.47). Based on four regression models, significant differences were observed (level of confidence: 95%): midwifery students' mean moral sensitivity score was 6.16 less than that of nursing students; midwifery students' mean score was 4.58 higher than that of midwives; midwives’ mean score was 1.83 less than that of nurses; and nurses’ mean score was 3.01 less than that of nursing students.
Conclusion: The participants' moral sensitivity can be conceived as desirable. The significance of differences may be accounted for by factors such as their role and specialty. Taking measures to sustain and strengthen the moral sensitivity in all medical and healthcare groups are recommended.

Background: A study on the dengue daily counting in São Paulo city in a fixed period of time is assumed considering a new regression model approch.
Methods: Under a Bayesian approach, it is introduced a polynomial linear regression model in presence of some covariates which could affect the counts of dengue in São Paulo city considered in the logarithm scale, combined with existing stochastic volatility models usually assumed in financial data analysis. Markov Chain Monte Carlo methods are used to get the posterior summaries of interest.
Results: The new model approach showed some advantages when compared to other existing times series models usually used to model epidemics data.
Conclusion: The use of the polynomial regression model combined with existing volatility models under a Bayesian approach showed that it is possible to get very accurate fit for the counting dengue data in São Paulo city where it is possible to jointly model the means and volatilities (variances) of the epidemiological dengue time series.

Background: There has been a great interest in joint modeling of longitudinal and survival data in recent two decades. Joint models have less restrictive assumptions in multivariate modeling and could address various research questions. This has led to their wide applications in practice. However, earlier models had normality assumption on the distribution in longitudinal part that is usually violated in real data. Hence, recent research have focused on circumventing this issue. Using various skewed distributions has been proposed and applied in the literature. Nevertheless, the flexibility of the proposed methods is limited especially when the data are skew positive.
Methods: In this paper, we introduce the use of Birnbaum-Saunders (BS) distribution in joint modeling context. BS distribution is more flexible and could cover a wide range of skew, kurtotic or bimodal data.
Results: We analyzed publicly available ddI/ddC data both with normal and BS distributions in Bayesian setting and compared their fit by Widely Applicable Information Criterion (WAIC). The joint BS model showed a better fit to the data.
Conclusion: We introduced and applied BS distribution in joint modeling of longitudinal-survival data. Using multi-parameter distributions such as BS in Bayesian setting could improve the fit of models without limitations that arise in transformation of data from original scale. 

Background: In the last few years, statisticians have introduced new generated families of univariate distributions. These new generators are obtained by adding one or more extra shape parameters to the underlying distribution to get more flexibility in fitting data in different areas such as medical sciences, economics, finance and environmental sciences. The addition of parameter(s) has been proven useful in exploring tail properties and also for improving the goodness-of-fit of the family of distributions under study.
Methods: A new three parameter family of distributions was introduced by using the idea of T-X methodology. Some statistical properties of the new family were derived and studied.
Results: A new Topp Leone Kumaraswamy-G family of distributions was introduced. Two special sub-models, that is, the Topp Leone Kumaraswamy exponential distribution and Topp Leone Kumaraswamy  log-logistic distribution were investigated. Two real data sets were used to assess the flexibility of the sub-models.
Conclusion: The results suggest that the two sub-models performed better than their competitors.

Introduction: Stroke is the third most common cause of death in world after heart diseases and cancer. Due to the higher rate of stroke and less attention to its occurrence, assessing the prevalence of ischemic brain stroke and its clinical patterns across countries and nations such as Iran could be valuable and important.
Objectives: The aim of this study was to assess the epidemiological, Risk factor and clinical characteristics of ischemic stroke in Ardabil province.
Materials and Methods: This descriptive cross-sectional study has been done on 676 patients with ischemic stroke who referred to Ardabil city hospital during at year 2018. Data collected by using a checklist including demographic and clinical data of patients. Collected data were analyzed by statistical methods in SPSS version 21. The p-value less than 0.05 was considered as significant.
Results: Of all patients, 294 (43.5%) were female and rest of them were male. The mean age of patients was 69.3±13.2 years. Of all patients, 25.3% arrived to the hospital in less than 4.5 hours. The difference between arrival time to the hospital in rural and urban patients was significant. The most risk factors were seen in both sexes was HTN and in female only was DM and in male only was CVA. The results of CT and MRI showed that 30% and 64.9% of patients had lesion respectively that of them the most lesion in MRI was SMALL VESSEL and in CT was Middle Cerebral Artery (MCA).
Conclusion: Results showed that the patients who live in urban areas arrive to emergency sooner than rural patients. The significant relation was seen between AF, residence place and valvular with arriving time to hospital.

Background: Over the past years, there has been a great deal of interest in applying statistical machine learning methods to survival analysis. Ensemble-based methods, especially random survival forest, have been developed in various fields, especially medical sciences, due to their high accuracy and non-parametric nature and applicability in high-dimensional data sets. This paper aims to provide a methodological review and how to use random survival forests in the analysis of right-censored survival data.
Method: We present a review article based on the latest research in the PubMed database on random survival forest model methodology.
Results: This article begins with an introduction to tree-based methods, ensemble algorithms, and random forest (RF) method, followed by random survival forest framework, bootstrapped data and out-of-bag (OOB) ensemble estimators, review of performance evaluation indicators, how to select important variables, and other advanced topics of random survival forests for time-to-event data.
Conclusion: When analyzing right-censored survival data with high-dimensional data, while the relationships between variables are complex and their interactions are taken into account, the nonparametric random survival forest (RSF) method determines important variables affecting survival times with high accuracy and speed and also does not need to test the restrictive assumptions.

Context: Hepatitis C, as a major public health problem, has serious complications and drug users are the highest risk group for it.
Objectives: As the importance of this subject, the current study has been done to estimate the pooled prevalence  and distribution of hepatitis c virus in Iranian Drug User.
Evidence Acquisition: Articles were identified through international searching databases including PubMed, Scopus, Elsevier, Google Scholar and Web of Science and Iranian scientific information database (SID), Health. barakatkns, IranDoc, Civilica and MagIran. We reviewed systematically all studies reporting the prevalence of HCV Iranian Drug User.
Results: 227 records were identified by the electronic search, of which 62 studies were identified as relevant  papers which were meta-analyzed for the pooled HCV prevalence. Overall, prevalence of HCV  was 42.01 %( 36.83%-47.20%) in Iranian drug user.
Conclusion: Our meta-analysis study showed that HCV prevalence is high in drug users in Iran. With respect to the high prevalence of Hepatitis C among Drug User, ongoing preventive actions for this group are recommended.