مجله اپیدمیولوژی ایران
سال چهاردهم شماره 3 (پیاپی 51، پاییز 1397)

  The sustainable development goals include 17 goals, one of which is exclusively related to health. The aim of this study was to assess the status of indicators of sustainable development goals related to health in Iran compared to countries in the region and the WHO countries.

Secondary data were used to examine the status of indicators of sustainable development goals related to health. The data reported by the World Health Organization in 2017, the World Bank, and other available resources at the international levels such as the UN and UNICEF were addressed. The indicators according to which the situation in Iran was below the average of the region were selected for further exploration and analysis. A checklist, which met the research objectives, was used for data collection.

The status of the maternal mortality ratio (25 in 100,000 people), incidence of tuberculosis) 16 in 100,000 people), and incidence of malaria (0.5 in 1000 people) was favorable. The indicators with an unfavorable status in Iran compared to the regional average were the mortality due to natural disasters, unintentional poisoning, road traffic injuries, and alcohol consumption per capita.

Iran has achieved many SDGs goals; however, there is a need for carefully designed plans for controlling and reducing some indicators. Prioritizing indicators in which Iran has had little success should be taken seriously by decision makers.

  Human errors are the most important factors for RTIs, which have been addressed in few quantitative studies. The objective of this study was to calculate the annual population attributable fraction (PAF) of the most important human risk factor for RTIs in the streets and highways of the Great Tehran.

In this study, the data of 124518 individuals registered in the COM114 form of the Police Information and Communication Technology (ICT- FAVA) data system of the country were employed in 2014. A regression model was applied to estimate the magnitude of the impact of traffic violations of drivers with RTIs using the adjusted relative risk ratio. To estimate the PAF, the prevalence and effect size were calculated based on the risk ratio (RR) using the baseline method or the Miettinen formula.

The PAF of driving in reverse gear and failure to observe safe distance was 1.98% and 1.93%, respectively. Driving in the reverse gear and failure to observe the safe distance increased RTIs by 22% and 23%, respectively. On the highways of the Great Tehran, the largest proportion of PAF was related to driving in reverse gear (1.96%), sudden movement of the car (1.90%), speeding (1.87%). Speeding increased the risk of RTIs by 23%.

Monitoring traffic violations can decrease road crashes by 10% in the streets. Moreover, on highways, use of strategies to prevent traffic violations can decrease RTIs by up to 15%.

Hepatitis B virus (HBV) and hepatitis C virus (HCV) co-infections could affect the prognosis of disease in patients with human immunodeficiency virus (HIV)/AIDS. We aimed to determine HBV/HCV co-infection in HIV-positive individuals; and to compare the characteristics of different subgroups of HIV/HBV/HCV.

In this cross-sectional study, 908 of 2134 newly-registered HIV-positive individuals in the Iranian Ministry of Health in 2016 were recruited. Participants were divided into four subgroups (HBV-/HCV-, HBV+/HCV-, HBV-/HCV+, and HBV+/HCV+). Demographic data and HIV transmission routes were compared between subgroups.

Among908 participants, 505 (60.6%) were HBV-/HCV-, 20 (2.2%) were HBV+/HCV-, 318 (35%) were HBV-/HCV+, and 20 (2.2%) were HBV+/HCV+. Mean age of participants was 36.9 years; 67% were male and 47% were married. The commonest high-risk behaviors were unsafe sex (43.5%) and IV drug-abuse (34.4%). There was no statistically significant difference between the mean ages of subgroups. All HBV+/HCV+ ,92% of HBV-/HCV+, 50% of HBV+/HCV- and 52% of HBV-/HCV- were men and the difference between groups was statistically significant (p-value: <0.001). Seventy four percent of HBV-/HCV+ and 80% of HBV+/HCV+ reported IV drug-abuse. Thirty percent of HBV-/HCV- and 40% of HBV+/HCV- were spouse of HIV-positive or high-risk individuals.

More than 30% of newly registered HIV-positive individuals in the Iranian Ministry of Health in 2016 were co-infected with either HBV or HCV. Frequency of men, marital status and high-risk behaviors were different in various subgroups.

  Academic failure is of paramount importance for medical students because it might lead to a decline in scientific level of the community of physicians in the future. This study was conducted to investigate the predictors of academic failure in medical students of Lorestan University of Medical Sciences using classification tree.

In this cohort study, academic records of all medical students of Lorestan University of Medical Sciences during the academic years of 1999-2008 were selected by census and were followed up until September 2016. Academic failure was defined as having at least one of the components of appropriate grade point average, prolonged graduation, academic probation, dropout, expulsion, and any failure in ccomprehensive exams and the CART classification tree was adopted using the SPSS 22 software to predict it.

The cumulative incidence of academic failure was 26.4% and the most prevalent components were prolonged graduation (21.7%) and academic probation (15.0%). The probability of academic failure was 0.449 in subjects taking guest courses, 0.220 in subjects with no history of guest courses admitted to courses with less than 40 students and admission quotas of zone 1 or 3, and 0.456 in subjects with no history of guest courses admitted to courses with more than 40 students and males.

With respect to identifying the predictors of academic failure, it is suggested that these students be referred to consulting centers of the university or educational supervisors’ moreover, the regulations of taking guest courses in other universities should be revised.

  Accidents are one of the global concerns in public health. About 1.24 million people die in traffic accidents annually. Road accidents are the leading cause of years of life lost in Iran and motorcyclists are the riskiest group of road users. Drug abuse is one of the main causes of road traffic accidents and motorcycling is more popular in drug abusers. So, this study was conducted to compare the riding behavior of normal and drug abuser motorcyclists.

This historical cohort was performed in 411 motorcyclists in 3 study groups: 100 methadone consumer motorcyclists, 100 methamphetamine consumer motorcyclists, and 211 normal motorcyclists. The participants in three groups were matched for age and area of residence. A motorcycle riding behavior questionnaire was used to collect the data. Then, the collected data were analyzed with the SPSS (V-20) software using descriptive and analytic statistics. Multivariate linear regression was applied for statistical analysis.

There was a significant difference in the riding behavior score between normal motorcyclists and methamphetamine user and methadone user motorcyclists (P<0.0001), but the difference between drug user motorcyclists was not significant (P=0.292). The most frequent violation in all three groups was speeding.

the results showed speeding was the most frequent violation in all three groups followed by traffic errors. It was found that drug abuse (methamphetamine and methadone) could have an impact on motorcyclists’ riding behaviors as it could increase the riding behavior score in drug user motorcyclists compared to normal motorcyclists.

The results of recent studies suggest that model-based health education programs are more effective in changing health behaviors. Interventional programs based on health education theories can be used to explain individual and psychological behavioral determinants in order to reduce the risk of HIV/AIDS. This study was conducted with the aim of psychometric analysis of an HIV scale based on model, population and culture of Iran.

We used qualitative and quantitative methods during August 2015 to December 2017. The items were generated based on a literature review and interviews with an expert panel. After providing a primary list of the instrument items, we used content and face validity. The first step of the study was done in a sample of medical and non-medical college and high school student from Zanjan. The exploratory factor analysis, internal consistency reliability, and test-retest reliability of the scale were measured.

Fifty-eight items were generated from interviews with experts and review of the literature. After measuring the face and content validity, the items decreased to 53 items. The exploratory factor analysis discovered five factors (perceived susceptibility, severity, benefits, barriers, and self-efficacy), including 30 items for high school students and 31 for university students, that predictive more than 58% of the observed variance. The Cronbach’s alpha coefficient for the sub-scales ranged from 0.80 to 0.86, and the ICC was above 0.86.

The results showed that the HBM was a valid and reliable tool for measuring the HIV/AIDS beliefs and could be used in future investigations.

  Researchers and health specialists are increasingly using self-reports to obtain information on chronic illnesses. This study was conducted to assess the validity of self-reports of diabetes based on a recent field survey in Mashhad.

In this cross-sectional study, we used the results of 2015 census in Mashhad, a population based survey of people over the age of 30 (n =307103), to determine the proportion of self-reported diabetes. The patient records of Sina Electronic Health Record system (SinaEHR®) coded as E11 and E12 approved by doctors were used as a reference. The sensitivity, specificity, positive predictive value, and negative predictive value were calculated with 95% confidence intervals.

The sensitivity, specificity, PLR, NLR, PPV, and NPV of self-reported diabetes was 24.59% (95% CI: 23.97-25.21), 98.04% (95% CI: 97.99-98.09), 12.56% (95% CI: 12.11-13.02), 0.77% (95% CI: 0.76-0.78), 44.77% (95% CI: 43.89-45.67), and 95.27% (95% CI: 95.23-95.31), respectively. The sensitivity of self-reported diabetes was higher in men, Iranian individuals, single subjects, people aged 60 and over, individuals with a BMI of 18.5-25, and those with university education.

Although the sensitivity of self-reported diabetes was poor in this study, its specificity and positive and negative predictive values were good. Furthermore, the sensitivity of self-reported diabetes was higher in men, Iranian individuals, single subjects, people aged 60 and over, individuals with a BMI of 18.5-25, and those with university education. It seems that caution should be exercised in using self-reported data in epidemiological studies.

  Pulmonary embolism is a potentially fatal and prevalent event that has led to a gradual increase in the number of hospitalizations in recent years. For this reason, it is one of the most challenging diseases for physicians. The main purpose of this paper was to report a research project to compare different data mining algorithms to select the most accurate model for predicting pulmonary embolism in hospitalized patients. This model would provide the knowledge needed by the medical staff fir better decision making.

In this research, we designed a prediction model using different methods of machine learning that would best predict the probability of pulmonary embolism in patients at risk. Among data mining algorithms, Bayesian network, decisions tree (J48), logistic regression (LR), and sequential minimal optimization (SMO) were used. The data used in the study included risk factors and past history of patients admitted to the Lung Department of Shariati Hospital, Tehran, Iran.

The results showed that the accuracy and specificity of all prediction models were satisfactory. The Bayesian model had the highest sensitivity in predicting pulmonary embolism.

Although the results showed a little difference in the performance of prediction models, the Bayesian model is a more appropriate tool to predict the occurrence of pulmonary embolism in hospitalized patients in this type of data. It can be considered a supportive approach along medical decisions to improve disease prediction.

  The Cox model is one of the methods used in survival data; however, the use of hierarchical data, such as the data of this study, violates the assumption of independence, the the Cox model cannot be used assuming independence of observations. One of the important methods for analyzing survival hierarchy data is the use of the multilevel Cox model. In this method, in addition to modeling the response variable, regression coefficients are also modeled and the measurement error resulting from the lack of data independence is reduced. The present study used a multilevel Cox model to investigate the effect of the retention of antihypertensive drugs in people with hypertension.

This longitudinal survival study was conducted in 346 workers with hypertension in Mobarakeh Steel Company in Isfahan. During the years 1390-1394, when the staff attended the health center in the factory, they were treated with six drugs, including captopril, losartan, atenolol, propranolol, amlodipine, and hydrochlorothiazide. In order to examine the relationship of the retention of drugs with job experience, body mass index, and drug history, a two-level Cox model was used as h_ij (t)=h_0 (t)exp⁡(α_g+x_ij β_j), where i and j is the first and second level units, respectively.

During five years, the findings of model fitting showed the effect of body mass index (P = 0.019), atenolol (P=0.046), and amlodipine (P=0.021) in a single form, and the effect of losartan-amlodipine (P=0.042) and atenolol-hydrochlorothiazide (P = 0.003) in a combination form were significant.

Based on the study results, the most effective drugs for hypertension control are amlodipine monotherapy, amlodipine-losartan combination therapy, and atenolol monotherapy, respectively.

  Hypertension is a major global issue due to its consequences. Ordinary regression models have limitations in assessment of hypertension since the reference graph derived from a specific population may not be appropriate for another population. The polynomial quantile regression model is considered as a possible alternative. Hence, this study was conducted with the aim of determining reference values as well as blood pressure percentile curves in Mashhad.

This cross-sectional study was carried out in a random sample of 6949 individuals attending Samen health centers for diabetes screening in 2010. Different percentiles were analyzed using some variables such as gender, age, BMI, WHR, and systolic and diastolic blood pressure. The R software (version 3.0.1) was used for data analysis.

In this study, 70.58% and 29.42% of subjects were men and women, respectively. The results of the quantile regression model showed that with an increase in age, BMI, and WHR, blood pressure increased in all percentiles. In all variables, subjects in the 75th and 95th percentiles had moderately high and high blood pressure while they had a normal blood pressure in other percentiles.

The model provided more information about blood pressure and its related patterns. According to the results, it seems that more attention should be paid to elderly and overweight individuals in the 75th and 95th percentiles