Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 8 1 2022 03 15 1 7 Mojtaba Sepandi Exercise Physiology Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran Yousef Alimohamadi Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran Firooz Esmaeilzadeh Department of Public Health, School of Public Health, Maragheh University of Medical Sciences, Maragheh, Iran 2021 10 27 2022 01 01 Introduction: An essential concept in assessing the extent to which an infectious outbreak spread is the concept of basic reproductive number (R0). The current systematic review and meta-analysis aimed to estimate the R0 of the Delta variant of SARS-CoV-2 based on studies published from 1 January 2021 to 23 September 2021. Methods: International databases (including Google Scholar, Science Direct, PubMed, and Scopus) were searched using keywords: "Basic reproduction number, R0, COVID-19, SARS-COV-2, Severe Acute Respiratory Syndrome Coronavirus, NCOV, 2019 NCOV, coronavirus, Delta variant, B.1.617.2". Due to significant heterogeneity, DerSimonian-Laird random-effects model was used to estimate the pooled value of R0. Results: A total of 245 reports were identified. After assessing the inclusion criteria, three studies were selected. The pooled R0 for the Delta variant was estimated as 5.10 (95% CI, 3.04 to 7.17). (I2 =86.77%, T2:2.68, p-value from the chi-square test for heterogeneity was<0.001). Conclusions: Considering the estimated value of R0 for the Delta variant of SARS-CoV-2, the amount of vaccine coverage required to achieve herd immunity appears to be higher than previous variants of the virus. https://jbe.tums.ac.ir/index.php/jbe/article/view/782

Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 8 1 2022 03 15 8 23 Firouz Amani Department of Biostatistics and Community Medicine, School of Medicine, Ardabil University of Medical Science, Ardabil, Iran Jafar Abdollahi Department of Computer Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran alireza mohammadnia Department of Basic Science, School of Medicine, Ardabil University of Medical Science, Ardabil, Iran paniz amani Student in Electronic Engineering, Tabriz University, Tabriz, Iran ghasem fattahzadeh-ardalani Department of Neurology, School of Medicine, Ardabil University of Medical Science, Ardabil, Iran 2021 05 08 2022 01 16 Introduction: Early arrival of patients with acute ischemic stroke to start of treatment by recombinant tissue plasminogen activator (rt-PA) within 4.5 hours after onset of stroke. We aimed to develop a machine learning method to predict effective factors on arrival time of patients with stroke to hospital after symptom onset. Methods: We included 676 patients with ischemic stroke who referred to Ardabil city hospital a province in northwest of Iran at year 2018. Classification models such as Random forest (RF), Gradient Boosting Classifier (GB), Decision Tree Classifier (DT), Support-Vector Machines (SVM), Naïve Bayes (NB), artificial neural networks (ANN), and logistic regression (LR) with 10-fold cross-validation were developed to predict effective factors on arrival time of patient with stroke to hospital. The performances were evaluated with accuracy, sensitivity, specificity, positive prophetical worth, and negative prophetical worth.  Results: Of all patients, 25.3% arrived to the hospital in less than 4.5 hours. The accuracy of RF, NB, ANN, GB, DT, SVM, LR and suggest method (Stacking) were 0.98, 0.72, 0.73, 0.79, 0.98, 0.73, 0.74, and 0.99. Conclusion: In this study, the Stacking technique provide a better result (Accuracy 99.51%, sensitivity 100%, and specificity 99.40%) among all other techniques and this model could be used as a valuable tool for clinical decision making. https://jbe.tums.ac.ir/index.php/jbe/article/view/512 https://jbe.tums.ac.ir/index.php/jbe/article/download/512/353

Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 8 1 2022 03 15 24 36 Kindu Gebre Department of Statistics, College of Computing and Informatics, Haramaya University, Dire Dawa, Ethiopia Million Demissie Department of Statistics, College of Computing and Informatics, Haramaya University, Dire Dawa, Ethiopia 2021 07 09 2021 09 01 Introduction: Anemia is the most common public problem caused by nutritional deficiency diseases among women of reproductive age. The main objective of this study was determining the regional variation and associated factors of anemia status among women of reproductive age in Ethiopia. Methods: A cross-sectional study was conducted among 14,489 women who enrolled in Ethiopia demographic and health survey data of 2016. Binary and multilevel logistic regression was carried out for variables to determine associated factors with anemia status of women and its regional variations at ascertained of 5% level. This study was used information criteria to compared candidates models. Results: This finding shows that women who use improved source of drinking water (OR=1.98, 95%CI=1.05, 3.72), being in middle wealth index (OR=0.25, 95%CI=0.10, 0.63), being in rich wealth index (OR=0.42, 95%CI=0.19, 0.94), having age at 1st birth in 20-24 years(OR=0.24, 95%CI=0.11, 0.53), having number of living children 1-2(OR=3.68, 95%CI=3.48, 4.98), having number of living children 3-4(OR=3.03, 95%CI=2.48, 4.05) and women who used government health center for place of delivery(OR=0.96, 95%CI=0.22, 1.70) were significantly related to anemia status of women. Conclusion: This finding concluded that there is a significant variation of anemia status of women between regions in Ethiopia. Women in the middle and rich wealth index was less likely to be anemic than poor. Women having age at 1st birth in 20-24 years and women who used government health center for place of delivery were less likely to be anemic. But women having number of living children 1-2 and 3-4 were more likely to be more anemic than no child. Likewise, women who use improved source of drinking water were more likely to be anemic as compared to an unimproved source of drinking water. It is recommended that health workers should begive attention to these proximate determinants on anemia at regional level https://jbe.tums.ac.ir/index.php/jbe/article/view/629 https://jbe.tums.ac.ir/index.php/jbe/article/download/629/354

Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 8 1 2022 03 15 37 44 mahdieh mirzaie Modeling in Health Research Center, Institute for Futures Studies in Health,Kerman University of Medical Sciences, Kerman,Iran. AND Department of Biostatistics and Epidemiology, School of Public Health, Kerman University of Medical Sciences, Kerman, Iran yunes jahani Modeling in Health Research Center, Institute for Futures Studies in Health,Kerman University of Medical Sciences, Kerman,Iran. AND Department of Biostatistics and Epidemiology, School of Public Health, Kerman University of Medical Sciences, Kerman, Iran abbas bahrampour Modeling in Health Research Center, Institute for Futures Studies in Health,Kerman University of Medical Sciences, Kerman,Iran AND Department of Biostatistics and Epidemiology, School of Public Health, Kerman University of Medical Sciences, Kerman, Iran 2021 07 17 2022 01 16 Background: Logistic regression is one of the most common models used to predict and classify binary and multiple state responses in medicine. Genetic algorithms search techniques inspired by biology have recently been used successfully as a predictive model. Objective: The aim of this study was to use the genetic algorithm and logistic regression models in diagnosing and predicting factors affecting breast cancer mortality. Method: In this study, data of 2836 people with breast cancer during the years 2014-2018 was examined; their information was recorded in the cancer registration system of Kerman University of Medical Sciences. Death status was considered a dependent variable, while age, morphology, tumor differentiation (grad), residence status, and place of residence were considered independent variables. Sensitivity, specificity, accuracy, and area under the ROC curve were used to compare the models. Results: the logistic regression model determined factors affecting the breast cancer mortality rate, (with sensitivity (0.62), specificity (0.81), area under the ROC curve (0.74), and accuracy (0.84)), and genetic algorithm model (, with sensitivity (0.19), specificity (0.97), area under the ROC curve (0.58) and accuracy (0.87)). Conclusions: The sensitivity and area under the ROC curve of the logistic regression model were higher than those of the genetic algorithm, but the specificity and accuracy of the genetic algorithm were higher than those of the logistic regression. According to the purpose of the study, two models can be used simultaneously. https://jbe.tums.ac.ir/index.php/jbe/article/view/650 https://jbe.tums.ac.ir/index.php/jbe/article/download/650/355

Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 8 1 2022 03 15 45 60 Elham Haem Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran. Marziyeh Doostfatemeh Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz 2021 07 25 2021 10 12 Background: The multidimensional item response theory (MIRT) model provides an ideal foundation for assessing the psychological properties of a questionnaire designed with multidimensional structure. This study aimed to present the first use of MIRT models to investigate the psychometric properties of general health questionnaire (GHQ-12) in parents of school-aged children. Methods: A total of 1104 parents of school children-aged completed the Persian version of GHQ-12 questionnaire. The unidimensional IRT model and MIRT models with two and three factors were applied to model the observed scores for each GHQ-12 item as a function of the subject’s latent traits while taking the correlation among dimensions of the questionnaire into account. Goodness of fit indices were reported for the three models, and the fits of items were assessed for the best model. Individual items were described in detail through item characteristic curves, and the amount of information carried by different items was presented using information curves. Results: The MIRT analysis with three factors corresponding with anxiety depression, social dysfunction and loss of confidence provided the best account of the GHQ-12 data. The model showed that all items were fitted adequately. Items varied in their discrimination ranged from 0.94 to 2.13, 1.31 to 2.74, and 2.87 to 3.57 for social dysfunction, anxiety depression, and loss of confidence, respectively. Moreover, items 8 and 2 provided the least information in social dysfunction and anxiety depression dimensions, respectively. Items in the loss of confidence dimension carried the most information among all items of the GHQ-12. Conclusions: The developed framework for evaluating the psychometric properties of GHQ-12 can be a suitable alternative to traditional approaches as well as unidimensional IRT models, the use of which has been restricted due to the multidimensional structure of the questionnaire.       https://jbe.tums.ac.ir/index.php/jbe/article/view/664 https://jbe.tums.ac.ir/index.php/jbe/article/download/664/356

Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 8 1 2022 03 15 61 76 Maryam Ghodsi Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran Bagher Larijani Endocrinology and Metabolism Research Center (EMRC), Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran Shahin Roshani Non-Communicable Diseases Research Center (NCDRC), Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran Mahsa Mohammad Amoli Metabolic Disorders Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran Farideh Razi Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran Abbas Ali Keshtkar Department of Health Sciences Education Development, School of Public Health, Tehran University of Medical Sciences (TUMS), Tehran, Iran Patricia Khashayar Center for microsystem technology, Imec and Ghent University, Zwijnaarde, Ghent, Belgium ANDOsteoporosis Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran Fariba Zarrabi Thrombosis Hemostasis Research Center, Tehran University of Medical Sciences, Tehran, Iran AND Department of Biology, College of Basic Science, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran Mohammad Reza Mohajeri-Tehrani Endocrinology and Metabolism Research Center (EMRC), Endocrinology and Metabolism Clinical Sciences Institute , Tehran University of Medical Sciences (TUMS), Tehran, Iran 2021 08 04 2022 02 01 Background. An important part of preventing major common diseases is identifying genetic factors that contribute to their occurrence. For the first time in our knowledge, we investigated the association between polymorphisms of five vitamin D receptor (VDR) genes (ApaI, BsmI, FokI, EcoRV, and TaqI) and low bone density/osteopenia/osteoporosis in individuals with type 2 diabetes using classification and regression tree (CART) algorithms. Methods. Data from 158 participants with T2D were used to develop the CART analysis. The binary output variable was "bone state" with low or normal values. Age and BMI (continuous variables), vitamin D deficiency (yes/no), and gender (binary variables), as well as polymorphisms of the five VDR genes (categorical variables) all played a role in the explanatory model. A 10-fold cross-validation process was used for model validation. Results. Participants were divided into three groups based on their sex. In all groups, age was the major factor predicting the low state in the final obtained tree model. The second most significant predictor in each model was BMI in both sexes (accuracy:75.32% and, AUC:0.748), EcoRV polymorphism in women (accuracy:78.79 %, AUC: 0.794), and TaqI polymorphism in men (accuracy:71.19%, AUC: 0.651). Conclusion Model validation of the final tree models demonstrated that the use of CART algorithms could be a valuable technique for identifying individuals with T2D who are at risk for early-onset osteoporosis based on their polymorphism of the studied VDR genes. Our recommendation is to conduct more population-based studies. We hope this study will serve as a basis for future research. https://jbe.tums.ac.ir/index.php/jbe/article/view/685 https://jbe.tums.ac.ir/index.php/jbe/article/download/685/357

Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 8 1 2022 03 15 77 89 Mohammad Reza Afrash School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Leila Erfanniya Assistant Professor of Health Information Management, Department of Health Information Technology, Faculty of Paramedical, Zahedan University of Medical Sciences, Zahedan, Iran AND Clinical Education Research Center, Shiraz University of Medical Sciences, Shiraz, Iran Morteza Amraei Department of Health Information Technology, School of Allied Medical Sciences, Lorestan University of Medical Sciences, Khorramabad, Iran. Nahid Mehrabi Assistant Professor, Department of Health Information Technology, Aja University of Medica Saeed Jelvay Instructor of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran raoof Nopour Department of Health Information Technology and Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran. Mostafa Shanbehzadeh Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran 2021 08 20 2022 01 30 Background: Needless to say that correct and real-time detection and effective prognosis of the COVID-19 are necessary measures to deliver the best possible care for patients and, accordingly, diminish the pressure on the health care industries. The main purpose of the present paper was to devise practical solutions based on Machine Learning (ML) techniques to ease the COVID-19 screening in routine blood test data. We came up with different algorithms for the early detection of COVID-19 and finally succeeded to opt for the best performing algorithm. Material and methods:  In this developmental study,  the laboratory data of 1703 COVID-19 and non-COVID-19 patients Using a single-center registry from February 9, 2020, to December 20, 2020, were analyzed by several ML algorithms which included, K-Nearest Neighbors, AdaBoost Classifier, Decision Tree, and HistGradient Boosting Classifier. A 10-fold cross-validation method and six performance evaluation metrics were used to evaluate and compare these implemented algorithms. Using the best ML-developed model, a Clinical Decision Support System (CDSS) was implemented with C# programming language. Results: The results indicated that the best performance belongs to the AdaBoost classifier with mean accuracy, specificity, sensitivity, F-measure, KAPA rate, and ROC of 87.1%, 85.3%, 87.3%, 87.1 %, 89.4%, and 87.3 % respectively Discussion: The ML makes a reasonable level of accuracy possible for an early diagnosis and screening of COVID-19. The empirical results reveal that the Adaboost model yielded higher performance compared with other classification models and was used for developing our CDSS interface in discriminates positive COVID-19 from negative cases. https://jbe.tums.ac.ir/index.php/jbe/article/view/704 https://jbe.tums.ac.ir/index.php/jbe/article/download/704/358

Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 8 1 2022 03 15 90 101 Tayebeh Rakhshani Nutrition Research Center, Department of Public Healt, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran Hamid Salehiniya Social Determinants of Health Research Center, Birjand University of Medical Sciences, Birjand, Iran Monire Azizi Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran Alireza Ansari-moghadam Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran. seydeh Zeinab Almasi Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran. AND Department of Epidemiology and Biostatistics, Tehran University of Medical Sciences, Tehran, Iran. Hassan okati Aliabad Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran 2021 08 28 2021 10 23 Background: recent medical and health advances have reduced mortality, consequently a relative increase in life expectancy and aging of population. One of the indices that properly indicate the status of elderly is the quality of life index. Objectives: objective of the present study was to identify the factors affecting the quality of life of the elderly in Zahedan, Iran. Methods: This cross-sectional study was performed on 600 elderly people referring to the Zahedan health centers. In this two-stage cluster random sampling method, the data were collected in the  check list  and  the quality of life questionnaire SF12 through interview and  analyzed using independent t-test, one-way ANOVA, Pearson correlation coefficient and  multiple linear regression Results: Of the 600 elderly men and women over 60 years, 472 subjects participated in the study, of whom 291 (61%) were male and 182 (39%) female. The mean age of the study subjects was 66.2(4.04), and the mean overall quality of life scores in males and females were 28.4(3.7) and 29.07(3.7), respectively. The mean and standard deviation of PCS and MCS scores in males and females were 12.3(2.2) and 16.6(2.5), respectively. Age had inverse correlation with QOL and MS and had a direct and significant relationship with PCS. In multiple linear regression, significant relation was observed between chronic illness, hypertension, skeletal disease, diabetes, gastrointestinal disease, marital status, hookah use and smoking with PCS and also between marital status, Hypertension and mental illness with MCS. Conclusions: What is obtained from this study and the other relevant studies indicate that QOL is a multifactorial phenomenon that is influenced by demographic, clinical and behavioral factors, but the role of chronic diseases is more obvious. Therefore, it seems necessary to adopt health policies to correct the lifestyle of society   https://jbe.tums.ac.ir/index.php/jbe/article/view/708 https://jbe.tums.ac.ir/index.php/jbe/article/download/708/359

Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 8 1 2022 03 15 102 114 Olubiyi Olaolu Abidemi Department of Family Medicine, State Specialist Hospital, Ijero-Ekiti, Nigeria Olubiyi Oluwafunmilola Department of Statistics, Faculty of Science, Ekiti State University, Ado-Ekiti, Nigeria. Babalola Teniola 3Department of Mathematics and Statistics, School of Mathematics and Computing, Kampala International University, Kampala, Uganda 2021 04 13 2022 01 30 Introduction: People living with HIV/AIDs (PLWHA) are also prone to mental health problem such as depression. However, there is limited evidence on the effects of care giver counselling on the level of depression among people living with HIV/AIDs. This study aimed to determine factors associated with depression and the effects of care giver counselling and follow up on depression among PLWHA attending Federal Teaching Hospital, Ido-Ekiti, Nigeria. Methods: This study has two parts. The first part addressed the descriptive aspect of the study while in the second part, an experimental study was performed on 64 depressed HIV patients (32 intervention group and 32 in the control group). These 64 respondents were randomly screened out of 351 consenting PLWHA in the hospital using Zung’s self-rating depression scale. A systematic random sampling technique was employed to allocate participants to the groups, with the first client of the 64 participants allocated to the control group and the next client allocated to intervention group. On-phone counselling of a minimum of 30 minutes (once in a week) was done for the patients in the experimental group for a month after which a post intervention assessment was done for both intervention and control groups. Bar chart and descriptive statistics were employed to explain the data. Yate’s Chi-squared statistics was employed to find out statistical associations between the groups while the p-values were consequently reported. Results: The age of the studied subjects ranged between 21-80 years with a mean age of 41.53 (±9.06). One hundred forty-nine (42.5%) of the 351 subjects were found to have one form of depression or the other. Of 351 subjects, 57.5% were not depressed, 17.1% had mild depression, 10.3% had moderate depression and 15.1% had severe depression. One hundred two (29.1%) of 351 respondents came from a severely dysfunctional family, while 193(55.0%) from a moderately dysfunctional family and 56(16%) from highly functional family. The percentage of the intervention group that suffered severe depression reduced from 40.6% to 6.2% after the intervention as opposed to a marginal reduction of 34.4% to 31.2% in the control group without intervention (p-value<0.001). Also, the relationships between the severity of depression and BMI, CD4 and family functions were significantly associated with p-values of <0.001. Conclusion: Care giver counselling significantly reduced depression among PLWHA. Therefore, PLWHA should be encouraged through policies and otherwise to attend counselling sessions with caregivers https://jbe.tums.ac.ir/index.php/jbe/article/view/478