Introduction: Socioeconomic inequality among low- and middle-income countries has an immense impact on the growth characteristics of children. Consequently, the millennium development goals were established for  action to fight poverty and reduce the health problems for most disadvantaged groups.

Objectives: The objectives of this study were to investigate the growth characteristics and correlates of height growth among children in low- and middle-income countries. 

Methods: Data from the Young Lives study conducted in Ethiopia, India, Peru and Vietnam for 15 years were used. A linear mixed-effects fractional polynomial modeling approach was used to analyze the growth characteristics and to assess the determinants. 

Results: There was a significant growth difference in height among children in low- and middle-income countries. Children in Vietnam grew at a faster rate during the entire period considered (1-15 years). In four countries, children grew very quickly in early childhood and the growth rates slow down gradually during the consecutive years. The results show that factors such as gender, parents’ education, household size, wealth index, access to sanitation, fathers’ age and residence area are significantly associated with child growth. 

Conclusion: The functional relationship between height growth and time is nonlinear. Males are taller than females at an early childhood age. Children from the most educated father and mother had been taller than those from the least educated father and mother. The effect of the household wealth index is positive on height growth, while the effect of household size is negative.

Background and Objective: Due to the impact of risky behaviors in the community and the need for getting information and planning in this regard, the number of people with high-risk sexual behaviors in Isfahan will be indirectly estimated by the network scale-up method. 

Method: In a cross-sectional study conducted in June2018in14districts of Isfahan, a sample of1000 people was recruited by a non-random multistage method and interviewed using a standard questionnaire to identify people with high-risk sexual behavior. Data are analyzed based on a network scale-up method in the STATA application.

Results: According to a report by men, the prevalence of male Extra marital sexual relations (N=2437) and relation with paying prostituted women (N=1211), with non-paying prostituted women  (N=298), Homosexuality (N=696) and history of traveling for sexual relations (N =880/100,000); And according to a report by women, the prevalence of female Extra marital sexual relations (N=1386) and Sex Worker women (Monetary) (N=946), Sex Worker women (Non-Monetary) (N=258), and history of travelling for sexual relations (N=13/100,000). In both sexes, the age group of18 to 30years was more at risk for sexual behaviors than other groups 

Discussion and Conclusion: It seems that the prevalence of sexual high-risk behaviors in Isfahan is remarkable as the increased prevalence of sexually transmitted diseases, including HIV, but unfortunately, the required training is low in this regard, more attention should be paying to train people to prevent the prevalence of these high-risk sexual behaviors in society.

Introduction: Angiography is used as the gold standard for diagnosis of coronary artery disease (CAD). It is an invasive procedure and has several complications. Also, some patients refuse angiograms for reasons such as fear, high cost, and loss of trust in physician diagnosis. The negative results of this test is more than a third. Therefore, having a statistical predictive model for estimating the risk of CAD, as an evidence-based support system, can help the physician and patient decide on the necessity of angiography. 

Aims: In this study we aimed to find an evidence-based supportive model for decision making on the necessity of angiography in people who were candidates for angiography by the physician after initial tests. 

Methods: In this study, 1187 patients who had been referred to Ghaem Hospital of Mashhad University of Medical Sciences and diagnosed with physicians after initial tests were enrolled. Demographic data, lipid and blood sugar levels, and the history of underlying disorders were variables that were studied in the statistical model fitting. Initially, generalized additive models were used singularly for quantitative predictors, then by applying right transformations on the predictor variables we entered them simultaneously in logistic and count regression models. These two models were fitted to the data using R software and then compared in terms of predictive accuracy. 

Findings: Generalized additive models showed that the relationship between CAD with the hs-CRP level was not monotone. Exploratory analyzes showed that 62% of people with hs-CRP level <3 and 50% of people with hs-CRP levels between 3 and 6 were suffered from the CAD. The highest rate of CAD was seen in the range of 6-8 (93%) but with increasing the hs-CRP level to above 8 it decreased to 70%. The relationship between age and the risk of CAD was S-shaped. Risk of CAD in diabetic subjects with normal FBS was equal to that of nondiabetic subjects with normal fasting blood sugar. The age, gender, diabetes, FBS, and hs-CRP were significant in both models (p <0.05). The area under the ROC curve was upgraded to 81 for the logistic model. 

Conclusion: The most important finding of this exploratory study was that out of 232 patients with hs-CRP level between 6 to 8, 217 (93%) had coronary artery occlusion, for these subjects the probability of occluding a coronary artery was 0.93. The present study also showed that if the blood sugar is controlled in patients with diabetes, then this disease will not be a risk factor for patients with coronary artery occlusion. The logistic regression model presented in this study is recommended as the final model to support decision-making about the necessity of angiography.

Introduction: Postpartum depression (PPD) is a major cause of burden of diseases in women within the first 4 weeks of delivery. The aim of this study was to determine the prevalence and the role of various factors in PPD in the northern and northeastern regions of Khuzestan province. 

Methods: This descriptive-analytical study was undertaken as the first phase of a case-control study on 1424 mothers in the first 24 to 48 hours after childbirth between January 2019 and January 2020. The data collected covered three areas: baseline characteristics, medical history, and PPD. The latter was measured using the Edinburgh Postpartum Depression Scale with a cut-off point of equal to or greater than 12. The collected data were analyzed using Stata-16 software and simple and multiple Logistic Regression models. 

Results: The prevalence of PPD was estimated at 26.6% in the study sample. In the multiple model, the History of elective abortion (OR= 2.26, 95%CI=1.19, 4.27), delivery in the summer (OR= 2.11, 95%CI=1.39, 3.20), birth defect (OR= 2.09, 95%CI=1.10, 3.94), the history of infertility treatment (OR= 0.33, 95CI=0.18, 0.61) and living in urban areas (OR= 0.52, 95%CI=0.39, 0.70), had relationship with the chance of developing PPD. 

Conclusion: The results of this study, which sought to identify factors reinforcing and preventing PPD, can be used to identify mothers at risk for PPD. Moreover, it can help make appropriate interventions, including psychological and emotional support of mother during and even before pregnancy to alleviate PPD.

Introduction: The COVID-19 epidemic is currently fronting the worldwide health care systems with many qualms and unexpected challenges in medical decision-making and the effective sharing of medical resources. Machine Learning (ML)-based prediction models can be potentially advantageous to overcome these uncertainties.

Objective: This study aims to train several ML algorithms to predict the COVID-19 in-hospital mortality and compare their performance to choose the best performing algorithm. Finally, the contributing factors scored using some feature selection methods. 

Material and Methods: Using a single-center registry, we studied the records of 1353 confirmed COVID19 hospitalized patients from Ayatollah Taleghani hospital, Abadan city, Iran. We applied six feature scoring techniques and nine well-known ML algorithms. To evaluate the models’ performances, the metrics derived from the confusion matrix calculated. 

Results: The study participants were 1353 patients, the male sex found to be higher than the women (742 vs. 611), and the median age was 57.25 (interquartile 18-100). After feature scoring, out of 54 variables, absolute neutrophil/lymphocyte count and loss of taste and smell were found the top three predictors. On the other hand, platelet count, magnesium, and headache gained the lowest importance for predicting the COVID-19 mortality. Experimental results indicated that the Bayesian network algorithm with an accuracy of 89.31% and a sensitivity of 64.2 % has been more successful in predicting mortality. 

Conclusion: ML provides a reasonable level of accuracy in predicting. So, using the ML-based prediction models facilitate more responsive health systems and would be beneficial for timely identification of vulnerable patients to inform appropriate judgment by the health care providers. Abbreviation: Coronavirus Disease 2019 (COVID‐19), World Health Organization (WHO), Machine Learning (ML), Artificial Intelligence (AI), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Locally Weighted Learning (LWL), Clinical Decision Support System (CDSS) 

Introduction: The aim of this study was to investigate the prevalence of COVID-19 in dentists and dental assistants and their associated signs and symptoms. 

Methods: A cross-sectional study was conducted using an online survey from 9th to 23th May 2020. The sample size was 385. The questionnaire was registered at Porsline website. The questionnaire included questions about infection of dentists, dental assistants and their families with COVID-19, signs and symptoms, paraclinical tests, and treatments. The data were entered into Excel and SPSS software and analyzed using logistic regression test. 

Results: From all responding dentists, 15.8% were suspected of having covid-19. Based on symptoms, only 1.6% of dentists were highly suspicious for COVID-19. Only 0.78% of dentists were definitely positive for COVID-19 based on paraclinical tests. Symptoms were often very mild to moderate in severity. Among dental assistants, 5.5% were suspected of having COVID-19. Based on symptoms, none of dentists’ assistants were highly suspicious for COVID-19. None of dental assistants were definitely positive for COVID-19 based on paraclinical tests. Symptoms were often very mild to moderate in severity. Logistic regression showed that the odds of infection with Corona was higher in government-sector dentists than in the private sector (OR: 1.189; 95% C.I: 0.812-1.742), in specialist dentists than in general (OR: 1.903; 95% C.I: 0.532-2.245), and in dentists between the ages of 30-60 years old than under the age of 30 (OR: 3.647; 95% C.I: 0.840-15.835). 

Conclusion: Despite the fact that dentistry is a high-risk job for COVID-19 infection, the overall prevalence of COVID-19 in dentists and their assistants might be very low and the severity of symptoms in case of infection is probably mild.

Introduction: Anxiety in students is a challenge of educational systems. The present study was conducted to investigate the efficiency of Pop Quiz (unannounced formative tests) in teaching biostatistics to postgraduate midwifery students and its effects on their statistics anxiety, test anxiety and statistical analysis skills. 

Methods: This quasi-experimental study conducted during the first semester of the academic year of 2019-2020 in the Faculty of Nursing and Midwifery, Ahvaz Jundishapur University of Medical Sciences. The MSc midwifery students were divided into two separate classes. One of the classes was randomly selected for educational intervention (Pop Quiz). Teaching via the lecture method considered as control method. Test anxiety and statistical anxiety questionnaires were completed by the students in both groups before the educational intervention, during and at the end of semester. The final exam score considered as the statistical skills score.Data were analyzed in SPSS 22 using Fisher's exact test and GEE model. 

Results: Thirty eight MSC midwifery students (12 in intervention group and 26 in comparison group)were recruited in this study. The mean and standard deviation of the exam score of students in lecture and Pop Quiz groups were respectively 14.43 ± 3.80 and 15.95 ± 2.79 (P=0.182). The patterns of change in test anxiety score differed significantly over time between the two teaching methods (P = 0.003). Although, there was a decreasing trend in mean score of statistics anxiety scores in Pop Quiz group in comparison with lecture based group, but there were not statistically significant differences. 

Conclusion: Applying Pop Quiz to teaching biostatistics reduces test anxiety and statistics anxiety and increases statistical analysis skills in postgraduate midwifery students

Introduction: Recently, with the surge in the availability of relevant data in various industries, the use of Information Fusion technique for data analysis is increasing. This method has several advantages, such as increased accuracy, and the use of meaningful information. In addition, there are certain challenges, including the impact of data type and analytical method on results. The goal of this study is to propose a framework for introducing the advantages and classifying the challenges of this technique. 

Method: We conducted a review of articles published between January 1960 and December 2017 for the design stage and from January 2018 to December 2018 for the evaluation stage. Articles were identified from various databases such as Science Direct, IEEE, Scopus, Web of Science, and Google Scholar, using the keywords decision fusion, information fusion, and symbolic fusion. We report the advantages and challenges of the methodologies described in these articles. Analysis was conducted in accordance with PRISMA guidelines. 

Results: A total of 132 articles were identified in the design stage and 90 articles were identified in the evaluation stage. Categories within the framework for challenges include “hardware and software requirements for processing and maintaining the process”, “data” and “data analysis method”. The categories for advantages include “value modeling”, “preferable management of uncertainty and variability”, “excellent decision making”, “comprehensive interpretation and representation”, “data management” and “simplicity of infrastructure”. Our results indicate using these two frameworks with 95% Confidence interval. 

Conclusion: An overall understanding of the advantages and challenges of the information fusion technique could act as a guide for the researcher for the correct usage of this technique  

Introduction: Epidemic curves are a type of time series data consisting of the number of events that occur over a period of time. The time unit in this data can be a day, a week, or a month, etc. 

Methods: In the current letter, the authors tried to explain the growth factor and its effect on epidemic curves by using some literature. 

Results: In the outbreaks setting, the number of cases can increase with different patterns. When the number of cases is increasing exponentially, it means that the number of cases is increasing at a certain speed, which is determined by a factor called an exponential growth factor. When this factor is greater than one, it means that the cases are increasing exponentially, and when this coefficient is equal to 1, it means that we have reached an inflection point that we will face a change in the growth rate of the cases. 

Conclusion: Some factors such as reducing the contact between infected and healthy people, run the social distancing program, and so on can have an effective role in decreasing epidemic growth factor and controlling the epidemic.