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The β-thalassemias are a group of genetic disorders defined by decreased levels of functional hemoglobin (Hb). In light of pivotal improvements in patient survival, the load of consistent treatment harms patients' quality of life (QOL). This study aimed to determine the QOL in patients with β-thalassemia (β-thal) in Iran and identify associated factors. This cross-sectional study was conducted among 1240 patients with β-thal. Data for this study were obtained from the General, the TranQol (Transfusion-dependent QoL) Standard, and the Multidimensional Scale of Perceived Social Support (MSPSS) questionnaires. The univariate and multivariable linear regression was used in STATA version 14 to identify factors related to QOL. Overall, the QOL score was 103 ± 21.96, and adults had a higher score than children under 15 years old. Emotional health had the highest score (39.96 ± 11.54), and sexual activity in adults (1.87 ± 2.08) and activities related to education in children (10.43 ± 7.46) had the lowest. The multivariable linear regression analysis showed that the age, gender, age of blood transfusion initiation, Hb level, number of underlying diseases, and social support level by family and community significantly impact QOL. In exchange for an increase in comorbidities, patients' QOL decreased by 86.0% [odds ratio (OR) = 0.14, 95% confidence interval (95% CI): 0.04-0.45]. Many factors affecting the QOL can be controlled, so social support, increased Hb levels, regular and timely blood transfusions, and treatment can improve the thalassemia patients' QOL.

Background Surgical Site Infections (SSIs) are among the leading causes of the postoperative complications. This study aimed at investigating the epidemiologic characteristics of orthopedic SSIs and estimating the under-reporting of registries using the capture-recapture method. Methods This study, which was a registry-based, cross-sectional one, was conducted in six educational hospitals in Tehran during a one-year period, from March, 2017 to March, 2018. The data were collected from two hospital registries (National Nosocomial Infection Surveillance System (NNIS) and Health Information Management database (HIM)). First, all orthopedic SSIs registered in these sources were used to perform capture-recapture (N = 503). Second, 202 samples were randomly selected to assess patients` characteristics. Results Totally, 76.24% of SSIs were detected post-discharge. Staphylococcus aureus (11.38%) was the most frequently detected bacterium in orthopedic SSIs. The median time between the detection of a SSI and the discharge was 17 days. The results of a study done on 503 SSIs showed that the coverage of NNIS and HIM was 59.95 and 65.17%, respectively. After capture-recapture estimation, it was found that about 221 of orthopedic SSIs were not detected by two sources among six hospitals and the real number of SSIs were estimated to be 623 ± 36.58 (95% CI, 552–695) and under-reporting percentage was 63.32%. Conclusion To recognize the trends of SSIs mortality and morbidity in national level, it is significant to have access to a registry with minimum underestimated data. Therefore, according to the weak coverage of NNIS and HIM among Iranian hospitals, a plan for promoting the national Infection Prevention and Control (IPC) programs and providing updated protocols is recommended.

Objective (s): This study aimed to investigate the pattern, prevalence, behavioral and socio-economic factors that relate to tooth loss among Shahid-Beheshti University of Medical Sciences employees. Methods: The data used for this was derived from a cohort study of Shahid-Beheshti University of Medical Sciences employees. The study was assessed tooth loss among 1381 participants using ordinal regression modeling . Additionally data from self-administered questionnaire were used. Results: Prevalence of tooth loss and edentulous in both genders was 68.1% and 1.8% respectively. In general tooth loss among women were lower than men (P

Recent studies reported that some recovered COVID‐19 patients have tested positive for virus nucleic acid again. A systematic search was performed in Web of Science, PubMed, Scopus, and Google Scholar up to March 6, 2021. The pooled estimation of reinfection, recurrence, and hospital readmission among recovered COVID‐19 patients was 3, 133, and 75 per 1000 patients, respectively. The overall estimation of reinfection among males compared to females was greater. The prevalence of recurrence in females compared to males was more common. Also, hospital readmission between sex groups was the same. There is uncertainty about long‐term immunity after SARS‐Cov‐2 infection. Thus, the possibility of reinfection and recurrence after recovery is not unexpected. In addition, there is a probability of hospital readmission due to adverse events of COVID‐19 after discharge. However, with mass vaccination of people and using the principles of prevention and appropriate management of the disease, frequent occurrence of the disease can be controlled., Highlights The pooled estimation of re‐infection rate among recovered COVID‐19 patients was 3 per 1000 patients.The pooled estimation of recurrence rate among recovered COVID‐19 patients was 133 per 1000 patients.Hospital readmission among recovered COVID‐19 patients was 75 per 1000 patients.The possibility of re‐infection, recurrence and hospital readmission after recovery is not unexpected. Thus, adherence to principles of prevention besides vaccination is essential.

Introduction: The correlation between echocardiographic findings and the outcome of COVID-19 patients is still under debate. Objective: In the present study it has been endeavored to evaluate the cardiovascular condition of COVID-19 patients using echocardiography and to assess the association of these findings with in-hospital mortality. Methods: In this retrospective cohort study, hospitalized COVID-19 patients from February to July 2020 with at least one echocardiogram were included. Data were extracted from patients’ medical records and the association between echocardiographic findings and in-hospital mortality was assessed using a multivariate model. The findings were reported as relative risk (RR) and 95% confidence interval (95% CI). Results: Data from 102 COVID-19 hospitalized patients were encompassed in the present study (63.7±15.7 mean age;60.8% male). Thirty patients (29.4%) died during hospitalization. Tricuspid regurgitation (89.2%), mitral valve regurgitation (89.2%), left ventricular (LV) diastolic dysfunction (67.6%), pulmonary valve insufficiency (PI) (45.1%) and LV systolic dysfunction (41.2%) were the most common findings on patients’ echocardiogram. The analyses of data showed that LV systolic (p=0.242) and diastolic (p=0.085) dysfunction was not associated with in-hospital mortality of COVID-19 patients, while the presence of PI (RR=1.85;95% CI: 1.02 to 3.33;p=0.042) and patients’ age (RR=1.03;95% CI: 1.01 to 1.08;p=0.009) were the two independent prognostic factors of in-hospital mortality. Conclusions: It seems that LV systolic and diastolic dysfunction was not associated with in-hospital mortality of COVID-19 patients. However, presence and PI and old age are possible prognostic factors of COVID-19 in-hospital mortality. Therefore, using echocardiography might be useful in management of COVID-19. © 2021 Tehran University of Medical Sciences.

Objectives: Reducing infant mortality in the whole world is one of the millennium development goals.The aim of this study was to determine the factors related to infant mortality using data mining algorithms. Methods: This population-based case-control study was conducted in eight provinces of Iran. A sum of 2,386 mothers (1,076 cases and 1,310 controls) enrolled in this study. Data were extracted from health records of mothers and filled with checklists in health centers. We employed several data mining algorithms such as AdaBoost classifier, Support Vector Machine, Artificial Neural Networks, Random Forests, K-nearest neighborhood, and Naive Bayes in order to recognize the important predictors of infant death; binary logistic regression model was used to clarify the role of each selected predictor. Results: In this study, 58.7 of infant mortalities occurred in rural areas, that 55.6 of them were boys. Moreover, Naive Bayes and Random Forest were highly capable of predicting related factors among data mining models. Also, the results showed that events during pregnancy such as dental disorders, high blood pressure, loss of parents, factors related to infants such as low birth weight, and factors related to mothers like consanguineous marriage and gap of pregnancy (< 3 years) were all risk factors while the age of pregnancy (18 - 35 year) and a high degree of education were protective factors. Conclusions: Infant mortality is the consequence of a variety of factors, including factors related to infants themselves and their mothers and events during pregnancy. Owing to the high accuracy and ability of modern modeling compared to traditional modeling, it is recommended to use machine learning tools for indicating risk factors of infant mortality. © 2021, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.

Background: Unintentional and intentional injuries are the most important factors that cause disability and death among children throughout the world, especially in Iran. Objectives: This study aimed to assess injury- and non-injury-related factors associated with under five mortality (U5M) at individual and community levels in Iran. Methods: This population-based case-control study recruited a total of 2,228 mothers in Iran in 2017. Subjects were divided into three groups (428 as the case group, 664 as the control group 1, and 1,136 as the control group 2). Data were extracted from health records of mothers and interviews through a researcher-made checklist in the health center of eight provinces of Iran. The multilevel logistic regression model was used to evaluate risk factors of under-five mortality at individual and community levels. Data were analyzed using R V. 3.5.0 software. Results: Analysis showed gestational age (post-term and pre-term) (OR = 4.20, CI: 3.14 - 5.61 , P < 0.001), pregnancy interval (between 1 to 3 years) (OR = 3.05, CI: 2.15 - 4.13, P < 0.001), history of mother’s diseases (OR = 3.14, CI: 2.41 - 4.10, P < 0.001), living in urban area (OR = 1.51, CI: 1.16 - 1.97, P < 0.01) and worst life events among mothers (OR = 1.91, CI: 1.46 - 2.49, P < 0.001) were risk factors associated with under-five injury-related mortality. Moreover, women’s literacy (OR = 0.72, CI: 0.54 - 0.97, P < 0.05), mothers being housewife (OR = 0.76, CI: 0.61 - 0.93, P < 0.01) were protective factor at the community level (P < 0.05). Conclusions: This study revealed factors related to under-five mortality at individual and community levels, which most of them are preventable. Therefore, child mortality can be controlled by improving family health, mothers’ education, as well as conducting interventions at the community level.

IntroductionThe World Health Organization (WHO) declared the outbreak to be a public health emergency and international concern and recognized it as a pandemic. The aim of this study was to estimate the epidemiologic parameters of novel coronavirus (COVID-19) pandemic for clinical and epidemiological help.MethodsFour electronic databases including Web of Science, Medline (PubMed), Scopus and Google Scholar were searched for literature published from early December 2019 up to 23 March 2020. The “metan” command was used to perform a fixed or random effects analysis. Cumulative meta-analysis was performed using the “metacum” command.ResultsTotally 76 observational studies were included in the analysis. The pooled estimate for R0was 2.99 (95% CI: 2.71-3.27) for COVID-19. The overall R0was 3.23, 1.19, 3.6 and 2.35 for China, Singapore, Iran and Japan, respectively. The overall Serial Interval, doubling time, incubation period were 4.45, 4.14 and 4.24 days for COVID-19. In addition, the overall estimation for growth rate and case fatality rate for COVID-19 were 0.38% and 3.29%, respectively.ConclusionCalculating the pooled estimate of the epidemiological parameters of COVID-19 as an emerging disease, could reveal epidemiological features of the disease that consequently pave the way for health policy makers to think more about control strategies.

Background: The aim of this study is to develop and validate a scoring system as a tool for predicting the in-hospital mortality in COVID-19 patients in early stage of disease. Methods: This retrospective cohort study, conducted on 893 COVID-19 patients in Tehran from February 18 to July 20, 2020. Potential factors were chosen via stepwise selection and multivariable logistic regression model. Cross-validation method was employed to assess the predictive performance of the model as well as the scoring system such as discrimination, calibration, and validity indices. Results: The COVID-19 patients’ median age was 63 yrs (54.98% male) and 233 (26.09%) patients expired during the study. The scoring system was developed based on 8 selected variables: age ≥55 yrs (OR = 5.67, 95% CI: 3.25–9.91), males (OR = 1.51, 95% CI: 1.007–2.29), ICU need (OR = 16.32, 95% CI 10.13–26.28), pulse rate >90 (OR = 1.89, 95% CI: 1.26–2.83), lymphocytes 700 U/L (OR = 1.68, 95%CI: 1.13–2.51) and troponin I level >0.03 ng/mL (OR = 1.75, 95%CI: 1.17–2.62). The AUC and the accuracy of scoring system after cross-validation were 79.4% and 79.89%, respectively. Conclusion: This study showed that developed scoring system has a good performance and can use to help physicians for identifying high-risk patients in early stage of disease.