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Background: Since 1990, the maternal mortality significantly decreased at global scale as well as the North Africa and Middle East. However, estimates for mortality and morbidity by cause and age at national scale in this region are not available. Method(s): This study is part of the Global Burden of Diseases, Injuries, and Risk Factors study (GBD) 2019. Here we report maternal mortality and morbidity by age and cause across 21 countries in the region from 1990 to 2019. Result(s): Between 1990 and 2019, maternal mortality ratio (MMR) dropped from 148.8 (129.6-171.2) to 94.3 (73.4-121.1) per 100 000 live births in North Africa and Middle East. In 1990, MMR ranged from 6.0 (5.3-6.8) in Kuwait to 502.9 (375.2-655.3) per 100 000 live births in Afghanistan. Respective figures for 2019 were 5.1 (4.0-6.4) in Kuwait to 269.9 (195.8-368.6) in Afghanistan. Percentages of deaths under 25 years was 26.0% in 1990 and 23.8% in 2019. Maternal hemorrhage, indirect maternal deaths, and other maternal disorders rank 1st to 3rd in the entire region. Ultimately, there was an evident decrease in MMR along with increase in socio-demographic index from 1990 to 2019 in all countries in the region and an evident convergence across nations. Conclusion(s): MMR has significantly declined in the region since 1990 and only five countries (Afghanistan, Sudan, Yemen, Morocco, and Algeria) out of 21 nations didn't achieve the Sustainable Development Goal (SDG) target of 70 deaths per 100 000 live births in 2019. Despite the convergence in trends, there are still disparities across countries.Copyright © 2022 Academy of Medical Sciences of I.R. Iran. All rights reserved.
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Background: Technologies can predict various aspects of COVID-19, such as early prediction of cases and those at higher risks of severe disease. Predictions will yield numerous benefits and can result in a lower number of cases and deaths. Herein, we aimed to review the published models and techniques that predict various COVID-19 outcomes and identify their role in the management of the COVID-19. Methods: This study was a review identifying the prediction models and techniques for management of the COVID-19. Web of Science, Scopus, and PubMed were searched from December 2019 until September 4th, 2021. In addition, Google Scholar was also searched. Results: We have reviewed 59 studies. The authors reviewed prediction techniques in COVID-19 disease management. Studies in these articles have shown that in the section medical setting, most of the subjects were inpatients. In the purpose of the prediction section, mortality was also the most item. In the type of data/predict section, basic patient information, demographic, and laboratory values were the most cases. Also, in the type of technique section, logistic regression was the most item used. Training, internal and external validation, and cross-validation were among the issues raised in the type of validation section. Conclusion: Artificial intelligence and machine learning methods were found to be useful in disease control and prevention. They accelerate the process of diagnosis and move toward great progress in emergency circumstances like the COVID-19 pandemic. Copyright © 2023, Journal of Iranian Medical Council. All rights reserved. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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Background: To find different variables involved in the hospitalization of patients referred to the respiratory Emergency Room (ER) at the time of COVID-19 pandemic. Methods: A questionnaire was designed to determine different hypothetical factors involved in the hospitalization of 3481 patients during the COVID-19 pandemic. Results: This study suggests that the following factors/variables are involved in the hospitalization of patients: age, respiratory distress, duration of symptoms, cough, 5-day-prolonged fever, diabetes mellitus, respiratory diseases, renal conditions, history of chemotherapy, saturation of blood oxygen, sore throat, and fever. Conclusion: We found that through having an insight towards what may and may not be involved in the severity of the novel coronavirus infection, one might be able to decide if a patient could potentially benefit from hospitalization. Copyright 2022, Journal of Iranian Medical Council. All rights reserved.
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Background: COVID-19 has led to the demand for finding effective antiviral agents. Preliminary experiments showed Umifenovir inhibit replication in vivo. There is limited data on the clinical efficacy of COVID-19-infected pneumonia. Therefore, we aimed to evaluate this medication based on clinical findings. Methods: The present study was designed to investigate the advantages and disadvantages of Umifenovir and compared to empirical treatments. For this purpose, multi-stage sampling was considered. 56 people who had mild-to-moderate symptoms without signs of pneumonia, were selected by accidental non-random sampling method and divided into two groups [(group A with Hydroxychloroquine (HCQ) and group B in combination with Umifenovir] by randomized block sampling (1:1). During the study, three patients left the case group. Their clinical signs and symptoms were evaluated on 3rd, 7th, and 14th day after taking these medicines in the disease course. The SPSS software was used for data analysis and the significance level was considered to be p<0.05. Results: On the seventh day after visiting the patients, there were statistically significant differences in recuperation dry cough (p=0.001), weakness (p=0.004), gastrointestinal symptoms (p=0.043) and shortness of breath (p=0.001) between the two groups so that group B patients (HCQ and Umifenovir) had a faster recovery. In patients treated with HCQ and Umifenovir compared to the control group, myalgia (p=0.03), gastrointestinal symptoms (p=0.047) and weakness (p=0.007) improved significantly earlier during the illness. Conclusion: Evaluation of the clinical findings in mild-to-moderate COVID-19 patients' symptoms was performed and it was shown that recuperation was faster in the group who received both HCQ and Umifenovir. Copyright 2022, Journal of Iranian Medical Council. All rights reserved.
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Background: Returning symptomatic patients with a history of recovered COVID-19 with a new positive SARS CoV-2 PCR test after several weeks to months of a negative PCR result is challenging during the COVID-19 pandemic. Objectives: We aimed to determine such Iranian patients' clinical and laboratory characteristics and discuss possible reasons.
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We report a case series of nine patients with confirmed coronavirus disease 2019 (COVID-19) in Tehran, Iran. This paper also reports narrative information on the signs and symptoms of the patients and discusses the potential immunologic perspective of the disease, especially with respect to the cytokine storm.
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Introduction: COVID-19 is a new rapidly spreading epidemic. The symptoms of this disease could be diverse as the virus can affect any organ in the body of an infected person. This study aimed to investigate the available evidence for long-term complications of COVID-19. Methods: This study was a systematic review of current evidence conducted in November 2020 to investigate probable late and long-term complications of COVID-19. We performed a systematic search, using the keywords, in online databases including PubMed, Scopus, Science Direct, Up to Date, and Web of Science, to find papers published from December 2019 to October 2020. Peer-reviewed original papers published in English, which met the eligibility criteria were included in the final report. Addressing non-human studies, unavailability of the full-text document, and duplicated results in databases, were characteristics that led to exclusion of the papers from review. Results: The full-texts of 65 articles have been reviewed. We identified 10 potential late complications of COVID-19. A review of studies showed that lung injuries (n=31), venous/arterial thrombosis (n=28), heart injuries (n=26), cardiac/brain stroke (n=23), and neurological injuries (n=20) are the most frequent late complications of COVID-19. Conclusion: Since we are still at the early stages of the COVID-19 epidemic, it is too soon to predict what long-term complications are likely to appear in the survivors of the disease in years after recovery. Furthermore, the complexity of COVID-19 behaviors and targets in the human body creates uncertainty in anticipating long-term complications.