Levels and Trends of Maternal Mortality and Morbidity by Cause in North Africa and Middle East, 1990 to 2019: An Analysis for the Global Burden of Disease Study 2019

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.

Pathogenesis of the SARS Coronavirus-2 and Potential Therapeutic Strategies

The recent outbreak of severe acute respiratory syndrome (SARS) belongs to a broad family of viruses known as Coronaviridae. SARS-CoV-2 is an emerging global pandemic with a relatively low mortality rate. The virus has been mutated in a unique manner thus prolonging its search for its vaccine and drug therapy. SARS-CoV-2 is an enveloped virus consisting of many spike (S) proteins, which mediates its fusion to the membrane of the host cell. Its ‘crown-like’ appearance under an electron microscope has led to its name. The clinical symptoms that patients experience would be due to their central immune response to the infection. Pro-inflammatory cytokines play an essential role in cell growth and regulation of the immune system. However, its abundance could contribute to pathological conditions which can cause further injury and possible death. This brief review discusses the pathogenesis of the SARS-CoV-2 along with receptors that can be potentially targeted by therapeutic strategies, inhibiting the membrane fusion, genome replication and immune response. © 2022: Author(s).

Performance Analysis of Selected Decision Tree Algorithms for Predicting Drug Adverse Reaction among COVID-19 Hospitalized Patients

Increase in drug allergies and unpleasant adverse effects caused by COVID-19 medication therapies has doubled the need for computing technologies and intelligent systems for predicting poor medication outcomes. This study aimed to construct machine learning (ML) based prediction models to better predict adverse drug effects among COVID-19 hospitalized patients. In this retrospective and single-center study, 482 hospitalized COVID-19 patients were used for analysis. First, the Chi-square test was employed to determine the most critical factors predicting adverse drug effects at P<0.05. Second, the four selected decision tree (DT) algorithms were applied to implement the model. Finally, the best DT model was acquired for predicting adverse drug effects using various performance criteria. This study showed that the 18 variables gained the Chi-square at P<0.05 as the most important factors predicting adverse drug reactions. Besides, comparing the performance of selected algorithms demonstrated that generally, the J-48 algorithm with F-Score=94.6% and AUC=0.957 was the best classifier predicting adverse drug reactions among hospitalized COVID-19 patients. Finally, it found that the J-48 algorithm enables a reasonable level of accuracy in predicting the risk of harmful drug effects among COVID-19 hospitalized patients. It potentially facilitates identifying high-risk patients and informing proper interventions by the clinicians. © 2022 by SPC (Sami Publishing Company)

Predictive modeling of hospital length of stay in COVID-19 patients using machine learning algorithms

The rapid worldwide outbreak of COVID-19 has posed serious and unprecedented challenges to healthcare systems in predicting disease behavior, consequences and resource utilization. Therefore, predicting the Length of Stay (LOS) is necessary to ensure optimal allocate of scarce hospital resources. The purpose of this research was to construct a model for predicting COVID-19 patients' hospital LOS by multiple Machine Learning (ML) algorithms. Using a single-center registry, we studied the records of 1225 laboratory-confirmed COVID-19 hospitalized patients from February 9, 2020, to December 20, 2020. The most important clinical parameters in the COVID-19 LOS prediction were identified with a correlation coefficient at the P-value< 0.2. Then, the prediction models were developed based on seven ML techniques according to selected variables. Finally, to evaluate the performances of those models several standard quantitative measures includes accuracy, sensitivity, specificity and ROC curve were used to evaluate the proposed predictive models. After implementing feature selection, a total of 20 variables was identified as the most relevant predictors to build the prediction models. The results indicated that the best performance belonged to the Support Vector Machine (SVM) algorithm with the mean accuracy of 99.5%, mean specificity of 99.7%, mean sensitivity of 99.4%, and the standard deviation of 1.2. The SVM provided a reasonable level of accuracy and certainty in predicting the LOS in COVID-19 patients and potentially facilitates hospital bed management, turnover and optimized resource allocation. © 2021 by SPC (Sami Publishing Company).

The Impact of the Social Distancing Policy on COVID-19 Incidence Cases and Deaths in Iran from February 2020 to January 2021: Insights from an Interrupted Time Series Analysis

Background: In December 2019, a viral outbreak occurred in China, and rapidly spread out worldwide. Due to the lack of immediately available vaccines and effective drugs, many policy- and decision-makers have focused on non-pharmacological methods, including social distancing. This study was aimed at assessing the effects of the implementation of this policy in Iran, one of the countries most affected by COVID-19. We conducted a quasi-experimental study, utilizing the interrupted time series analysis (ITSA) approach. Methods: We collected daily data between February 20, 2020 and January 29, 2021, through governmental websites from 954 public hospitals and healthcare settings. The Iranian government launched the social distancing policy on March 27, 2020. Statistical analyses, including ITSA, were carried out with R software Version 3.6.1 (London, UK). Results: During the study period, 1,398,835 confirmed incidence cases and 57,734 deaths occurred. We found a decrease of -179.93 (95% CI: -380.11 to -20.25, P-value=0.078) confirmed incidence cases following the implementation of the social distancing policy, corresponding to a daily decrease in the trend of -31.17 (95% CI: -46.95 to -15.40, P-value=0.08). Moreover, we found a decrease of -28.28 (95% CI: -43.55 to -13.01, P-value=0.05) deaths, corresponding to a daily decrease in the trend of -4.52 (95% CI: -5.25 to -3.78, P-value=0.003). Conclusion: The growth rate of confirmed incidence cases and deaths from COVID-19 in Iran has decreased from March 27, 2020 to January 29, 2021, after the implementation of social distancing. By implementing this policy in all countries, the burden of COVID-19 may be mitigated.