ABSTRACT
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.
ABSTRACT
Introduction: The World Health Organization on March 11, 2020 declared the outbreak of severe acute respiratory syndrome Corona virus 2 disease (COVID-19) a pandemic situation. The main aim of this study was investigating mortality of COVID 19 by considering chronic diseases. Materials and methods: this study was conducted as a cross-sectional in which all confirmed cases were examined. The variables considered in this study were age, sex, diabetes mellitus, cancers, hypertension, heart diseases, kidney diseases, and liver diseases. Independent sample t test, Chi-square and binary logistic regression were used to data analysis. All statistical analysis was done in SPSS 16 and significant level was set at 0.05.
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Introduction: Yazd province is the center of Iran and the highway for travelers to other cities. This province is susceptible to disease transmission in Iran. This study aimed to spatial analysis of corona virus prevalence, predicting the spread and determination of hot spot areas in Yazd province, central part of Iran.
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This document summarizes how during the disease epidemic in Iran, the government first considered emergency measures for the center of the disease epidemic, the Qom province. And after that by observing new cases in Tehran, Gilan, and Mazandaran provinces, which are the adjacent provinces to Qom province, the emergency measures for these three provinces were considered as well. Severe travel restrictions, preventing cars with non -native license plates from entering center of the provinces, restrictions on the reopening of guilds, passages, shopping and entertainment centers, reducing working hours of some guilds,closure of mosques and holy places, teleworking of government employees, preventing the reopening of schools and universities, strict restrictions in banks and imposing night traffic ban from 9 p.m. were effective measures of the Ministry of Health and the government.
ABSTRACT
Introduction: The Coronavirus has crossed geographical borders. This study was performed to rank and cluster Iranian provinces based on coronavirus disease (COVID-19) recorded cases from February 19 to March 22, 2020. Materials and Methods: This cross-sectional study was conducted in 31 provinces of Iran using the daily number of confirmed cases. Cumulative Frequency (CF) and Adjusted CF (ACF) of new cases for each province were calculated. Characteristics of provinces like population density, area, distance from the original epicenter (Qom province), altitude from sea level, and Human Development Index (HDI) were used to investigate their correlation with ACF values. Spearman correlation coefficient and K-Means Cluster Analysis (KMCA) were used for data analysis. Statistical analyses were conducted in RStudio. The significant level was set at 0.05. Results: There were 21,638 infected cases with COVID-19 in Iran during the study period. Significant correlations between ACF values and province HDI (r = 0.46) and distance from the original epicenter (r = -0.66) was observed. KMCA, based on both CF and ACF values, classified provinces into 10 clusters. In terms of ACF, the highest level of spreading belonged to cluster 1 (Semnan and Qom provinces), and the lowest one belonged to cluster 10 (Kerman, Sistan and Baluchestan, Chaharmahal and Bakhtiari and Busher provinces). Conclusion: This study showed that ACF gives a real picture of each province's spreading status. KMCA results based on ACF identify the provinces that have critical conditions and need attention. Therefore, using this accurate model to identify hot spots to perform quarantine is recommended. © 2021. All Rights Reserved.