ABSTRACT
Background: The outbreak of COVID-19 has a serious crisis for health systems in different countries. This study aimed to investigate the association between COVID-19 anxiety and cognitive failure and temperament components in the personnel of a hospital in the Yazd Province. Materials and Methods: This was a cross-sectional study. The participants were the personnel of a hospital. The convenience sampling method was used for the sampling purpose. The participants were the personnel of COVID-19 and non-COVID-19 sections of a hospital. Data collection tools included a demographic questionnaire, the Corona Disease Anxiety Scale (CDAS), the Cognitive Failure Questionnaire (CFQ), and the Emotionality Activity Sociability (EAS) Questionnaire. Data analysis was performed in SPSS-24 software. Results: The mean age of the participants was 33.45 (6.42) years. COVID-19 anxiety was significantly higher in the non-COVID-19 personnel than in the COVID-19 personnel (P < 0.001). In addition, a positive correlation was found between COVID-19 anxiety scores and cognitive failure scores (P = 0.04). After investigating the simultaneous relation of COVID-19 anxiety with cognitive failure and temperament components, the model results showed that cognitive failure (P = 0.02) and sociability (P < 0.001) had a significant effect on COVID-19 anxiety. Conclusions: High levels of COVID-19 anxiety in non-COVID-19 section personnel indicated the importance of paying more attention to all hospital occupations. To reduce cognitive failure and anxiety, it is recommended to provide psychological training, workloads reduction, and the number of personnel be increased. © The Author(s) 2022;All rights reserved. Published by Rafsanjan University of Medical Sciences Press.
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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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.