ABSTRACT
Objectives: This article reports medical interns' knowledge, attitude and practice (KAP) toward COVID-19 prevention measures in the Kingdom of Saudi Arabia (KSA). Design(s): We conducted a cross-sectional online survey. The questionnaire included 10 questions each to assess knowledge and attitudes, and seven questions to assess practice. We did descriptive analyses to report KAP and performed t-test or ANOVA, and multi-variable logistic regression analyses to investigate socio-demographic determinants of KAP. Setting(s): All regions in the KSA Subjects: Medical interns from all medical colleges in the KSA were invited to participate Intervention: Not applicable Main Outcome Measure(s): Attitude about COVID-19 was assessed as positive (>=90% correct responses), moderate (80-90% correct) or poor (< 80% correct);whereas knowledge and practice were assessed as excellent, good or poor respectively for >=90%, 75%-90%, and <75% correct responses. Result(s): Our results suggest that 24% of medical interns rely on social media, television, or friends as primary source of COVID-19 information. The prevalence of positive attitude, excellent knowledge and excellent/good practices are 55.2%, 38% and 24%, respectively. Graduating from government universities are associated with higher odds of excellent knowledge [Odds Ratio (OR): 3.87;95% Confidence Interval (CI): 1.05-14.22] and positive attitude [OR: 4.84 (1.28-18.23)]. Interns from the west [OR: 2.35 (1.05-5.23)] and north [OR: 3.2 (1.32-7.75)] regions have higher odds of excellent/good practice compared to the central region. Conclusion(s): Our findings reveal gaps in KAP among medical interns. Medical interns in the KSA are not deployed as front-line health workers to combat COVID-19. However, community transmission of COVID-19 makes it critical to improve KAP of medical interns toward COVID-19 prevention measures.Copyright © 2023, Kuwait Medical Association. All rights reserved.
ABSTRACT
The death t toll of the coronavirus disease 2019 (COVID-19) has been considerable. Several risk factors have been linked to mortality due to COVID-19 in hospitals. This study aimed to describe the clinical characteristics of patients who either died from COVID-19 at Dhaka Medical College Hospital in Bangladesh. In this retrospective study, we reviewed the hospital records of patients who died or recovered and tested positive for COVID-19 from May 3 to August 31, 2020. All patients who died during the study period were included in the analysis. A comparison group of patients who survived COVID-19 at the same hospital during the same period was systematically sampled. All available information was retrieved from the records, including demographic, clinical, and laboratory variables. Of the 3115 patients with confirmed COVID-19 during the study period, 282 died.The mean age of patients who died was higher than that of those who survived (56.7 vs 52.6 years). Approximately three-fourths of deceased patients were male. History of smoking (risk ratio 2.3;95% confidence interval: 1.6-3.4), comorbidities (risk ratio: 1.5;95% confidence interal:1.1-2.1), chronic kidney disease (risk ratio: 3.2;95% confidence interval: 1.7-6.25), and ischemic heart disease (risk ratio:1.8;95% confidence interval: 1.1-2.9) were higher among the deceased than among those who survived. Mean C-reactive protein and D-dimer levels [mean (interquartile range), 34 (21-56) vs. 24 (12-48);and D-dimer [1.43 (1-2.4) vs. 0.8 (0.44-1.55)] were higher among those who died than among those who recovered. Older age, male sex, rural residence, history of smoking, and chronic kidney disease were found to be important predictors of mortality. Early hospitalization should be considered for patients with COVID-19 who are older, male, and have chronic kidney disease. Rapid referral to tertiary care facilities is necessary for high-risk patients in rural settings.Copyright © 2023 Hoque MM.
ABSTRACT
COVID-19 illness has a detrimental impact on the respiratory system, and the severity of the infection may be determined utilizing a selected imaging technique. Chest computer tomography (CT) imaging is a reliable diagnostic technique for finding COVID-19 early and slowing its progression. Recent research shows that deep learning algorithms, particularly convolutional neural network (CNN), may accurately diagnose COVID-19 using lung CT scan images. But in an emergency, detection accuracy simply is not enough. Determinants of data loss and classification completion time play a critical element. This study addresses the issue by finding the most efficient CNN model with the least data loss and classification time. Eight deep learning models, including Max Pooling 2D, Average Pooling 2D, VGG19, VGG16, MobileNetV2, InceptionV3, AlexNet, NFNet using a dataset of 16000 CT scans image data of COVID-19 and non-COVID-19 are compared in the study. Using the confusion matrix, the performance of the models is compared and together with the data loss and completion time. It is observed from the research that MobileNetV2 provides the highest accurate result of 99.12% with the least data loss of 0.0504% in the lowest classification completion time of 16.5secs per epoch. Thus, employing MobileNetV2 gives the best and the quickest result in an emergency. © 2022 Institute of Advanced Engineering and Science. All rights reserved.
ABSTRACT
Background: COVID-19 pandemic has caused an unprecedented strike on humanity around the world. The scenario in Bangladesh is getting worse day by day, and every aspect of the society is observing its impact. Health care professionals are at a greater risk of contracting the disease while caring for patients. Objective: The research objective is to explore knowledge, awareness, and practices of registered dentists regarding COVID-19 epidemiology and transmission during the rapid outbreak of this highly contagious virus in Bangladesh. Material and Methods: A cross-sectional web-based survey was conducted among the dentists who were enrolled with their valid unique Bangladesh Medical and Dental Council (BMDC) registration number. A structured questionnaire was distributed among the dentists through different social media platforms. A total of 184 dentists participated in the survey between March and April 2020. Both descriptive analysis and multivariable logistic regression analysis was performed. Results: The dentists' mean age was 31.75 years, with a standard deviation of 6.5 years. About 29.3% of dentists completed their postgraduate qualification, and 76% of them were engaged in private practice at the time of data collection. Compared to the dentists with undergraduate education, the dentists with a postgraduate education are three times (OR=3.1, 95%CI 1.2-7.9 and over 5 times (OR=5.3, 95% CI: 1.2-23.3) more likely to have) better knowledge and practices toward COVID-19 respectively. Dentists aged 26-30 years are less likely to have good practices than the younger dentists (OR:.1;95% CI:.01-.5). However, dentists with less than five years experience are 10.3 (1.6-68.9) times more likely to have good practices compared to the dentists with more experience. Conclusion: Majority of the dentists from Bangladesh have shown good knowledge, awareness, and practice regarding COVID-19. We recommend that the healthcare authorities, professional organizations, and hospitals coordinate, and conduct mandatory advanced infectious disease training for all the practicing dentists in the country. © 2021, Universidad de Concepcion. All rights reserved.
ABSTRACT
The outbreak of Coronavirus Disease 2019 (Covid-19) had an enormous impact on humanity. Till May 2021, almost 172 million people have been affected globally due to the contagious spread of Covid-19. Although the distribution of vaccines has been started, the worldwide mass distribution is yet to happen. According to the World Health Organization (WHO), wearing a facemask can reduce the contagious spread of Covid-19 significantly. The governments of different countries have recommended implementing the "no mask, no service" method to impede the spread of Covid-19. However, even the improper wearing of a facemask can obstruct the goal and lead to the spread of the virus. Therefore, to ensure public safety, a system for monitoring facemasks on faces, commonly known as a facemask detection algorithm, is essential for overcoming this crisis. The facemask detection algorithms are part of the object detection algorithms which are used to detect objects in an image. Among the various object detection algorithms, deep learning showed tremendous performance in facemask detection for its excellent feature extraction capability than the traditional machine learning algorithms. However, there remains a lot of scope for future research to build an efficient facemask detection system. Therefore, this study aims to draw attention to the researchers by providing a narrative and meta-analytic review on all the published works related to facemask detection in the context of Covid-19. Because facemask detection algorithms are run mainly by adopting object detection algorithms, this paper also explores the progress of object detection algorithms over the last few decades. A comprehensive analysis of different datasets used in facemask detection techniques by many studies has been explored. The performance comparison among these algorithms is discussed in narrative and meta-analytic approaches. Finally, this study concludes with a discussion of some of the major challenges and future scope in the related field.