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- Ghazy, Ramy Mohamed, et al.
(författare)
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Acceptance of COVID-19 Vaccine Booster Doses Using the Health Belief Model : A Cross-Sectional Study in Low-Middle- and High-Income Countries of the East Mediterranean Region
- 2022
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Ingår i: International Journal of Environmental Research and Public Health. - : MDPI AG. - 1661-7827 .- 1660-4601. ; 19:19
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Tidskriftsartikel (refereegranskat)abstract
- Coronavirus disease (COVID-19) booster doses decrease infection transmission and disease severity. This study aimed to assess the acceptance of COVID-19 vaccine booster doses in low, middle, and high-income countries of the East Mediterranean Region (EMR) and its determinants using the health belief model (HBM). In addition, we aimed to identify the causes of booster dose rejection and the main source of information about vaccination. Using the snowball and convince sampling technique, a bilingual, self-administered, anonymous questionnaire was used to collect the data from 14 EMR countries through different social media platforms. Logistic regression analysis was used to estimate the key determinants that predict vaccination acceptance among respondents. Overall, 2327 participants responded to the questionnaire. In total, 1468 received compulsory doses of vaccination. Of them, 739 (50.3%) received booster doses and 387 (26.4%) were willing to get the COVID-19 vaccine booster doses. Vaccine booster dose acceptance rates in low, middle, and high-income countries were 73.4%, 67.9%, and 83.0%, respectively (p < 0.001). Participants who reported reliance on information about the COVID-19 vaccination from the Ministry of Health websites were more willing to accept booster doses (79.3% vs. 66.6%, p < 0.001). The leading causes behind booster dose rejection were the beliefs that booster doses have no benefit (48.35%) and have severe side effects (25.6%). Determinants of booster dose acceptance were age (odds ratio (OR) = 1.02, 95% confidence interval (CI): 1.01–1.03, p = 0.002), information provided by the Ministry of Health (OR = 3.40, 95% CI: 1.79–6.49, p = 0.015), perceived susceptibility to COVID-19 infection (OR = 1.88, 95% CI: 1.21–2.93, p = 0.005), perceived severity of COVID-19 (OR = 2.08, 95% CI: 137–3.16, p = 0.001), and perceived risk of side effects (OR = 0.25, 95% CI: 0.19–0.34, p < 0.001). Booster dose acceptance in EMR is relatively high. Interventions based on HBM may provide useful directions for policymakers to enhance the population’s acceptance of booster vaccination.
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- Ghazy, Ramy Mohamed, et al.
(författare)
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Quality of life among health care workers in Arab countries 2 years after COVID-19 pandemic
- 2022
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Ingår i: Frontiers in Public Health. - : Frontiers Media SA. - 2296-2565. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Background: Assessment of the quality of life (QoL) among healthcare workers (HCWs) is vital for better healthcare and is an essential indicator for competent health service delivery. Since the coronavirus disease 2019 (COVID-19) pandemic strike, the frontline position of HCWs subjected them to tremendous mental and psychological burden with a high risk of virus acquisition. Aim: This study evaluated the QoL and its influencing factors among HCWs residing in the Arab countries. Methods: This was a cross-sectional study using a self-administered online questionnaire based on the World Health Organization QoL-BREF instrument with additional questions related to COVID-19. The study was conducted in three different languages (Arabic, English, and French) across 19 Arab countries between February 22 and March 24, 2022. Results: A total of 3,170 HCWs were included in the survey. The majority were females (75.3%), aged 18–40 years (76.4%), urban residents (90.4%), married (54.5%), and were living in middle-income countries (72.0%). The mean scores of general health and general QoL were 3.7 ± 1.0 and 3.7 ± 0.9, respectively. Those who attained average physical, psychological, social, and environmental QoL were 40.8, 15.4, 26.2, and 22.3%, respectively. The income per capita and country income affected the mean scores of all QoL domains. Previous COVID-19 infection, having relatives who died of COVID-19, and being vaccinated against COVID-19 significantly affected the mean scores of different domains. Conclusion: A large proportion of the Arab HCWs evaluated in this study had an overall poor QoL. More attention should be directed to this vulnerable group to ensure their productivity and service provision.
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- Wakili, Musa Adamu, et al.
(författare)
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Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning
- 2022
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Ingår i: Computational Intelligence and Neuroscience. - : Hindawi Publishing Corporation. - 1687-5265 .- 1687-5273. ; 2022
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Tidskriftsartikel (refereegranskat)abstract
- Breast cancer is one of the most common invading cancers in women. Analyzing breast cancer is nontrivial and may lead to disagreements among experts. Although deep learning methods achieved an excellent performance in classification tasks including breast cancer histopathological images, the existing state-of-the-art methods are computationally expensive and may overfit due to extracting features from in-distribution images. In this paper, our contribution is mainly twofold. First, we perform a short survey on deep-learning-based models for classifying histopathological images to investigate the most popular and optimized training-testing ratios. Our findings reveal that the most popular training-testing ratio for histopathological image classification is 70%: 30%, whereas the best performance (e.g., accuracy) is achieved by using the training-testing ratio of 80%: 20% on an identical dataset. Second, we propose a method named DenTnet to classify breast cancer histopathological images chiefly. DenTnet utilizes the principle of transfer learning to solve the problem of extracting features from the same distribution using DenseNet as a backbone model. The proposed DenTnet method is shown to be superior in comparison to a number of leading deep learning methods in terms of detection accuracy (up to 99.28% on BreaKHis dataset deeming training-testing ratio of 80%: 20%) with good generalization ability and computational speed. The limitation of existing methods including the requirement of high computation and utilization of the same feature distribution is mitigated by dint of the DenTnet. © 2022 Musa Adamu Wakili et al.
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