Background: Following the emergency approval of the coronavirus disease 2019 (COVID-19) vaccines, research into its vaccination hesitancy saw a substantial increase. However, the psychological behaviors associated with this hesitancy are still not completely understood. This study assessed the psychological antecedents associated with COVID-19 vaccination in the Arab population. Methodology: The validated Arabic version of the 5C questionnaire was distributed online across various social media platforms in Arabic-speaking countries. The questionnaire had three sections, namely, socio-demographics, COVID-19 related infection and vaccination, and the 5C scale of vaccine psychological antecedents of confidence, complacency, constraints, calculation, and collective responsibility. Results: In total, 4,474 participants with a mean age of 32.48 ± 10.76 from 13 Arab countries made up the final sample, 40.8% of whom were male. Around 26.7% of the participants were found to be confident about the COVID-19 vaccination, 10.7% indicated complacency, 96.5% indicated they had no constraints, 48.8% had a preference for calculation and 40.4% indicated they had collective responsibility. The 5C antecedents varied across the studied countries with the confidence and collective responsibility being the highest in the United Arab Emirates (59.0% and 58.0%, respectively), complacency and constraints in Morocco (21.0% and 7.0%, respectively) and calculation in Sudan (60.0%). The regression analyses revealed that sex, age, educational degrees, being a health care professional, history of COVID-19 infection and having a relative infected or died from COVID-19 significantly predicted the 5C psychological antecedents by different degrees. Conclusion: There are wide psychological antecedent variations between Arab countries, and different determinants can have a profound effect on the COVID-19 vaccine’s psychological antecedents.

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.

Vaccine hesitancy (VH) is defined as a delayed in acceptance or refusal of vaccines despite availability of vaccination services. This multinational study examined user interaction with social media about COVID-19 vaccination. The study analyzed social media comments in 24 countries from five continents. In total, 5856 responses were analyzed; 83.5% of comments were from Facebook, while 16.5% were from Twitter. In Facebook, the overall vaccine acceptance was 40.3%; the lowest acceptance rates were evident in Jordan (8.5%), Oman (15.0%), Senegal (20.0%) and Morocco (20.7%) and the continental acceptance rate was the lowest in North America 22.6%. In Twitter, the overall acceptance rate was (41.5%); the lowest acceptance rate was found in Oman (14.3%), followed by USA (20.5%), and UK (23.3%) and the continental acceptance rate was the lowest in North America (20.5%), and Europe (29.7%). The differences in vaccine acceptance across countries and continents in Facebook and Twitter were statistically significant. Regarding the tone of the comments, in Facebook, countries that had the highest number of serious tone comments were Sweden (90.9%), USA (61.3%), and Thailand (58.8%). At continent level, serious comments were the highest in Asia (58.4%), followed by Africa (46.2%) and South America (46.2%). In Twitter, the highest serious tone was reported in Egypt (72.2%) while at continental level, the highest proportion of serious comments was observed in Asia (59.7%), followed by Europe (46.5%). The differences in tone across countries and continents in Facebook and Twitter and were statistically significant. There was a significant association between the tone and the position of comments. We concluded that the overall vaccine acceptance in social media was relatively low and varied across the studied countries and continents. Consequently, more in-depth studies are required to address causes of such VH and combat infodemics.

Background: Female genital mutilation (FGM) is a human rights violation of girls and women and quite common in several countries, including Egypt. Objective(s): To estimate the prevalence of FGM among Egyptian females, to assess their knowledge, attitude, and intention to practice FGM, and to determine their possible correlates. Methods: A total of 770 females aged 15 - 49 years attending the family health centers at five randomly selected Egyptian governorates representing the different provinces of Egypt were included. A cross-sectional study design was used. A predesigned structured interviewing questionnaire was utilized to collect data on socio-demographic characteristics, females’ knowledge and attitude regarding FGM, and their intention to practice FGM. Special scoring systems were developed regarding knowledge and attitude. Results: The prevalence of FGM was 74.2%. Only 5.7% of females had a good level of knowledge, 65.3% had a favorable attitude regarding (i.e. were against) FGM, and 31.8% reported that they intended to practice FGM. Logistic regression models showed that younger age, rural area of residence, and level of education (illiteracy or just reading and writing) were significantly associated with a poor level of knowledge, and level of education, marital status, exposure to mutilation, and level of knowledge significantly affected their attitude. Significant predictors of females’ intention to practice were their mutilation experience, level of knowledge, and attitude and husband’s pressure to perform FGM. Conclusion: FGM remains common in Egypt. Poor knowledge and unfavorable attitude had a positive significant correlation, and both were among the predictors of females’ intention to practice mutilation. Males played a role in the continuation of practice in the community. Awareness campaigns and law enforcement may help reduce the practice of FGM in Egypt.

Congenital anomalies (CAs) are structural, functional, or metabolic anomalies that originate during intrauterine life and can interfere with the body functions. In Egypt, the prevalence of CAs is increasing. The study aimed to estimate the frequency, describe the types, and identify the possible risk factors of CAs among infants attending the Pediatric University Hospital, Alexandria, Egypt. A retrospective case series and a case-control study were conducted. Patients’ records for the years 2010–2015 were reviewed, and a sample of 200 infants (100 cases and 100 controls) was taken from infants presented to Pediatrics, Pediatric Surgery, and Genetics Clinics of the hospital. Data were collected using a record review checklist and a predesigned interviewing questionnaire. The study revealed that congenital anomalies of the digestive system (38.0%), musculoskeletal system (32.9%), and circulatory system (11.0%) were the most common types of CAs. Males were more affected with CAs than females (63% versus 37%). The major risk factors for CAs were old-aged parents, complications during pregnancy, unprescribed medications and excessive vitamin A intake during pregnancy, exposure to chemicals and pesticides during pregnancy, and living near mobile strengthening stations. Congenital malformations of the digestive, musculoskeletal, and circulatory systems were the most common types of CAs in the Pediatric Hospital. To prevent CAs, there is a need to restrict the prescription of medications that may have a teratogenic effect.

Machine learning (ML) is an artificial intelligence tool that focuses on learning by generating models using established algorithms that represent a given dataset. It can be used as a predictive tool for students’ academic performance (AP) at both undergraduate and postgraduate levels. A cross-sectional analysis was conducted using academic records of 922 postgraduate students admitted to the High Institute of Public Health, Alexandria University, Egypt, between 2020–2024. Data included 22 features spanning pre-enrollment metrics, academic performance, and demographic traits. Classification algorithms, and regression models were trained on 75% of the dataset, validated via 5-fold cross-validation. Performance metrics included accuracy, precision, recall, AUC for classification, and MAE, RMSE, and R² for regression. Regression models outperformed classification models in AP prediction, with Ensemble (Soft Voting) achieving the highest accuracy (74.25%), lowest MAE (0.3383), and RMSE (0.4316). Among classification models, Random Forest demonstrated superior accuracy (71.43%) and AUC (0.87). Numerical features like the number of failed courses showed the strongest negative correlation with AP ( r = -0.37). Key predictors included bachelor’s university, major, department, and pre-enrollment CGPA. Feature importance analysis highlighted failed courses as the top determinant, followed by institutional and academic background variables. Regression-based ML models, particularly Ensemble (Soft Voting), proved more effective than classification approaches for predicting nuanced variations in AP. These findings enable institutions to prioritize early interventions for at-risk students, and optimize resource allocation. However, moderate R² values (0.3832) underscore the need to integrate psychosocial and behavioral factors in future studies.