Impact of COVID-19 pandemic on the mental health of university students in the United Arab Emirates: a cross-sectional study.

BACKGROUND: The COVID-19 pandemic had a significant impact on the mental well-being of students worldwide. There is a scarcity of information on the mental health impact of the COVID-19 pandemic on university students in the United Arab Emirates (UAE). This study aimed to investigate the mental health impact of the COVID-19, including depression, anxiety and resilience among a sample of university students in the UAE. METHODS: A cross-sectional study using an online survey was conducted from September to November 2021. The patient health questionnaire (PHQ-9), generalized anxiety disorder-7 (GAD-7) and Connor-Davidson Resilience Scale (CD-RISC-10) were used to assess depression, anxiety, and resilience. The COVID-19 impact was assessed using a list of questions. RESULTS: Only, 798 students completed the survey and were analyzed for this study. Overall, 74.8% of the students were females, 91.2% were never married, and 66.3% were UAE-nationals. Based on PHQ-9 and GAD-7 cut-off scores (≥ 10), four out of ten of the students self-reported moderate to severe depression (40.9%) and anxiety (39.1%). Significantly higher mean PHQ-9 and GAD-7 scores were found among students who were impacted by COVID-19 than those non-impacted (mean PHQ-9 = 9.51 ± 6.39 and 6.80 ± 6.34; p = 0.001, respectively) and (mean GAD-7 = 9.03 ± 6.00 and 8.54 ± 6.02; respectively, p < 0.001). Female students who were impacted by COVID-19 had statistically significant higher depression and anxiety scores (mean PHQ-9 of 9.14 ± 5.86 vs. 6.83 ± 6.25, respectively; p < 0.001) than the non-impacted females (mean GAD-7 of 9.57 ± 6.32 vs. 5.15 ± 3.88, respectively; p = 0.005). Never married students had significantly higher PHQ-9 and GAD-7 scores than ever-married (9.31 ± 6.37 vs. 6.93 ± 5.47, P = 0.003) and (8.89 ± 6.11 vs. 7.13 ± 5.49, respectively; p = 0.017). CONCLUSIONS: The results of this study demonstrate that the COVID-19 pandemic has negatively impacted the mental health of this sample of university students in terms of depression and anxiety. The results highlight the need to adopt culturally appropriate interventions for university students and focus on vulnerable groups.

Doctor for Every Citizen: Telehealth Visits at Dubai Health Authority during COVID-19 Pandemic in Dubai, United Arab Emirates.

BACKGROUND: Digital health significantly affects healthcare delivery. Moreover, empirical studies on the utilization of telehealth in Dubai are limited. Accordingly, this study examines the utilization of telehealth services in Dubai Health Authority (DHA) facilities and the factors associated with telehealth appointment completion and turnaround time. METHODS: This cross-sectional study examines patients who used telehealth services in DHA from 2020 through 2021 using 241,822 records. A binary logistic regression model was constructed to investigate the association between appointment turnaround time as a dependent variable and patient and visit characteristics as independent variables. RESULTS: Of the total scheduled telehealth visits, more than three-quarter (78.55%) were completed. Older patients, non-Emiratis, patients who had their visits in 2020, patients who had video visits, and those who sought family medicine as a specialty had a shorter turnaround time to receive their appointment. CONCLUSIONS: This study identifies several characteristics associated with the turnaround time. Moreover, technological improvements focusing on specialties that can readily be addressed through telehealth and further research in this domain will improve service provision and support building an evidence-base in the government sector of the emirate of Dubai.

Kumaraswamy Inverted Topp–Leone Distribution with Applications to COVID-19 Data

In this paper, an attempt is made to discover the distribution of COVID-19 spread in different countries such as;Saudi Arabia, Italy, Argentina and Angola by specifying an optimal statistical distribution for analyzing the mortality rate of COVID-19. A new generalization of the recently inverted Topp Leone distribution, called Kumaraswamy inverted Topp–Leone distribution, is proposed by combining the Kumaraswamy-G family and the inverted Topp–Leone distribution. We initially provide a linear representation of its density function. We give some of its structure properties, such as quantile function, median, moments, incomplete moments, Lorenz and Bonferroni curves, entropies measures and stress-strength reliability. Then, Bayesian and maximum likelihood estimators for parameters of the Kumaraswamy inverted Topp–Leone distribution under Type-II censored sample are considered. Bayesian estimator is regarded using symmetric and asymmetric loss functions. As analytical solution is too hard, behaviours of estimates have been done viz Monte Carlo simulation study and some reasonable comparisons have been presented. The outcomes of the simulation study confirmed the efficiencies of obtained estimates as well as yielded the superiority of Bayesian estimate under adequate priors compared to the maximum likelihood estimate. Application to COVID-19 in some countries showed that the new distribution is more appropriate than some other competitive models.