There is a dearth of evidence synthesis on the prevalence of anxiety among university students even though the risk of psychological disorders among this population is quite high. We conducted a quantitative systematic review to estimate the global prevalence of anxiety among university students during the COVID-19 pandemic. A systematic search for cross-sectional studies on PubMed, Scopus, and PsycINFO, using PRISMA guidelines, was conducted from September 2020 to February 2021. A total of 36 studies were included, using a random-effects model to calculate the pooled proportion of anxiety. A meta-analysis of the prevalence estimate of anxiety yielded a summary prevalence of 41% (95% CI = 0.34–0.49), with statistically significant evidence of between-study heterogeneity (Q = 80801.97, I² = 100%, p ≤ 0.0001). A subgroup analysis reported anxiety prevalence in Asia as 33% (95% CI:0.25–0.43), the prevalence of anxiety in Europe as 51% (95% CI: 0.44–0.59), and the highest prevalence of anxiety in the USA as 56% (95% CI: 0.44–0.67). A subgroup gender-based analysis reported the prevalence of anxiety in females as 43% (95% CI:0.29–0.58) compared to males with an anxiety prevalence of 39% (95% CI:0.29–0.50). University students seem to have a high prevalence of anxiety, indicating an increased mental health burden during this pandemic.

The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.

Cardiovascular outcome trials are mandated by the U.S. Food and Drug Administration to assess the cardiovascular safety of new antidiabetic medications before entering the market. However, these trials often involve highly selective populations and results may not generalize to routine practice. Our study aimed to synthesize observational studies to assess the generalizability of cardiovascular outcome trials to routine practice. We systematically reviewed observational studies that were target trial emulations of previous cardiovascular outcome trials for dipeptidyl peptidase-4 (DPP-4) inhibitors, glucagon-like peptide 1 (GLP-1) receptor agonists, and sodium glucose cotransporter-2 (SGLT-2) inhibitors among patients with type 2 diabetes. We searched the MEDLINE, EMBASE and Cochrane databases for observational studies that focused on trial emulation or cross-sectional studies that reported the proportion of real-world patients eligible for completed trials. Nineteen studies were included in our systematic review, including four cohort studies that were target trial emulations of previous RCTs and 15 cross-sectional studies that evaluated trial eligibility. Results between RCTs and real-world data (RWD) were concordant for all drug classes in finding non-inferiority. The median eligibility percentage ranged from 13% to 31% for SGLT-2 inhibitor trials and 12% to 43% for GLP-1 receptor agonist trials. These results suggest that, while RCTs and RWD are concordant in their estimates, the trials lack representativeness. More research is needed on the emulation of cardiovascular outcome trials using RWD to understand how different emulation methods may impact findings.

Aims: To determine whether sodium glucose co‐transporter 2 inhibitors (SGLT2i) use, compared with dipeptidyl peptidase‐4 inhibitors (DPP4i) use, is associated with the risk of ventricular arrhythmias (VA) among patients with type 2 diabetes. Materials and Methods: We conducted a population‐based cohort study using a prevalent new‐user design and data from the United Kingdom Clinical Practice Research Datalink Aurum, linked to hospitalisation and vital statistics data. SGLT2i users were matched to DPP4i users on diabetes treatment intensity, duration of prior DPP4i use, calendar time, sex, age, and time‐conditional propensity score. Cox models estimated the hazard ratio (HR) and corresponding 95% confidence intervals (CI) for VA with SGLT2i vs. DPP4i use. Secondary analyses stratified by SGLT2i user type. Secondary outcomes included fatal VA and cardiac arrest. Results: Among 88 516 matched patients, 385 VA events occurred over a mean follow‐up of 0.86 years (25.3 per 10 000 person‐years). Overall, SGLT2i use was not associated with the risk of VA (HR: 0.88, 95% CI: 0.71–1.07). There was no association among incident new users (HR: 0.98, 95% CI: 0.76–1.27) but SGLT2i use was associated with a lower risk among prevalent new users (HR: 0.65, 95% CI: 0.46–0.92). SGLT2i use was associated with a lower risk of cardiac arrest (HR: 0.64, 95% CI: 0.49–0.83); our analysis of fatal VA was inconclusive due to sparse data (HR: 1.76, 95% CI: 0.47–6.64). Conclusions: SGLT2i use was not associated with the risk of VA among patients with type 2 diabetes.