Diabetic retinopathy is the most common microvascular complication of diabetes mellitus and one of the leading causes of blindness globally. Due to the progressive nature of the disease, earlier detection and timely treatment can lead to substantial reductions in the incidence of irreversible vision-loss. Artificial intelligence (AI) screening systems have offered clinically acceptable and quicker results in detecting diabetic retinopathy from retinal fundus and optical coherence tomography (OCT) images. Thus, this systematic review and meta-analysis of relevant investigations was performed to document the performance of AI screening systems that were applied to fundus and OCT images of patients from diverse geographic locations including North America, Europe, Africa, Asia, and Australia. A systematic literature search on Medline, Global Health, and PubMed was performed and studies published between October 2015 and January 2020 were included. The search strategy was based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines, and AI-based investigations were mandatory for studies inclusion. The abstracts, titles, and full-texts of potentially eligible studies were screened against inclusion and exclusion criteria. Twenty-one studies were included in this systematic review; 18 met inclusion criteria for the meta-analysis. The pooled sensitivity of the evaluated AI screening systems in detecting diabetic retinopathy was 0.93 (95% CI: 0.92-0.94) and the specificity was 0.88 (95% CI: 0.86-0.89). The included studies detailed training and external validation datasets, criteria for diabetic retinopathy case ascertainment, imaging modalities, DR-grading scales, and compared AI results to those of human graders (e.g., ophthalmologists, retinal specialists, trained nurses, and other healthcare providers) as a reference standard. The findings of this study showed that the majority AI screening systems demonstrated clinically acceptable levels of sensitivity and specificity for detecting referable diabetic retinopathy from retinal fundus and OCT photographs. Further improvement depends on the continual development of novel algorithms with large and gradable sets of images for training and validation. If cost-effectiveness ratios can be optimized, AI can become a financially sustainable and clinically effective intervention that can be incorporated into the healthcare systems of low-to-middle income countries (LMICs) and geographically remote locations. Combining screening technologies with treatment interventions such as anti-VEGF therapy, acellular capillary laser treatment, and vitreoretinal surgery can lead to substantial reductions in the incidence of irreversible vision-loss due to proliferative diabetic retinopathy.

The aim of this systematic review and meta-analysis is to evaluate the prevalence of depression, anxiety, insomnia, stress, PTSD, and distress in Chinese healthcare workers (HCWs) and the changes in prevalence before and after the peak incidence of COVID-19 in China. 20 cross-sectional studies assessing the aforementioned psychological outcomes were included. Eligible studies were searched from the following databases: PubMed, Scopus, and Web of Science. Comparative analysis based on the time period of the included studies was conducted to assess changes in prevalence before and after peak incidence. Additionally, subgroup analyses based on study quality, province, survey tools, gender and healthcare profession, frontline or non-frontline working status, and severity of psychological outcomes were conducted to evaluate the prevalence of outcomes across various study methods, geographic regions, and professions. The findings of this study suggest that the overall prevalence of depression, anxiety, insomnia, stress, PTSD, and distress before peak incidence were 36.2%, 34.2%, 22.4%, 31.3%, 9.8%, and 56.7% as opposed to 31.8%, 24.1%, 34.4%, 59.0%, 20.9%, and 40.7% after the peak. The higher prevalence of depression, anxiety, and distress prior to the peak incidence of COVID-19 in China and of insomnia, stress, and PTSD thereafter serve as evidence that the mental health decline of HCWs is dynamic and should be addressed with adaptive approaches that provide tailored treatments.

Current epidemiological data reports that adults aged 65 years and older comprise the most vulnerable age group with the highest proportion of CAP-attributable hospitalizations. Pneumococcal vaccine efficacy (VE) has been shown to decrease over time, contributing to increasing incidence rates of CAP. A holistic evaluation of age, sex, seasonality, and VE are is conducted in this systematic review and meta-analysis of 12 prospective and retrospective cohort studies. The findings suggest that incidence and age are positively associated and that incidence in females is more often reported to be higher in females than in males. In studies that observed seasonality of CAP, high seasons and low seasons were reported to be in winter and summer months, respectively. Lastly, studies that reviewed the effect of vaccination on incidence consistently found decreased observance of CAP in elderly adults following reception of PCV13 or PPSV23. However, one study suggested that such vaccinations may have decreased effectiveness in elderly populations and that research examining potential explanations for this require further investigation. Furthermore, distinct diagnostic and case ascertainment standards, descriptive measures, and methods of prevention and treatment of CAP used across the US are outlined in this review. Public health guidance such as encouraging the reception of pneumococcal vaccinations and mask-wearing during high seasons of CAP, and communicating the risks of not adhering to the aforementioned preventative measures can facilitate an effort to reduce the incidence of CAP and its associated adverse outcomes in the US elderly population.