Social Media Big Data: The Good, The Bad, and the Ugly (Un)truths

10.3389/fdata.2021.623794Frontiers in Big Data462379

Population eye health education using augmented reality and virtual reality : scalable tools during and beyond COVID-19

The COVID-19 outbreak has had a massive impact on healthcare systems, with over 12 million infected individuals as of 10 July 2020.¹ This has necessitated operational overhaul in ophthalmology and other clinical specialties in accordance with public health measures such as physical distancing and cancellation of non-urgent clinical services.² The downstream impact of these measures include the disruption of healthcare functions including preventive programmes such as eye screening, which serve a crucial role to detect disease at early stages before the onset of irreversible morbidity such as visual impairment (VI).The study was partially funded by Tan Tock Seng Hospital (TTSH), Singapore which supported the research staffing hours contributed by study team members employed by the hospital (Funding/grant number: nil)

Digital health during COVID-19: lessons from operationalising new models of care in ophthalmology

10.1016/s2589-7500(20)30287-9The Lancet Digital Health3

Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies

10.1038/s41746-021-00412-9npj Digital Medicine414

Telehealth demand trends during the COVID-19 pandemic in the top 50 most affected countries: Infodemiological evaluation

10.2196/24445JMIR Public Health and Surveillance72e2444

The Historical Evolution of Ocular Tuberculosis: Past, Present and Future

Ocular Immunology and Inflammation303593-59

What does IGRA testing add to the diagnosis of ocular tuberculosis? A Bayesian latent class analysis

Abstract Background To evaluate the contribution made to the diagnostic work-up for patients with suspected ocular tuberculosis (TB) by QuantiFERON-TB Gold In-Tube (QFT) tests using latent class analysis model. Methods A single centre retrospective cohort study. A Bayesian latent class model was constructed on the basis of demographics, phenotypes and test results from patients attending a tertiary referral center in the UK. This estimated the probability of ocular TB for each patient in two versions, first with and then without QFT. The estimated probability of ocular TB was compared with treatment failure. Results From a database of 365 patients with clinical signs suggestive of ocular TB, 267 patients who had QFT and complete data were evaluated. Mean age was 45.0 ± 15.4 years with 141 (52.9%) male and 148 (50.5%) of Asian ethnicity. QFT was positive in 208 (70.1%) patients and ATT was instituted in 145 (49.5%) patients with 100 (34.1%) patients also having concurrent systemic corticosteroid therapy. The best estimate of a QFT level separating TB-positive and TB-negative patients was extremely low. This weak discrimination between TB and non-TB groups was reflected in poor positive and negative predictive values for treatment failure. Conclusions The latent class model did not successfully predict treatment failure, despite taking all variables into account. The threshold between TB and non-TB in QFT values was implausibly low and removing QFT from the model made prediction slightly worse. A larger prospective study is required to establish the role of all tests, demographics and phenotypes in diagnosis

The Impact and Applications of Social Media Platforms for Public Health Responses Before and During the COVID-19 Pandemic: Systematic Literature Review

10.2196/33680JOURNAL OF MEDICAL INTERNET RESEARCH24