Multicentric analysis of the perceptions and knowledge of digital health amongst undergraduate medical students.

INTRODUCTION: Digital health (DH) is continuously evolving by use of information and communications technology to improve healthcare provision, thereby reshaping systems and clinical practices. Recent studies identified an overwhelming lack of awareness of DH within the profession. This study aimed to analyse student perceptions and knowledge of DH to assess confidence in its use to develop greater DH awareness and literacy. METHODS: Students enrolled in undergraduate medical degrees were invited to take part in an online survey assessing aspects of DH including demography, familiarity, attitudes, level of knowledge and confidence. Anonymised data was collated and subsequently analysed to review DH awareness. RESULTS: A total of 143 students participated from nine British universities with 28.7% of respondents admitting low levels of familiarity of DH concepts. Students anticipated negative repercussions of DH including reduced data security (42.7%) and deterioration in doctor-patient relationship (30%); while improvements in healthcare access and health-outcomes are expected by 89.5% and 68.5%, respectively. 71.4% of participants believed they had minimal experience of exposure to DH and 76% believed they did not possess the necessary skills to utilise DH. Only 3.5% of students had some exposure to DH teaching during their course. CONCLUSION: There is an important requirement to address the lack of knowledge and exposure of students to DH, particularly as the world targets the COVID-19 pandemic. DH is forming the basis of the 'new normal' in healthcare, however the full potential of DH cannot be achieved unless there is an increase in its teaching incorporated into medical school curricula.

Risk factors associated with Diabetes Mellitus in COVID-19 infected patients: A meta-analytic synthesis of observational studies (preprint)

Aims: : We carried out a systematic literature review and meta-analytic synthesis to find out association between DM and related outcomes in patients with COVID-19 infection. Methods: We systematically searched MEDLINE, and Web of Science to identify studies investigating comorbidities, clinical manifestations and resource utilization of diabetic patients exposed with COVID-19 published from inception to January 2021. Meta-analysis was carried out using Review Manager 5.3. Random effects model was used to compute the pooled estimates of odds ratio/mean difference (OR)/(MD) and 95% confidence intervals (CI). Results: Results from the pooled meta-analysis found that CVD, hypertension, AKI, cerebrovascular disease, AKI and ARDS were significantly associated with DM in COVID-19 infected patients compared to non-diabetic patients. There is significant association found between mortality and DM compared to non-diabetic patients [OR (95% CI): 2.46 (1.68, 3.58)]. ICU admission and use of mechanical ventilation was significantly associated with DM and COVID-19 vs. non-diabetic [OR (95% CI): 2.79 (1.79,4.34) and 3.33 (2.05, 5.42)] respectively. However, LOS, hospitalization, and ICU admission were not significantly differing between diabetes vs. non-diabetes. Conclusions: The results showed a significant association between mortality and DM exposed with COVID-19. Other co-morbidities especially CVD/hypertension could be a serious threat for DM COVID-19 infected patients for the higher mortality.

Federated Learning used for predicting outcomes in SARS-COV-2 patients (preprint)

‘Federated Learning’ (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the “EXAM” (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.92, an average improvement of 16%, and a 38% increase in generalisability over local models. The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. This provided the broader healthcare community with a validated model to respond to COVID-19 challenges, as well as set the stage for broader use of FL in healthcare.

COVID-19 and its impact on environment: Improved pollution levels during the lockdown period - A case from Ahmedabad, India.

The novel Coronavirus pandemic (COVID-19) hit the world severely in the first half of 2020 which forced several nations to impose severe restrictions on all sorts of activities involving human population. People were mainly advised to remain home quarantined to curb the virus spread. Industrial and vehicular movements were ceased as a result of lockdown, and therefore the rate of pollutants entering the ecosystem was also reduced in many places. Water and air pollution remained a major concern in the last few decades as these were gradually deteriorating in many spheres including the hydrosphere and atmosphere. As the nation-wide lockdown period in India completed more than two months, this study attempted to analyze the impact of lockdown on water and air quality to understand the short-term environmental changes. Using remote sensing data, this study demonstrated the improvements in ambient water quality in terms of decreased turbidity levels for a section of the Sabarmati River in the Ahmedabad region of India. The Suspended Particulate Matter (SPM) concentrations are evaluated to underline the turbidity levels in the study area before and during the lockdown period using the Landsat 8 OLI images. We noticed that the average SPM has significantly decreased by about 36.48% when compared with the pre-lockdown period; and a drop of 16.79% was observed from the previous year's average SPM. Overall, the average SPM concentration during the lockdown period (8.08 mg/l), was the lowest when compared with pre-lockdown average and long-term (2015-2019) April month average. The atmospheric pollution level (NO2, PM2.5, and PM10) data obtained from the Central Pollution Control Board for Ahmedabad city also shows a significant improvement during the study period, implying a positive response of COVID-19 imposed lockdown on the environmental fronts.