A cross-sectional questionnaire-based study to assess anxiety among healthcare professionals in india during COVID-19 pandemic

Background and Aims: Coronavirus has resulted in unique challenges to the healthcare professionals. The pandemic has affected the mental health of healthcare professionals, which may impact their ability to provide care to patients. We aimed to assess anxiety among healthcare professionals, and its relation to selected parameters. Methods: This online questionnaire-based study recruited Indian healthcare workers through convenience sampling. Anxiety was measured using the Generalized Anxiety Disorder 7 scale. The reasons of emotional distress were also ascertained. Results: The sample comprised of 677 healthcare professionals including resident doctors, consultants, interns, nursing personnel, and others. Moderate/severe anxiety was present in 24.4% of the respondents. Moderate/severe anxiety was more likely to occur among those who had a friend or family member affected by COVID-19 (odds ratio [OR] 2.74, 95% confidence intervals [CI] 1.61–4.69), or who themselves had a psychiatric disorder in the past (OR 2.44, 95% CI 1.36–4.38). Those who reported long working hours and inadequate sleep, fear of being infected by the virus, concerns about transmitting the virus to a family member, mob violence against doctors, and shortage of personal protective equipment had significantly higher odds of moderate/severe anxiety. Conclusion: A considerable proportion of healthcare professionals suffering from anxiety suggests the need for addressing their mental health concerns.

Patient Experience of a Neurology Tele-Follow-Up Program Initiated During the Coronavirus Disease 2019 Pandemic: A Questionnaire-Based Study.

Background: Teleneurology consultations can be highly advantageous since neurological diseases and disabilities often limit patient's access to health care, particularly in a setting where they need to travel long distances for specialty consults. Patient satisfaction is an important outcome assessing success of a telemedicine program. Materials and Methods: A cross-sectional study was conducted to determine satisfaction and perception of patients toward an audio call based teleneurology follow-up initiated during the coronavirus disease 2019 pandemic. Primary outcomes were satisfaction to tele-consult, and proportion of patients preferring telemedicine for future follow-up. Results: A total of 261 patients who received tele-consult were enrolled. Satisfaction was highest for domain technological quality, followed by patient-physician dialogue (PPD) and least to quality of care (QoC). Median (interquartile range) patient satisfaction on a 5-point Likert scale was 4 (3-5). Eighty-five (32.6%; 95% confidence interval 26.9-38.6%) patients preferred telemedicine for future follow-up. Higher overall satisfaction was associated with health condition being stable/better, change in treatment advised on tele-consult, diagnosis not requiring follow-up examination, higher scores on domains QoC and PPD (p < 0.05). Future preference for telemedicine was associated with patient him-/herself consulting with doctor, less duration of follow-up, higher overall satisfaction, and higher scores on domain QoC (p < 0.05). On thematic analysis, telemedicine was found convenient, reduced expenditure, and had better physician attention; in-person visits were comprehensive, had better patient-physician relationship, and better communication. Discussion: Patient satisfaction was lower in our study than what has been observed earlier, which may be explained by the primitive nature of our platform. Several variables related to the patients' disease process have an effect on patient satisfaction. Conclusion: Development of robust, structured platforms is necessary to fully utilize the potential of telemedicine in developing countries.

A machine learning application for raising WASH awareness in the times of COVID-19 pandemic.

The COVID-19 pandemic has revealed the power of internet disinformation in influencing global health. The deluge of information travels faster than the epidemic itself and is a threat to the health of millions across the globe. Health apps need to leverage machine learning for delivering the right information while constantly learning misinformation trends and deliver these effectively in vernacular languages in order to combat the infodemic at the grassroot levels in the general public. Our application, WashKaro, is a multi-pronged intervention that uses conversational Artificial Intelligence (AI), machine translation, and natural language processing to combat misinformation (NLP). WashKaro uses AI to provide accurate information matched against WHO recommendations and delivered in an understandable format in local languages. The primary aim of this study was to assess the use of neural models for text summarization and machine learning for delivering WHO matched COVID-19 information to mitigate the misinfodemic. The secondary aim of this study was to develop a symptom assessment tool and segmentation insights for improving the delivery of information. A total of 5026 people downloaded the app during the study window; among those, 1545 were actively engaged users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot "Satya" increased thus proving the usefulness of a mHealth platform to mitigate health misinformation. We conclude that a machine learning application delivering bite-sized vernacular audios and conversational AI is a practical approach to mitigate health misinformation.

Risk factors for Coronavirus disease-associated mucormycosis.

BACKGROUND: The epidemiology of the Coronavirus-disease associated mucormycosis (CAM) syndemic is poorly elucidated. We aimed to identify risk factors that may explain the burden of cases and help develop preventive strategies. METHODS: We performed a case-control study comparing cases diagnosed with CAM and taking controls as recovered COVID 19 patients who did not develop mucormycosis. Information on comorbidities, glycemic control, and practices related to COVID-19 prevention and treatment was recorded. Multivariate regression analysis was used to identify independent predictors. RESULTS: A total of 352 patients (152 cases and 200 controls) diagnosed with COVID-19 during April-May 2021 were included. In the CAM group, symptoms of mucormycosis began a mean of 18.9 (SD 9.1) days after onset of COVID-19, and predominantly rhino-sinus and orbital involvement was present. All, but one, CAM cases had conventional risk factors of diabetes and steroid use. On multivariable regression, increased odds of CAM were associated with the presence of diabetes (adjusted OR 3.5, 95% CI 1.1-11), use of systemic steroids (aOR 7.7, 95% CI 2.4-24.7), prolonged use of cloth and surgical masks (vs. no mask, aOR 6.9, 95%CI 1.5-33.1), and repeated nasopharyngeal swab testing during the COVID-19 illness (aOR 1.6, 95% CI 1.2-2.2). Zinc therapy was found to be protective (aOR 0.05, 95%CI 0.01-0.19). Notably, the requirement of oxygen supplementation or hospitalization did not affect the risk of CAM. CONCLUSION: Judicious use of steroids and stringent glycemic control are vital to preventing mucormycosis. Use of clean masks, preference for N95 masks if available, and minimizing swab testing after the diagnosis of COVID-19 may further reduce the incidence of CAM.