COVID-19 vaccine hesitancy among medical students in India.

The coronavirus disease 2019 (COVID-19) vaccine was launched in India on 16 January 2021, prioritising health care workers which included medical students. We aimed to assess vaccine hesitancy and factors related to it among medical students in India. An online questionnaire was filled by 1068 medical students across 22 states and union territories of India from 2 February to 7 March 2021. Vaccine hesitancy was found among 10.6%. Concern regarding vaccine safety and efficacy, lack of awareness regarding their eligibility for vaccination and lack of trust in government agencies predicted COVID-19 vaccine hesitancy among medical students. On the other hand, the presence of risk perception regarding themselves being affected with COVID-19 reduced vaccine hesitancy as well as hesitancy in participating in COVID-19 vaccine trials. Vaccine-hesitant students were more likely to derive information from social media and less likely from teachers at their medical colleges. Choosing between the two available vaccines (Covishield and Covaxin) was considered important by medical students both for themselves and for their future patients. Covishield was preferred to Covaxin by students. Majority of those willing to take the COVID-19 vaccine felt that it was important for them to resume their clinical posting, face-to-face classes and get their personal life back on track. Around three-fourths medical students viewed that COVID-19 vaccine should be made mandatory for both health care workers and international travellers. Prior adult vaccination did not have an effect on COVID-19 vaccine hesitancy. Targeted awareness campaigns, regulatory oversight of vaccine trials and public release of safety and efficacy data and trust building activities could further reduce COVID-19 vaccine hesitancy among medical students.

Tobacco, alcohol use and other risk factors for developing symptomatic COVID-19 vs asymptomatic SARS-CoV-2 infection: a case-control study from western Rajasthan, India.

BACKGROUND: Understanding risk factors of symptomatic coronavirus disease 2019 (COVID-19) vis-à-vis asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, severe disease and death is important. METHODS: An unmatched case-control study was conducted through telephonic interviews among individuals who tested positive for SARS-CoV-2 in Jodhpur, India from 23 March to 20 July 2020. Contact history, comorbidities and tobacco and alcohol use were elicited using standard tools. RESULTS: Among 911 SARS-CoV-2-infected individuals, 47.5% were symptomatic, 14.1% had severe COVID-19 and 41 (4.5%) died. Older age, working outside the home, cardiac and respiratory comorbidity and alcohol use were found to increase the risk of symptomatic disease as compared with asymptomatic infection. Current tobacco smoking (odds ratio [OR] 0.46 [95% confidence interval {CI} 0.26 to 0.78]) but not smokeless tobacco use (OR 0.81 [95% CI 0.55 to 1.19]) appeared to reduce the risk of symptomatic disease. Age ≥60 y and renal comorbidity were significantly associated with severe COVID-19. Age ≥60 y and respiratory and cardiac comorbidity were found to predispose to mortality. CONCLUSIONS: The apparent reduced risk of symptomatic COVID-19 among tobacco smokers could be due to residual confounding owing to unknown factors, while acknowledging the limitation of recall bias. Cross-protection afforded by frequent upper respiratory tract infection among tobacco smokers could explain why a similar association was not found for smokeless tobacco use, thereby being more plausible than the 'nicotinic hypothesis'. Those with comorbidities and age ≥60 y should be prioritized for hospital admission.

Transmission Dynamics of the COVID-19 Epidemic at the District Level in India: Prospective Observational Study.

BACKGROUND: On March 9, 2020, the first COVID-19 case was reported in Jodhpur, Rajasthan, in the northwestern part of India. Understanding the epidemiology of COVID-19 at a local level is becoming increasingly important to guide measures to control the pandemic. OBJECTIVE: The aim of this study was to estimate the serial interval and basic reproduction number (R0) to understand the transmission dynamics of the COVID-19 outbreak at a district level. We used standard mathematical modeling approaches to assess the utility of these factors in determining the effectiveness of COVID-19 responses and projecting the size of the epidemic. METHODS: Contact tracing of individuals infected with SARS-CoV-2 was performed to obtain the serial intervals. The median and 95th percentile values of the SARS-CoV-2 serial interval were obtained from the best fits with the weibull, log-normal, log-logistic, gamma, and generalized gamma distributions. Aggregate and instantaneous R0 values were derived with different methods using the EarlyR and EpiEstim packages in R software. RESULTS: The median and 95th percentile values of the serial interval were 5.23 days (95% CI 4.72-5.79) and 13.20 days (95% CI 10.90-18.18), respectively. R0 during the first 30 days of the outbreak was 1.62 (95% CI 1.07-2.17), which subsequently decreased to 1.15 (95% CI 1.09-1.21). The peak instantaneous R0 values obtained using a Poisson process developed by Jombert et al were 6.53 (95% CI 2.12-13.38) and 3.43 (95% CI 1.71-5.74) for sliding time windows of 7 and 14 days, respectively. The peak R0 values obtained using the method by Wallinga and Teunis were 2.96 (95% CI 2.52-3.36) and 2.92 (95% CI 2.65-3.22) for sliding time windows of 7 and 14 days, respectively. R0 values of 1.21 (95% CI 1.09-1.34) and 1.12 (95% CI 1.03-1.21) for the 7- and 14-day sliding time windows, respectively, were obtained on July 6, 2020, using method by Jombert et al. Using the method by Wallinga and Teunis, values of 0.32 (95% CI 0.27-0.36) and 0.61 (95% CI 0.58-0.63) were obtained for the 7- and 14-day sliding time windows, respectively. The projection of cases over the next month was 2131 (95% CI 1799-2462). Reductions of transmission by 25% and 50% corresponding to reasonable and aggressive control measures could lead to 58.7% and 84.0% reductions in epidemic size, respectively. CONCLUSIONS: The projected transmission reductions indicate that strengthening control measures could lead to proportionate reductions of the size of the COVID-19 epidemic. Time-dependent instantaneous R0 estimation based on the process by Jombart et al was found to be better suited for guiding COVID-19 response at the district level than overall R0 or instantaneous R0 estimation by the Wallinga and Teunis method. A data-driven approach at the local level is proposed to be useful in guiding public health strategy and surge capacity planning.