ABSTRACT
BACKGROUND: On 16 Jan 2021, India launched its immunization program against COVID-19. Among the first recipients were 1.59 million Health Care Workers (HCWs) and Frontline Workers (FLWs) of the Indian Armed Forces, who were administered COVISHIELD (Astra Zeneca). We present an interim analysis of vaccine effectiveness (VE) estimates till 30 May 2021. METHODS: The VIN-WIN cohort study was carried out on anonymized data of HCWs and FLWs of Indian Armed Forces. The existing surveillance system, enhanced for COVID-19 monitoring, was sourced for data. The cohort transitioned from Unvaccinated (UV) to Partially Vaccinated (PV) to Fully Vaccinated (FV), serving as its own internal comparison. Outcomes studied in the three groups were breakthrough infections and COVID related deaths. Incidence Rate Ratio (IRR) was used to compare outcomes among the three groups to estimate VE. RESULTS: Data of 1,595,630 individuals (mean age 27.6 years; 99% male) over 135 days was analysed. Till 30 May 21, 95.4% and 82.2% were partially and fully vaccinated. The UV, PV and FV compartments comprised 106.6, 46.7 and 58.7 million person-days respectively. The number of breakthrough cases in the UV, PV and FV groups were 10061, 1159 and 2512; while the deaths were 37, 16 and 7 respectively. Corrected VE was 91.8-94.9% against infections. CONCLUSION: Interim results of the VIN-WIN cohort study of 1.59 million HCWs and FLWs of Indian Armed Forces showed a â¼93% reduction in COVID-19 breakthrough infections with COVISHIELD vaccination.
ABSTRACT
BACKGROUND: Various mathematical models were published to predict the epidemiological consequences of the COVID-19 pandemic. This systematic review has studied the initial epidemiological models. METHODS: Articles published from January to June 2020 were extracted from databases using search strings and those peer-reviewed with full text in English were included in the study. They were analysed as to whether they made definite predictions in terms of time and numbers, or contained only mathematical assumptions and open-ended predictions. Factors such as early vs. late prediction models, long-term vs. curve-fitting models and comparisons based on modelling techniques were analysed in detail. RESULTS: Among 56,922 hits in 05 databases, screening yielded 434 abstracts, of which 72 articles were included. Predictive models comprised over 70% (51/72) of the articles, with susceptible, exposed, infectious and recovered (SEIR) being the commonest type (mean duration of prediction being 3 months). Common predictions were regarding cumulative cases (44/72, 61.1%), time to reach total numbers (41/72, 56.9%), peak numbers (22/72, 30.5%), time to peak (24/72, 33.3%), hospital utilisation (7/72, 9.7%) and effect of lockdown and NPIs (50/72, 69.4%). The commonest countries for which models were predicted were China followed by USA, South Korea, Japan and India. Models were published by various professionals including Engineers (12.5%), Mathematicians (9.7%), Epidemiologists (11.1%) and Physicians (9.7%) with a third (32.9%) being the result of collaborative efforts between two or more professions. CONCLUSION: There was a wide diversity in the type of models, duration of prediction and the variable that they predicted, with SEIR model being the commonest type.
ABSTRACT
BACKGROUND: With the rise of Coronavirus disease 2019 (COVID-19) cases in India, lockdown was imposed from March 25, 2020. We studied post-lockdown scenarios and evaluated health-care constraints. Our aim was to identify the scenarios in which health-care availability would not be overwhelmed. METHODS: A modified compartmental SEIR stochastic model was used to calculate peak cases at various levels of effectiveness of prevention of transmission. Health-care constraints were evaluated using a Delphi study. We developed "q-metric" to evaluate the epidemic. Key constraints were matched against scenarios generated, and a monitoring mechanism was devised. RESULTS: Continuing lockdown ("q-metric" of >50) until mid-August was theoretically the most effective solution to end the epidemic. Lockdown might however be lifted earlier owing to various compulsions. The key constraints were identified as trained manpower and ventilators. It was estimated that shortfall of specialists to operate ventilators for COVID-19 intensive care units was approximately 40,000. This requires re-purposing of other specialists and short-term training to meet the surge. The shortage of ventilators is around 40,000-50,000. Procuring beyond those numbers would be infructuous owing to limits of training manpower. After lifting lockdown, the aim should be to contain the epidemic within the availability of key constraints. Our model suggests that this can be achieved by community containment and other non-pharmacological interventions at a "q-metric" of 19. An algorithm using "q-metric" was developed to monitor the epidemic. CONCLUSION: Various post-lockdown scenarios were simulated. Trained manpower and ventilators were identified as key health-care constraints. Partial community containment measures will require to be continued after the current lockdown is lifted.
ABSTRACT
BACKGROUND: In India, the SARS-CoV-2 COVID-19 epidemic has grown to 1251 cases and 32 deaths as on 30 Mar 2020. The healthcare impact of the epidemic in India was studied using a stochastic mathematical model. METHODS: A compartmental SEIR model was developed, in which the flow of individuals through compartments is modeled using a set of differential equations. Different scenarios were modeled with 1000 runs of Monte Carlo simulation each using MATLAB. Hospitalization, intensive care unit (ICU) requirements, and deaths were modeled on SimVoi software. The impact of nonpharmacological interventions (NPIs) including social distancing and lockdown on checking the epidemic was estimated. RESULTS: Uninterrupted epidemic in India would have resulted in more than 364 million cases and 1.56 million deaths with peak by mid-July. As per the model, at current growth rate of 1.15, India is likely to reach approximately 3 million cases by 25 May, implying 125,455 (±18,034) hospitalizations, 26,130 (±3298) ICU admissions, and 13,447 (±1819) deaths. This would overwhelm India's healthcare system. The model shows that with immediate institution of NPIs, the epidemic might still be checked by mid-April 2020. It would then result in 241,974 (±33,735) total infections, 10,214 (±1649) hospitalizations, 2121 (±334) ICU admissions, and 1081 (±169) deaths. CONCLUSION: At the current growth rate of epidemic, India's healthcare resources will be overwhelmed by the end of May. With the immediate institution of NPIs, total cases, hospitalizations, ICU requirements, and deaths can be reduced by almost 90%.
ABSTRACT
BACKGROUND: Medical errors are being detected with increasing frequency in healthcare environment, in many cases leading to patient harm. Measurement and improvement of patient safety climate has been identified as a strategic effort towards addressing this vital issue. METHOD: Safety Attitude Questionnaire (SAQ), validated by previous research was administered to 300 respondents in three tertiary care hospitals of India, the respondents representing various categories of healthcare workers and variations in safety scale score was analyzed by various statistical tools. RESULTS: No variation was observed in the Patient Safety Index score among the study hospitals. However, significant variations were observed among different categories of healthcare workers across dimensions of Teamwork, Perception of Management and Stress Recognition. Multiple Regression models identified Teamwork and Perception of Management to have significant correlation with Patient Safety Index Score. CONCLUSION: Patient Safety Climate can be effectively assessed and such assessment utilized for focused improvement efforts towards safety in healthcare organizations.
