ABSTRACT
COVID-19 has affected all countries. Its containment represents a unique challenge for India due to a large population (>1.38 billion) across a wide range of population densities. Assessment of the COVID-19 disease burden is required to put the disease impact into context and support future pandemic policy development. Here, we present the national-level burden of COVID-19 in India in 2020 that accounts for differences across urban and rural regions and across age groups. Disability-adjusted life years (DALY) due to COVID-19 were estimated in the Indian population in 2020, comprised of years of life lost (YLL) and years lived with disability (YLD). Scenario analyses were conducted to account for excess deaths not recorded in the official data and for reported COVID-19 deaths. The direct impact of COVID-19 in 2020 in India was responsible for 14,106,060 (95% uncertainty interval [UI] 14,030,129-14,213,231) DALYs, consisting of 99.2% (95% UI 98.47-99.64%) YLLs and 0.80% (95% UI 0.36-1.53) YLDs. DALYs were higher in urban (56%; 95% UI 56-57%) than rural areas (44%; 95% UI 43.4-43.6) and in males (64%) than females (36%). In absolute terms, the highest DALYs occurred in the 51-60-year-old age group (28%) but the highest DALYs per 100,000 persons were estimated for the 71-80 year old age group (5,481; 95% UI 5,464-5,500 years). There were 4,823,791 (95% UI 4,760,908-4,924,307) DALYs after considering reported COVID-19 deaths only. The DALY estimations have direct and immediate implications not only for public policy in India, but also internationally given that India represents one sixth of the worlds population.
ABSTRACT
There is a lack of COVID-19 adjusted case fatality risk (aCFR) estimates and information on states with high aCFR. State-specific aCFRs were estimated, using 13-day lag for fatality. To estimate country-level aCFR, state estimates were meta-analysed. Multiple correspondence analyses (MCA), followed by univariable logistic regression, were conducted to understand the association between aCFR and geodemographic, health and social indicators. Based on health indicators, states likely to report a higher aCFR were identified. Using random- and fixed-effects models, the aCFRs in India were 1.42 (95% CI 1.19 - 1.70) and 2.97 (95% CI 2.94 - 3.00), respectively. The aCFR was grouped with the incidence of diabetes, hypertension, cardiovascular diseases and acute respiratory infections in the first and second dimensions of MCA. The current study demonstrated the value of using meta-analysis to estimate aCFR. To decrease COVID-19 associated fatalities, states estimated to have a high aCFR must take steps to reduce co-morbidities. Article Summary LineMeta-analysis and the COVID-19 adjusted case fatality risks (aCFRs) in India are reported and states likely to report a higher aCFR have been identified.
ABSTRACT
BackgroundThe government of India implemented social distancing interventions to contain the COVID-19 epidemic. However, effects on epidemic dynamics are yet to be understood. MethodsRates of laboratory-confirmed COVID-19 infections per day and effective reproduction number (Rt) were estimated for 4 periods (Pre-lockdown and Lockdown Phases 1 to 3) according to nationally implemented phased interventions. Adoption of these interventions was estimated using Google mobility data. Estimates at the national level and for 12 Indian states most affected by COVID-19 are presented. FindingsDaily case rates ranged from 0{middle dot}03 to 30{middle dot}05/10 million people across 4 discrete periods in India. From May 4-17, 2020, the National Capital Territory (NCT) of Delhi had the highest case rate (222/10 million people/day), whereas Kerala had the lowest (2{middle dot}18/10 million/day). Average Rt was 1{middle dot}99 (95% CI 1{middle dot}93-2{middle dot}06) for India; it ranged from 1{middle dot}38 to 2{middle dot}78, decreasing over time. Median mobility in India decreased in all contact domains, with the lowest being 21% in retail/recreation (95% CI 13-46%), except home which increased to 129% (95% CI 117-132%) compared to the 100% baseline value. InterpretationThe Indian government imposed strict contact mitigation, followed by a phased relaxation, which slowed the spread of COVID-19 epidemic progression in India. The identified daily COVID-19 case rates and Rt will aid national and state governments in formulating ongoing COVID-19 containment plans. Furthermore, these findings may inform COVID-19 public health policy in developing countries with similar settings to India. FundingNon-funded.
