Impacts of Regional Lockdown Policies on COVID-19 Transmission in India in 2020

ObjectiveTo assess the impact of non-pharmaceutical interventions (NPIs) on the first wave of COVID transmission and fatalities in India. MethodsWe collected data on NPIs, using government notifications and news reports, in six major Indian states from March to August 2020, and we matched these with district-level data on COVID related deaths and Google Mobility reports. We used a district fixed effect regression approach to measure the extent to which district-level lockdowns and mobility restrictions helped reduce deaths in 2020. ResultsIn most states, COVID deaths grew most rapidly only after the initial lockdown was lifted. District-level NPIs were associated with a statistically significantly lower COVID death count in three out of five sample states (district analysis was not possible in Delhi) and in the aggregate. Interventions that were most associated with slowing fatalities were temple closures, retail closures, and curfews. DiscussionOutside of Maharashtra (the first state struck) the first fatality wave appears to have been delayed by the national lockdown. Indias NPIs, however incomplete, were successful in delaying or limiting COVID-19 deaths. Even with incomplete compliance, limiting mass gatherings in face of incipient viral waves may save lives.

Excess mortality in India from June 2020 to June 2021 during the COVID pandemic: death registration, health facility deaths, and survey data

BackgroundIndias official death totals from the COVID pandemic are widely regarded as under-reports. MethodsWe quantified all-cause excess mortality in India, comparing deaths during the peak of the first and second COVID waves (Jul-Dec 2020 and April-June 2021) with month wise deaths in 2015-19 from three sources: Civil Registration System (CRS) mortality reports from 15 states or cities with 37% of Indias population; deaths in 0.2 million health facilities; and a representative survey of 0.14 million adults about COVID deaths. ResultsDuring the first viral wave, the median excess mortality compared to CRS baseline was 22% and 41%, respectively, in included states and cities, rising to 46% and 85% during the second wave. In settings with 10 or more months of data across the two waves, the median excess mortality was 32% and 37% for states and cities, respectively. Deaths in health facilities showed a 27% excess mortality from July 2020-May 2021, reaching 120% during April-May 2021. The national survey found 3.5% of adults reported a COVID death in their household in April-June 2021, approximately doubling the 3.2% expected overall deaths. The national survey showed 29-32% excess deaths from June 1, 2020 to June 27, 2021, most of which were likely to be COVID. This translates to 3.1-3.4 million COVID deaths (including 2.5-2.8 million during April-June 2021). National extrapolations from health facility and CRS data suggest 2.7-3.3 million deaths during the year. ConclusionsIndias COVID death rate may be about 7-8 times higher than the officially reported 290/million population.

Representative Estimates of COVID-19 Infection Fatality Rates from Three Locations in India

There are very few estimates of the age-specific infection fatality rate (IFR) of SARS-CoV-2 in low- and middle-income countries. India reports the second highest number of SARS-CoV-2 infections in the world. We estimate age-specific IFR using data from seroprevalence surveys in Mumbai (population 12 million) and Karnataka (population 61 million), and a random sample of economically distressed migrants in Bihar with mortality followup. Among men aged 50-89, IFR is 0.12% in Karnataka (95% C.I. 0.09%-0.15%), 0.53% in Mumbai (0.52%-0.54%), and 5.64% among migrants in Bihar (0-11.16%). IFR in India is approximately twice as high for men as for women, is heterogeneous across contexts, and rises much less at older ages than in comparable studies from high income countries.

The COVID-19 mortality effects of underlying health conditions in India: a modelling study

ObjectiveTo model how known COVID-19 comorbidities will affect mortality rates and the age distribution of mortality in a large lower middle income country (India), as compared with a high income country (England), and to identify which health conditions drive any differences. DesignModelling study. SettingEngland and India. Participants1,375,548 respondents aged 18 to 99 to the District Level Household Survey-4 and Annual Health Survey in India. Additional information on health condition prevalence on individuals aged 18 to 99 was obtained from the Health Survey for England and the Global Burden of Diseases, Risk Factors, and Injuries Studies (GBD). Main outcome measuresThe primary outcome was the proportional increase in age-specific mortality in each country due to the prevalence of each COVID-19 mortality risk factor (diabetes, hypertension, obesity, chronic heart disease, respiratory illness, kidney disease, liver disease, and cancer, among others). The combined change in overall mortality and the share of deaths under 60 from the combination of risk factors was estimated in each country. ResultsRelative to England, Indians have higher rates of diabetes (10.6% vs. 8.5%), chronic respiratory disease (4.8% vs. 2.5%), and kidney disease (9.7% vs. 5.6%), and lower rates of obesity (4.4% vs. 27.9%), chronic heart disease (4.4% vs. 5.9%), and cancer (0.3% vs. 2.8%). Population COVID-19 mortality in India relative to England is most increased by diabetes (+5.4%) and chronic respiratory disease (+2.3%), and most reduced by obesity (-9.7%), cancer (-3.2%), and chronic heart disease (-1.9%). Overall, comorbidities lower mortality in India relative to England by 9.7%. Accounting for demographics and population health explains a third of the difference in share of deaths under age 60 between the two countries. ConclusionsKnown COVID-19 health risk factors are not expected to have a large effect on aggregate mortality or its age distribution in India relative to England. The high share of COVID-19 deaths from people under 60 in low- and middle-income countries (LMICs) remains unexplained. Understanding mortality risk associated with health conditions prevalent in LMICs, such as malnutrition and HIV/AIDS, is essential for understanding differential mortality. SUMMARY BOXO_ST_ABSWhat is already known on this topicC_ST_ABSCOVID-19 infections in low- and middle-income countries (LMICs) are rising rapidly, with the burden of mortality concentrated at much younger ages than in rich countries. A range of pre-existing health conditions can increase the severity of COVID-19 infections. It is feared that poor population health may worsen the severity of the pandemic in LMICs. What this study addsThe COVID-19 comorbidities that have been studied to date may have only a very small effect on aggregate mortality in India relative to England and do not shift the mortality burden toward lower ages at all. Indias younger demographics can explain only a third of the substantial difference in the share of deaths under age 60 between India and England. However, mortality risk associated with health conditions prevalent in LMICs, such as malnutrition and HIV/AIDS, is unknown and research on this topic is urgently needed.