The Mortality Burden from COVID in Low-Income Settings: Evidence from Verbal Autopsies in India (preprint)

Measuring the mortality burden of SARS-CoV-2 in lower-income countries is difficult because death registries are incomplete and lack cause of death. We address this problem in India, which had the second-highest number of officially reported cases. We completed WHO-compliant verbal autopsy (VA) surveys on roughly 20,000 deaths drawn from a population-representative sample. SARS-CoV-2 deaths spike in June 2020, just after Indias lockdown, and in May 2021, after its second wave. During those spikes the virus is responsible for 23.3% and 35.8% of all deaths, respectively. Cardiovascular deaths also spike during the start of the pandemic. We find that the death rate rises by 81% during the pandemic, SARS-CoV-2 is responsible for 33% of these excess deaths, and cardiovascular disease for 23% of these deaths.

A hierarchical Bayesian model for estimating age-specific COVID-19 infection fatality rates in developing countries (preprint)

The COVID-19 infection fatality rate (IFR) is the proportion of individuals infected with SARS-CoV-2 who subsequently die. As COVID-19 disproportionately affects older individuals, age-specific IFR estimates are imperative to facilitate comparisons of the impact of COVID-19 between locations and prioritize distribution of scare resources. However, there lacks a coherent method to synthesize available data to create estimates of IFR and seroprevalence that vary continuously with age and adequately reflect uncertainties inherent in the underlying data. In this paper we introduce a novel Bayesian hierarchical model to estimate IFR as a continuous function of age that acknowledges heterogeneity in population age structure across locations and accounts for uncertainty in the estimates due to seroprevalence sampling variability and the imperfect serology test assays. Our approach simultaneously models test assay characteristic, serology, and death data, where the serology and death data are often available only for binned age groups. Information is shared across locations through hierarchical modeling to improve estimation of the parameters with limited data. Modeling data from 26 developing country locations during the first year of the COVID-19 pandemic, we found seroprevalence did not change dramatically with age, and the IFR at age 60 was above the high-income country benchmark for most locations.

Contribution of infection and vaccination to population-level seroprevalence through two COVID waves in Tamil Nadu, India (preprint)

Four rounds of serological surveys were conducted, spanning two COVID waves (October 2020 and April-May 2021), in Tamil Nadu (population 72 million) state in India. Each round included representative populations in each district of the state, totaling  ≥20,000 persons per round. State-level seroprevalence was 31.5% in round 1 (October-November 2020), after India’s first COVID wave. Seroprevalence fell to 22.9% in 2 (April 2021), consistent with waning of SARS-Cov-2 antibodies from natural infection. Seroprevalence rose to 67.1% by round 3 (June-July 2021), reflecting infections from the Delta-variant induced second COVID wave. Seroprevalence rose to 93.1% by round 4 (December 2021-January 2022), reflecting higher vaccination rates. Antibodies also appear to wane after vaccination. Seroprevalence in urban areas was higher than in rural areas, but the gap shrunk over time (35.7 v. 25.7% in round 1, 89.8% v. 91.4% in round 4) as the epidemic spread even in low-density rural areas. The study documents substantial waning of SARS-CoV-2 antibodies at the population level and demonstrates how to calculate the extent to which infection and vaccination separately contribute to seroprevalence estimates.

Lessons from disease and economic surveillance during COVID in India

This paper describes disease and economic surveillance during COVID, along with the uses of that surveillance, and lessons learned about the pandemic from that surveillance. It ends with policy suggestions on how to gather intelligence during the next pandemic in India and how surveillance informs suppression policy. Important themes that I stress are the value of population-level surveillance, understanding the incentives and disincentives for surveillance and reporting, and tailoring policy to the results of surveillance.

Using household rosters from survey data to estimate all-cause mortality during COVID in India

Official statistics on deaths due to COVID undercount deaths due to lack of testing. In developed countries, death registries have been used to measure total excess death during the pandemic. However, very few developing countries have even partial death registries or the capacity to register deaths during a pandemic. In this paper we estimate excess deaths in India using the member roster of a large and representative household panel survey. We estimate roughly 6.3 million excess deaths during the pandemic through August 2021. We cannot demonstrate causality between COVID and deaths but the timing and age structure of deaths is consistent with the COVID pandemic and excess deaths are positively correlated with reported infections. Finally, we find that excess deaths were higher among higher-income persons and were negatively associated with mobility. The methods in this paper can be used in countries with a household panel to measure health-related demographic indicators.

Seroprevalence in Tamil Nadu through India's two COVID waves: Evidence on antibody decline following infection and vaccination (preprint)

Three rounds of population-representative serological studies through India's two COVID waves (round 1, 19 October-30 November 2020; round 2, 7-30 April 2021; and round 3, 28 June-7 July, 2021) were conducted at the district-level in Tamil Nadu state (population 72 million). State-level seroprevalence in rounds 1, 2 and 3 were 31.5%, 22.9%, and 67.1%. Estimated seroprevalence implies that at least 22.6 and 48.1 million persons were infected by the 30 November 2020 and 7 July 2021. There was substantial variation across districts in the state in each round. Seroprevalence ranged from 11.1 to 49.8% (round 1), 7.9 to 50.3% (round 2), and 37.8 to 84% (round 3). Seroprevalence in urban areas was higher than in rural areas (35.7 v. 25.7% in round 1, 74.8% v. 64.1% in round 3). Females had similar seroprevalence to males (30.8 v. 30.2% in round 1, 67.5 v. 65.5% in round 3). While working age populations (age 40-49: 31.6%) had significantly higher seroprevalence than the youth (age 18-29: 30.4%) or elderly (age 70+: 26.5%) in round 1, only the gap between working age (age 40-49: 66.7%) and elderly (age 70+: 59.6%) remained significant in round 3. Seroprevalence was greater among those who were vaccinated for COVID (25.7% v. 20.9% in round 2, 80.0% v. 62.3% in round 3). While the decline in seroprevalence from rounds 1 to 2 suggests antibody decline after natural infection, we do not find a significant decline in antibodies among those receiving at least 1 dose of COVID vaccine between rounds 2 and 3.

Assessing the Burden of COVID-19 in Developing Countries: Systematic Review, Meta-Analysis, and Public Policy Implications (preprint)

Introduction The infection-fatality rate (IFR) of COVID-19 has been carefully measured and analyzed in high-income countries, whereas there has been no systematic analysis of age-specific seroprevalence or IFR for developing countries. Methods We systematically reviewed the literature to identify all COVID-19 serology studies in developing countries that were conducted using population representative samples collected by early 2021. For each of the antibody assays used in these serology studies, we identified data on assay characteristics, including the extent of seroreversion over time. We analyzed the serology data using a Bayesian model that incorporates conventional sampling uncertainty as well as uncertainties about assay sensitivity and specificity. We then calculated IFRs using individual case reports or aggregated public health updates, including age-specific estimates whenever feasible. Results Seroprevalence in many developing country locations was markedly higher than in high-income countries. In most locations, seroprevalence among older adults was similar to that of younger age cohorts, underscoring the limited capacity that these nations have to protect older age groups. Age-specific IFRs were roughly 2x higher than in high-income countries. The median value of the population IFR was about 0.5%, similar to that of high-income countries, because disparities in healthcare access were roughly offset by differences in population age structure. Conclusion The burden of COVID-19 is far higher in developing countries than in high-income countries, reflecting a combination of elevated transmission to middle-aged and older adults as well as limited access to adequate healthcare. These results underscore the critical need to accelerate the provision of vaccine doses to populations in developing countries. Key Points - Age-stratified infection fatality rates (IFRs) of COVID-19 in developing countries are about twice those of high-income countries. - Seroprevalence (as measured by antibodies against SARS-CoV-2) is broadly similar across age cohorts, underscoring the challenges of protecting older age groups in developing countries. - Population IFR in developing countries is similar to that of high-income countries, because differences in population age structure are roughly offset by disparities in healthcare access as well as elevated infection rates among older age cohorts. - These results underscore the urgency of disseminating vaccines throughout the developing world.

Comparative cost-effectiveness of SARS-CoV-2 vaccine dose fractionation in India: a modelling study (preprint)

Given constrained vaccine supplies globally, fractionation of vaccine doses may be an effective strategy for reducing disease and healthcare burdens, even with the emergence of COVID-19 variants. Using a multi-scale model that incorporates population-level transmission and individual-level vaccination, we estimate the costs associated with hospitalization, vaccine costs, and the economic benefit of reducing COVID-19 deaths associated with dose-fractionation strategies. Assuming a willingness-to-pay of US$10,517 per averted year of life lost (YLL) and a price of $12 per vaccine, under various transmission scenarios, with effective reproduction numbers ranging from 1.1 to 5.0 and with vaccine efficacy against transmission from 52% to 91%, the optimal vaccination strategy would always involve fractional doses of vaccines. Vaccine dose fractionation is a cost-effective strategy for mitigating the COVID-19 pandemic and could save a large number of lives, even after the emergence of variants with higher transmissibility.

All-Cause Mortality During Sars-Cov-2 Pandemic in India: Nationally-Representative Estimates Independent of Official Death Registry (preprint)

We estimate excess deaths in India during the COVID pandemic using monthly deaths in the sample of a privately-conducted, nationally-representative, large, panel data set. The data set includes roughly 174,000 households (1.2 million members) and spans January 2015 - June 2021. We estimate COVID is associated with 3.36 million (95% CI: 2.08-4.63 million) excess deaths, a 17.3% increase in the all-cause death rate, until April 2021. Excess deaths spike during the peaks of the 2 infection waves in India. The second wave is associated with significantly more excess deaths than the first. The age-pattern of deaths is skewed towards the elderly relative to baseline.

Prevalence of COVID-19 in Rural Versus Urban Areas in a Low-Income Country: Findings from a State-Wide Study in Karnataka, India (preprint)

Although the vast majority of confirmed cases of COVID-19 are in low- and middle-income countries, there are relatively few published studies on the epidemiology of SARS-CoV-2 in these countries. The few there are focus on disease prevalence in urban areas. We conducted state-wide surveillance for COVID-19, in both rural and urban areas of Karnataka between June 15-August 29, 2020. We tested for both viral RNA and antibodies targeting the receptor binding domain (RBD). Adjusted seroprevalence across Karnataka was 46.7% (95% CI: 43.3-50.0), including 44.1% (95% CI: 40.0-48.2) in rural and 53.8% (95% CI: 48.4-59.2) in urban areas. The proportion of those testing positive on RT-PCR, ranged from 1.5 to 7.7% in rural areas and 4.0 to 10.5% in urban areas, suggesting a rapidly growing epidemic. The relatively high prevalence in rural areas is consistent with the higher level of mobility measured in rural areas, perhaps because of agricultural activity. Overall seroprevalence in the state implies that by August at least 31.5 million residents had been infected by August, nearly an order of magnitude larger than confirmed cases.

All-cause mortality during SARS-CoV-2 Pandemic in India: Nationally-representative estimates independent of official death registry (preprint)

We estimate excess deaths in India during the COVID pandemic using monthly deaths in the sample of a privately-conducted, nationally-representative, large, panel data set. The data set includes roughly 174,000 households (1.2 million members) and spans January 2015 - June 2021. We estimate COVID is associated with 3.36 million (95% CI: 2.08-4.63 million) excess deaths, a 17.3% increase in the all-cause death rate, until April 2021. Excess deaths spike during the peaks of the 2 infection waves in India. The second wave is associated with significantly more excess deaths than the first. The age-pattern of deaths is skewed towards the elderly relative to baseline.

Explaining the Income and Consumption Effects of COVID in India (preprint)

The COVID-19 pandemic led to stark reductions in economic activity in India. We employ CMIE's Consumer Pyramids Household Survey to examine the timing, distribution, and mechanism of the impacts from this shock on income and consumption through December 2020. First, we estimate large and heterogeneous drops in income, with ambiguous effects on inequality. While incomes of salaried workers fell 35%; incomes of daily laborers fell 75%. At the same time, we observe that income fell more for individuals from households in the highest income quartile. Second, we document an increase in effort to buffer income shocks by switching occupations. We employ a Roy Model to estimate the gains from occupation churn and find, surprisingly, that reservation wages fell, implying that the risk of COVID did not reduce the value of employment. Third, we find that consumption fell less than income, suggesting households were able to smooth the idiosyncratic components of the COVID shock as well as they did before COVID. Finally, consumption of food and fuel fell less than consumption of durables such as clothing and appliances. Following Costa (2001) and Hamilton (2001), we estimate Engel curves and find that changes in consumption reflect large price shocks (rather than a retreat to subsistence) in sectors other than food and fuel/power. In the food sector, it appear that lockdown successfully distinguished essential and non-essential services, at least to the extent that it did not increase the relative price of food. There is some suggestive evidence that the price shocks outside the food sector were larger in places with greater COVID-19 cases, even during the lockdown.

Infrastructure, enforcement and COVID-19 in Mumbai slums: A first look (preprint)

This study is among the first to investigate whether patterns of access to basic services could explain the disproportionately severe impact of COVID-19 in slums. Using geolocated containment zones and COVID-19 case data for Mumbai, India’s most populous city, we find that cases and case fatality rates are higher in slums compared to formal residential buildings. Our results show that while access to toilets for men is associated with lower COVID-19 prevalence, the effect is opposite in the case of toilets for women. This could be because limited hours for safely using toilets and higher waiting times increase risk of exposure, and women and children sharing toilet facilities results in crowding. Proximity to water pipelines has no effect on prevalence, likely because slumdwellers are disconnected from for- mal water supply networks. Indoor crowding does not seem to have an effect on case prevalence. Finally, while police capacity – measured by number of police station outposts – is associated with lower prevalence in non-slum areas, indicat- ing effective enforcement of containment, this relationship does not hold in slums. The study highlights the urgency of finding viable solutions for slum improvement and upgrading to mitigate the effects of contagion for some of the most vulnerable populations.

SARS-CoV-2 Seroprevalence in Tamil Nadu in October-November 2020 (preprint)

A population-representative serological study was conducted in all districts of the state of Tamil Nadu (population 72 million), India, in October-November 2020. State-level seroprevalence was 31.6%. However, this masks substantial variation across the state. Seroprevalence ranged from just 11.1% in The Nilgris to 51.0% in Perambalur district. Seroprevalence in urban areas (36.9%) was higher than in rural areas (26.9%). Females (30.8%) had similar seroprevalence to males (30.3%). However, working age populations (age 40-49: 31.6%) have significantly higher seroprevalence than the youth (age 18-29: 30.7%) or elderly (age 70+: 25.8%). Estimated seroprevalence implies that at least 22.6 million persons were infected by the end of November, roughly 36 times the number of confirmed cases. Estimated seroprevalence implies an infection fatality rate of 0.052%.

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

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.

Prevalence Of COVID-19 In Rural Versus Urban Areas in a Low-Income Country: Findings from a State-Wide Study in Karnataka, India (preprint)

Although the vast majority of confirmed cases of COVID-19 are in low- and middle-income countries, there are relatively few published studies on the epidemiology of SARS-CoV-2 in these countries. The few there are focus on disease prevalence in urban areas. We conducted state-wide surveillance for COVID-19, in both rural and urban areas of Karnataka between June 15-August 29, 2020. We tested for both viral RNA and antibodies targeting the receptor binding domain (RBD). Adjusted seroprevalence across Karnataka was 46.7% (95% CI: 43.3-50.0), including 44.1% (95% CI: 40.0-48.2) in rural and 53.8% (95% CI: 48.4-59.2) in urban areas. The proportion of those testing positive on RT-PCR, ranged from 1.5 to 7.7% in rural areas and 4.0 to 10.5% in urban areas, suggesting a rapidly growing epidemic. The relatively high prevalence in rural areas is consistent with the higher level of mobility measured in rural areas, perhaps because of agricultural activity. Overall seroprevalence in the state implies that by August at least 31.5 million residents had been infected by August, nearly an order of magnitude larger than confirmed cases.

Seroprevalence of SARS-CoV-2 in slums and non-slums of Mumbai, India, during June 29-July 19, 2020 (preprint)

Objective: Estimate seroprevalence in representative samples from slum and non-slum communities in Mumbai, India, a mega-city in a low or middle-income country and test if prevalence is different in slums. Design: After geographically-spaced community sampling of households, one individual per household was tested for IgG antibodies to SARS-CoV-2 N-protein in a two-week interval. Setting: Slum and non-slum communities in three wards, one each from the three main zones of Mumbai. Participants: Individuals over age 12 who consent to and have no contraindications to venipuncture were eligible. 6,904 participants (4,202 from slums and 2,702 from non-slums) were tested. Main outcome measures: The primary outcomes were the positive test rate for IgG antibodies to the SARS-CoV-2 N-protein by demographic group (age and gender) and location (slums and non-slums). The secondary outcome is seroprevalence at slum and non-slum levels. Sera was tested via chemiluminescence (CLIA) using Abbott Diagnostics ArchitectTM N-protein based test. Seroprevalence was calculated using weights to match the population distribution by age and gender and accounting for imperfect sensitivity and specificity of the test. Results: The positive test rate was 54.1% (95% CI: 52.7 to 55.6) and 16.1% (95% CI: 14.9 to 17.4) in slums and non-slums, respectively, a difference of 38 percentage points (P < 0.001). Accounting for imperfect accuracy of tests (e.g., sensitivity, 0.90; specificity 1.00), seroprevalence was as high as 58.4% (95% CI: 56.8 to 59.9) and 17.3% (95% CI: 16 to 18.7) in slums and non-slums, respectively. Conclusions: The high seroprevalence in slums implies a moderate infection fatality rate. The stark difference in seroprevalence across slums and non-slums has implications for the efficacy of social distancing, the level of herd immunity, and equity. It underlines the importance of geographic specificity and urban structure in modeling SARS-CoV-2.

Authoritarian Governments Appear to Manipulate COVID Data (preprint)

Because SARS-Cov-2 (COVID-19) statistics affect economic policies and political outcomes, governments have an incentive to control them. Manipulation may be less likely in democracies, which have checks to ensure transparency. We show that data on disease burden bear indicia of data modification by authoritarian governments relative to democratic governments. First, data on COVID-19 cases and deaths from authoritarian governments show significantly less variation from a 7 day moving average. Because governments have no reason to add noise to data, lower deviation is evidence that data may be massaged. Second, data on COVID-19 deaths from authoritarian governments do not follow Benford's law, which describes the distribution of leading digits of numbers. Deviations from this law are used to test for accounting fraud. Smoothing and adjustments to COVID-19 data may indicate other alterations to these data and a need to account for such alterations when tracking the disease.

Prevalence of SARS-CoV-2 among workers returning to Bihar gives snapshot of COVID across India (preprint)

India has reported the fourth highest number of confirmed SARS-CoV-2 cases worldwide. Because there is little community testing for COVID, this case count is likely an underestimate. When India partially exited from lockdown on May 4, 2020, millions of daily laborers left cities for their rural family homes. RNA testing on a near-random sample of laborers returning to the state of Bihar is used to estimate positive testing rate for COVID across India for a 6-week period immediately following the initial lifting of India's lockdown. Positive testing rates among returning laborers are only moderately correlated with, and 21% higher than, Indian states' official reports, which are not based on random sampling. Higher prevalence among returning laborers may also reflect greater COVID spread in crowded poor communities such as slums.