Cost-effectiveness of fractional doses of COVID-19 vaccine boosters in India

Background Coronavirus disease 2019 (COVID-19) continues to be a major global public health crisis in 2022 that exacts significant human and economic costs. Booster vaccination of individuals can improve waning immunity and reduce the impact of community epidemics. Methods Using an epidemiological model that incorporates population-level SARS-CoV-2 transmission and waning of vaccine-derived immunity, we identify the hypothetical potential of mass vaccination with fractionated vaccine doses specific to ChAdOx1 nCoV-19 (AZD1222 [Covishield];AstraZeneca) as an optimal and cost-effective strategy in India's Omicron outbreak. Findings We find that the optimal strategy is 1/8 fractional dosing under mild (Re ∼ 1.2) and rapid (Re ∼ 5) transmission scenarios, leading to an estimated $6 (95% CI: -13, 26) billion and $2 (95% CI:-26, 30) billion in health-related net monetary benefit over 200 days, respectively. Rapid and broad use of fractional dosing for boosters, together with delivery costs divided by fractionation, could substantially gain more net monetary benefit by $11 (95% CI: -10, 33) and $2 (95% CI: -23, 28) billion, respectively, under the mild and rapid transmission scenarios. Conclusions Mass vaccination with fractional doses of COVID-19 vaccines to boost immunity in a vaccinated population could be a cost effective strategy for mitigating the public health costs of resurgences caused by vaccine-evasive variants and fractional dosing deserves further clinical and regulatory evaluation. Funding Financial support was provided by the AIR@InnoHK Programme from Innovation and Technology Commission of the Government of the Hong Kong Special Administrative Region. Graphical This analysis demonstrated that the use of fractional dose could offer greater net monetary benefit in both moderate and rapid transmission scenarios given the epidemiological and socioeconomic conditions in India in 2022. In the face of a vaccine shortage, fractional dosage of vaccinations would have additional beneficial public health benefits.

Cost effectiveness of fractional doses of COVID-19 vaccine boosters in India.

BACKGROUND: Coronavirus disease 2019 (COVID-19) continues to be a major global public health crisis that exacts significant human and economic costs. Booster vaccination of individuals can improve waning immunity and reduce the impact of community epidemics. METHODS: Using an epidemiological model that incorporates population-level severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and waning of vaccine-derived immunity, we identify the hypothetical potential of mass vaccination with fractionated vaccine doses specific to ChAdOx1 nCoV-19 (AZD1222 [Covishield]; AstraZeneca) as an optimal and cost-effective strategy in India's Omicron outbreak. FINDINGS: We find that the optimal strategy is 1/8 fractional dosing under mild (Re ∼ 1.2) and rapid (Re ∼ 5) transmission scenarios, leading to an estimated $6 (95% confidence interval [CI]: -13, 26) billion and $2 (95% CI: -26, 30) billion in health-related net monetary benefit over 200 days, respectively. Rapid and broad use of fractional dosing for boosters, together with delivery costs divided by fractionation, could substantially gain more net monetary benefit by $11 (95% CI: -10, 33) and $2 (95% CI: -23, 28) billion, respectively, under the mild and rapid transmission scenarios. CONCLUSIONS: Mass vaccination with fractional doses of COVID-19 vaccines to boost immunity in a vaccinated population could be a cost-effective strategy for mitigating the public health costs of resurgences caused by vaccine-evasive variants, and fractional dosing deserves further clinical and regulatory evaluation. FUNDING: Financial support was provided by the AIR@InnoHK Program from Innovation and Technology Commission of the Government of the Hong Kong Special Administrative Region.

JUE insights: Does mobility explain why slums were hit harder by COVID-19 in Mumbai, India?

SARS-CoV-2 has had a greater burden, as measured by rate of infection, in poorer communities within cities. For example, 55% of Mumbai slums residents had antibodies to COVID-19, 3.2 times the seroprevalence in non-slum areas of the city according to a sero-survey done in July 2020. One explanation is that government suppression was less severe in poorer communities, either because the poor were more likely to be exempt or unable to comply. Another explanation is that effective suppression itself accelerated the epidemic in poor neighborhoods because households are more crowded and residents share toilet and water facilities. We show there is little evidence for the first hypothesis in the context of Mumbai. Using location data from smart phones, we find that slum residents had nominally but not significantly (economically or statistically) higher mobility than non-slums prior to the sero-survey. We also find little evidence that mobility in non-slums was lower than in slums during lockdown, a subset of the period before the survey.

Using household rosters from survey data to estimate all-cause excess death rates during the COVID pandemic in India

Official statistics on deaths from COVID undercount deaths due to lack of testing. In developed countries, death registries are used to estimate excess deaths due to COVID during the pandemic. However, few developing countries had complete death registries even before the pandemic and the pandemic further stressed administrative capacities. As a substitute, we estimate all-cause excess deaths in India using the member rosters of a large, representative household panel survey. We estimate roughly 4.2 million excess deaths during the pandemic through February 2022. 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.

Assessing the burden of COVID-19 in developing countries: systematic review, meta-analysis and public policy implications.

INTRODUCTION: The infection fatality rate (IFR) of COVID-19 has been carefully measured and analysed 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 representative samples collected by February 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 analysed 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: In most locations in developing countries, 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 2 times 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 ensure medical equity to populations in developing countries through provision of vaccine doses and effective medications.

Modeling comparative cost-effectiveness of SARS-CoV-2 vaccine dose fractionation in India.

Given global Coronavirus Disease 2019 (COVID-19) vaccine shortages and inequity of vaccine distributions, fractionation of vaccine doses might be an effective strategy for reducing public health and economic burden, notwithstanding the emergence of new variants of concern. In this study, we developed a multi-scale model incorporating population-level transmission and individual-level vaccination to estimate the costs of hospitalization and vaccination and the economic benefits of reducing COVID-19 deaths due to dose-fractionation strategies in India. We used large-scale survey data of the willingness to pay together with data of vaccine and hospital admission costs to build the model. We found that fractional doses of vaccines could be an economically viable vaccination strategy compared to alternatives of either full-dose vaccination or no vaccination. Dose-sparing strategies could save a large number of lives, even with the emergence of new variants with higher transmissibility.

Using Household Rosters from Survey Data to Estimate All-cause Mortality during COVID in India

Official statistics on deaths in India during the COVID pandemic are either incomplete or are reported with a delay. To overcome this shortcoming, we estimate excess deaths in India using the household roster from a large panel data set, the Consumer Pyramids Household Survey, which reports attrition from death. We address the problem that the exact timing of death is not reported in two ways, via a moving average and differencing monthly deaths. We estimate roughly 4.5 million (95% CI: 2.8M to 6.2M) excess deaths over 16 months during the pandemic in India. While we cannot demonstrate causality between COVID and excess deaths, the pattern of excess deaths is consistent with COVID-associated mortality. Excess deaths peaked roughly during the two COVID waves in India;the age structure of excess deaths is right skewed relative to baseline, consistent with COVID infection fatality rates;and excess deaths are positively correlated with reported infections. Finally, we find that the incidence of excess deaths was disproportionately among the highest tercile of income-earners and was negatively associated with district-level mobility.

Explaining the Income and Consumption Effects of COVID in India

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.

Does Mobility Explain Why Slums Were Hit Harder by COVID-19 in Mumbai, India?

SARS-CoV-2 has had a greater burden, as measured by rate of infection, in poorer communities within cities. For example, 55% of Mumbai slums residents had antibodies to COVID-19, 3.2 times the seroprevalence in non-slum areas of the city according to a sero-survey done in July 2020. One explanation is that government suppression was less severe in poorer communities, either because the poor were more likely to be exempt or unable to comply. Another explanation is that effective suppression itself accelerated the epidemic in poor neighborhoods because households are more crowded and residents share toilet and water facilities. We show there is little evidence for the first hypothesis in the context of Mumbai. Using location data from smart phones, we find that slum residents had nominally but not significantly (economically or statistically) higher mobility than non-slums prior to the sero-survey. We also find little evidence that mobility in non-slums was lower than in slums during lockdown, a subset of the period before the survey.

Adaptive Control of COVID-19 Outbreaks in India: Local, Gradual, and Trigger-based Exit Paths from Lockdown

Managing the outbreak of COVID-19 in India constitutes an unprecedented health emergency in one of the largest and most diverse nations in the world. On May 4, 2020, India started the process of releasing its population from a national lockdown during which extreme social distancing was implemented. We describe and simulate an adaptive control approach to exit this situation, while maintaining the epidemic under control. Adaptive control is a flexible counter-cyclical policy approach, whereby different areas release from lockdown in potentially different gradual ways, dependent on the local progression of the dis- ease. Because of these features, adaptive control requires the ability to decrease or increase social distancing in response to observed and projected dynamics of the disease outbreak. We show via simulation of a stochastic Susceptible-Infected-Recovered (SIR) model and of a synthetic intervention (SI) model that adaptive control performs at least as well as immediate and full release from lockdown starting May 4 and as full release from lockdown after a month (i.e., after May 31). The key insight is that adaptive response provides the option to increase or decrease socioeconomic activity depending on how it affects disease progression and this freedom allows it to do at least as well as most other policy alternatives. We also discuss the central challenge to any nuanced release policy, including adaptive control, specifically learning how specific policies translate into changes in contact rates and thus COVID-19's reproductive rate in real time.

Representative estimates of COVID-19 infection fatality rates from four locations in India: cross-sectional study.

OBJECTIVES: To estimate age-specific and sex-specific mortality risk among all SARS-CoV-2 infections in four settings in India, a major lower-middle-income country and to compare age trends in mortality with similar estimates in high-income countries. DESIGN: Cross-sectional study. SETTING: India, multiple regions representing combined population >150 million. PARTICIPANTS: Aggregate infection counts were drawn from four large population-representative prevalence/seroprevalence surveys. Data on corresponding number of deaths were drawn from official government reports of confirmed SARS-CoV-2 deaths. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was age-specific and sex-specific infection fatality rate (IFR), estimated as the number of confirmed deaths per infection. The secondary outcome was the slope of the IFR-by-age function, representing increased risk associated with age. RESULTS: Among males aged 50-89, measured IFR was 0.12% in Karnataka (95% CI 0.09% to 0.15%), 0.42% in Tamil Nadu (95% CI 0.39% to 0.45%), 0.53% in Mumbai (95% CI 0.52% to 0.54%) and an imprecise 5.64% (95% CI 0% to 11.16%) among migrants returning to Bihar. Estimated IFR was approximately twice as high for males as for females, heterogeneous across contexts and rose less dramatically at older ages compared with similar studies in high-income countries. CONCLUSIONS: Estimated age-specific IFRs during the first wave varied substantially across India. While estimated IFRs in Mumbai, Karnataka and Tamil Nadu were considerably lower than comparable estimates from high-income countries, adjustment for under-reporting based on crude estimates of excess mortality puts them almost exactly equal with higher-income country benchmarks. In a marginalised migrant population, estimated IFRs were much higher than in other contexts around the world. Estimated IFRs suggest that the elderly in India are at an advantage relative to peers in high-income countries. Our findings suggest that the standard estimation approach may substantially underestimate IFR in low-income settings due to under-reporting of COVID-19 deaths, and that COVID-19 IFRs may be similar in low-income and high-income settings.

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

Abstract 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 access to toilets for men is associated with lower COVID-19 prevalence. However, 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 formal 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, indicating 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. This article is protected by copyright. All rights reserved.