Estimating COVID-19‒ Related Mortality in India: An Epidemiological Challenge With Insufficient Data

Large and unequal life expectancy declines during the COVID-19 pandemic in India in 2020

Global population health during the COVID-19 pandemic is poorly understood because of weak mortality monitoring in low- and middle-income countries. High-quality survey data on 765,180 individuals, representative of one-fourth of India's population, uncover patterns missed by incomplete vital statistics and disease surveillance. Compared to 2019, life expectancy at birth was 2.6 years lower and mortality was 17% higher in 2020, implying 1.19 million excess deaths in 2020. Life expectancy declines in India were larger and had a younger age profile than in high-income countries. Increases in mortality were greater than expected based on observed seroprevalence and international infection fatality rates, most prominently among the youngest and older age groups. In contrast to global patterns, females in India experienced a life expectancy decline that was 1 year larger than losses for males. Marginalized social groups experienced greater declines than the most privileged social group. These findings uncover large and unequal mortality impacts during the pandemic in the world's most populous country.

Construction and validation of a covariate-based model for district-level estimation of excess deaths due to COVID-19 in India

Background

Different statistical approaches for estimating excess deaths due to coronavirus disease 2019 (COVID-19) pandemic have led to varying estimates. In this study, we developed and validated a covariate-based model (CBM) with imputation for prediction of district-level excess deaths in India.

Methods

We used data extracted from deaths registered under the Civil Registration System for 2015-19 for 684 of 713 districts in India to estimate expected deaths for 2020 through a negative binomial regression model (NBRM) and to calculate excess observed deaths. Specifically, we used 15 covariates across four domains (state, health system, population, COVID-19) in a zero inflated NBRM to identify covariates significantly (P < 0.05) associated with excess deaths estimate in 460 districts. We then validated this CBM in 140 districts by comparing predicted and estimated excess. For 84 districts with missing covariates, we validated the imputation with CBM by comparing estimated with predicted excess deaths. We imputed covariate data to predict excess deaths for 29 districts which did not have data on deaths.

Results

The share of elderly and urban population, the under-five mortality rate, prevalence of diabetes, and bed availability were significantly associated with estimated excess deaths and were used for CBM. The mean of the CBM-predicted excess deaths per district (x̄ = 989, standard deviation (SD) = 1588) was not significantly different from the estimated one (x̄ = 1448, SD = 3062) (P = 0.25). The estimated excess deaths (n = 67 540; 95% confidence interval (CI) = 35 431, 99 648) were similar to the predicted excess death (n = 64 570; 95% CI = 54 140, 75 000) by CBM with imputation. The total national estimate of excess deaths for all 713 districts was 794 989 (95% CI = 664 895, 925 082).

Conclusions

A CBM with imputation can be used to predict excess deaths in an appropriate context.

Conventional and frugal methods of estimating COVID-19-related excess deaths and undercount factors

Across the world, the officially reported number of COVID-19 deaths is likely an undercount. Establishing true mortality is key to improving data transparency and strengthening public health systems to tackle future disease outbreaks. In this study, we estimated excess deaths during the COVID-19 pandemic in the Pune region of India. Excess deaths are defined as the number of additional deaths relative to those expected from pre-COVID-19-pandemic trends. We integrated data from: (a) epidemiological modeling using pre-pandemic all-cause mortality data, (b) discrepancies between media-reported death compensation claims and official reported mortality, and (c) the "wisdom of crowds" public surveying. Our results point to an estimated 14,770 excess deaths [95% CI 9820-22,790] in Pune from March 2020 to December 2021, of which 9093 were officially counted as COVID-19 deaths. We further calculated the undercount factor-the ratio of excess deaths to officially reported COVID-19 deaths. Our results point to an estimated undercount factor of 1.6 [95% CI 1.1-2.5]. Besides providing similar conclusions about excess deaths estimates across different methods, our study demonstrates the utility of frugal methods such as the analysis of death compensation claims and the wisdom of crowds in estimating excess mortality.

Influence of COVID-19 pandemic in India on coronary artery disease clinical presentation, angiography, interventions and in-hospital outcomes: a single centre prospective registry-based observational study

OBJECTIVE

The study examined the influence of the COVID-19 pandemic in India on variation in clinical features, management and in-hospital outcomes in patients undergoing percutaneous coronary intervention (PCI).

DESIGN

Prospective registry-based observational study.

SETTING

A tertiary care hospital in India participant in the American College of Cardiology CathPCI Registry.

PARTICIPANTS

7089 successive patients who underwent PCI from April 2018 to March 2023 were enrolled (men 5627, women 1462). Details of risk factors, clinical presentation, coronary angiography, coronary interventions, clinical management and in-hospital outcomes were recorded. Annual data were classified into specific COVID-19 periods according to Government of India guidelines as pre-COVID-19 (April 2018 to March 2019, n=1563; April 2019 to March 2020, n=1594), COVID-19 (April 2020 to March 2020, n=1206; April 2021 to March 2022, n=1223) and post-COVID-19 (April 2022 to March 2023, n=1503).

RESULTS

Compared with the patients in pre-COVID-19 and post-COVID-19 periods, during the first COVID-19 year, patients had more hypertension, non-ST elevation myocardial infarction (NSTEMI), lower left ventricular ejection fraction (LVEF) and multivessel coronary artery disease (CAD). In the second COVID-19 year, patients had more STEMI, lower LVEF, multivessel CAD, primary PCI, multiple stents and more vasopressor and mechanical support. There were 99 (1.4%) in-hospital deaths which in the successive years were 1.2%, 1.4%, 0.8%, 2.4% and 1.3%, respectively (p=0.019). Compared with the baseline year, deaths were slightly lower in the first COVID-19-year (age-sex adjusted OR 0.68, 95% CI 0.31 to 1.47) but significantly more in the second COVID-19-year (OR 1.97, 95% CI 1.10 to 3.54). This variation attenuated following adjustment for clinical presentation, extent of CAD, in-hospital treatment and duration of hospitalisation.

CONCLUSIONS

In-hospital mortality among patients with CAD undergoing PCI was significantly higher in the second year of the COVID-19 pandemic in India and could be one of the reasons for excess deaths in the country. These patients had more severe CAD, lower LVEF, and more vasopressor and mechanical support and duration of hospitalisation.

Trajectories and correlates of poor mental health in India over the course of the COVID-19 pandemic: a nationwide survey

INTRODUCTION

The COVID-19 pandemic had large impacts on mental health; however, most existing evidence is focused on the initial lockdown period and high-income contexts. By assessing trajectories of mental health symptoms in India over 2 years, we aim to understand the effect of later time periods and pandemic characteristics on mental health in a lower-middle income context.

METHODS

We used data from the Real-Time Insights of COVID-19 in India cohort study (N=3709). We used covariate-adjusted linear regression models with generalised estimating equations to assess associations between mental health (Patient Health Questionnaire (PHQ-4) score; range 0-12) and pandemic periods as well as pandemic characteristics (COVID-19 cases and deaths, government stringency, self-reported financial impact, COVID-19 infection in the household) and explored effect modification by age, gender and rural/urban residence.

RESULTS

Mental health symptoms dropped immediately following the lockdown period but rose again during the delta and omicron waves. Associations between mental health and later pandemic stages were stronger for adults 45 years of age and older (p<0.001). PHQ-4 scores were significantly associated with all pandemic characteristics considered, including estimated COVID-19 deaths (PHQ-4 difference of 0.10 units; 95% CI 0.06 to 0.13), government stringency index (0.14 units; 95% CI 0.11 to 0.18), self-reported major financial impacts (1.20 units; 95% CI 1.09 to 1.32) and COVID-19 infection in the household (0.36 units; 95% CI 0.23 to 0.50).

CONCLUSION

While the lockdown period and associated financial stress had the largest mental health impacts on Indian adults, the effects of the pandemic on mental health persisted over time, especially among middle-aged and older adults. Results highlight the importance of investments in mental health supports and services to address the consequences of cyclical waves of infections and disease burden due to COVID-19 or other emerging pandemics.

Public health factors help explain cross country heterogeneity in excess death during the COVID19 pandemic

The COVID-19 pandemic has taken a devastating toll around the world. Since January 2020, the World Health Organization estimates 14.9 million excess deaths have occurred globally. Despite this grim number quantifying the deadly impact, the underlying factors contributing to COVID-19 deaths at the population level remain unclear. Prior studies indicate that demographic factors like proportion of population older than 65 and population health explain the cross-country difference in COVID-19 deaths. However, there has not been a comprehensive analysis including variables describing government policies and COVID-19 vaccination rate. Furthermore, prior studies focus on COVID-19 death rather than excess death to assess the impact of the pandemic. Through a robust statistical modeling framework, we analyze 80 countries and show that actionable public health efforts beyond just the factors intrinsic to each country are important for explaining the cross-country heterogeneity in excess death.

Trajectories and correlates of poor mental health in India over the course of the COVID-19 pandemic: a nation-wide survey

Introduction

The COVID-19 pandemic had large impacts on mental health; however, most existing evidence is focused on the initial lockdown period and high-income contexts. By assessing trajectories of mental health symptoms in India over two years, we aim to understand the effect of later time periods and pandemic characteristics on mental health in a lower-middle income context.

Methods

We used data from the Real-Time Insights of COVID-19 in India (RTI COVID-India) cohort study (N=3,662). We used covariate-adjusted linear regression models with generalized estimating equations to assess associations between mental health (PHQ-4 score) and pandemic periods as well as pandemic characteristics (COVID-19 cases and deaths, government stringency, self-reported financial impact, COVID-19 infection in the household) and explored effect modification by age, gender, and rural/urban residence.

Results

Mental health symptoms dropped immediately following the lockdown period but rose again during the delta and omicron waves. Associations between mental health and later pandemic stages were stronger for adults 45 years of age and older (p<0.001). PHQ-4 scores were significantly and independently associated with all pandemic characteristics considered, including estimated COVID-19 deaths (PHQ-4 difference of 0.041 SD units; 95% Confidence Interval 0.030 - 0.053), government stringency index (0.060 SD units; 0.048 - 0.072), self-reported major financial impacts (0.45 SD units; 0.41-0.49), and COVID-19 infection in the household (0.11 SD units; 0.07-0.16).

Conclusion

While the lockdown period and associated financial stress had the largest mental health impacts on Indian adults, the effects of the pandemic on mental health persisted over time, especially among middle-age and older adults. Results highlight the importance of investments in mental health supports and services to address the consequences of cyclical waves of infections and disease burden due to COVID-19 or other emerging pandemics.

The making of India's COVID-19 disaster: A Disaster Risk Management (DRM) Assemblage analysis

This article analyses the suite of policies and measures enacted by the Indian Union Government in response to the COVID-19 pandemic through apparatuses of disaster management. We focus on the period from the onset of the pandemic in early 2020, until mid-2021. This holistic review adopts a Disaster Risk Management (DRM) Assemblage conceptual approach to make sense of how the COVID-19 disaster was made possible and importantly how it was responded to, managed, exacerbated, and experienced as it continued to emerge. This approach is grounded in literature from critical disaster studies and geography. The analysis also draws on a wide range of other disciplines, ranging from epidemiology to anthropology and political science, as well as grey literature, newspaper reports, and official policy documents. The article is structured into three sections that investigate in turn and at different junctures the role of governmentality and disaster politics; scientific knowledge and expert advice, and socially and spatially differentiated disaster vulnerabilities in shaping the COVID-19 disaster in India. We put forward two main arguments on the basis of the literature reviewed. One is that both the impacts of the virus spread and the lockdown-responses to it affected already marginalised groups disproportionately. The other is that managing the COVID-19 pandemic through disaster management assemblage/apparatuses served to extend centralised executive authority in India. These two processes are demonstrated to be continuations of pre-pandemic trends. We conclude that evidence of a paradigm shift in India's approach to disaster management remains thin on the ground.

Attributed causes of excess mortality during the COVID-19 pandemic in a south Indian city

Globally, excess deaths during 2020-21 outnumbered documented COVID-19 deaths by 9.5 million, primarily driven by deaths in low- and middle-income countries (LMICs) with limited vital surveillance. Here we unravel the contributions of probable COVID-19 deaths from other changes in mortality related to pandemic control measures using medically-certified death registrations from Madurai, India-an urban center with well-functioning vital surveillance. Between March, 2020 and July, 2021, all-cause deaths in Madurai exceeded expected levels by 30% (95% confidence interval: 27-33%). Although driven by deaths attributed to cardiovascular or cerebrovascular conditions, diabetes, senility, and other uncategorized causes, increases in these attributions were restricted to medically-unsupervised deaths, and aligned with surges in confirmed or attributed COVID-19 mortality, likely reflecting mortality among unconfirmed COVID-19 cases. Implementation of lockdown measures was associated with a 7% (0-13%) reduction in all-cause mortality, driven by reductions in deaths attributed to injuries, infectious diseases and maternal conditions, and cirrhosis and other liver conditions, respectively, but offset by a doubling in cancer deaths. Our findings help to account for gaps between documented COVID-19 mortality and excess all-cause mortality during the pandemic in an LMIC setting.

Association of greenness with COVID-19 deaths in India: An ecological study at district level

BACKGROUND

The world has witnessed a colossal death toll due to the novel coronavirus disease-2019 (COVID-19). A few environmental epidemiology studies have identified association of environmental factors (air pollution, greenness, temperature, etc.) with COVID-19 incidence and mortality, particularly in developed countries. India, being one of the most severely affected countries by the pandemic, still has a dearth of research exploring the linkages of environment and COVID-19 pandemic.

OBJECTIVES

We evaluate whether district-level greenness exposure is associated with a reduced risk of COVID-19 deaths in India.

METHODS

We used average normalized difference vegetation index (NDVI) from January to March 2019, derived by Oceansat-2 satellite, to represent district-level greenness exposure. COVID-19 death counts were obtained through May 1, 2021 (around the peak of the second wave) from an open portal: covid19india.org. We used hierarchical generalized negative binomial regressions to check the associations of greenness with COVID-19 death counts. Analyses were adjusted for air pollution (PM2.5), temperature, rainfall, population density, proportion of older adults (50 years and above), sex ratio over age 50, proportions of rural population, household overcrowding, materially deprived households, health facilities, and secondary school education.

RESULTS

Our analyses found a significant association between greenness and reduced risk of COVID-19 deaths. Compared to the districts with the lowest NDVI (quintile 1), districts within quintiles 3, 4, and 5 have respectively, around 32% [MRR = 0.68 (95% CI: 0.51, 0.88)], 39% [MRR = 0.61 (95% CI: 0.46, 0.80)], and 47% [MRR = 0.53 (95% CI: 0.40, 0.71)] reduced risk of COVID-19 deaths. The association remains consistent for analyses restricted to districts with a rather good overall death registration (>80%).

CONCLUSION

Though cause-of-death statistics are limited, we confirm that exposure to greenness was associated with reduced district-level COVID-19 deaths in India. However, material deprivation and air pollution modify this association.

Role of multiresolution vulnerability indices in COVID-19 spread in India: a Bayesian model-based analysis

OBJECTIVES

COVID-19 has differentially affected countries, with health infrastructure and other related vulnerability indicators playing a role in determining the extent of its spread. Vulnerability of a geographical region to COVID-19 has been a topic of interest, particularly in low-income and middle-income countries like India to assess its multifactorial impact on incidence, prevalence or mortality. This study aims to construct a statistical analysis pipeline to compute such vulnerability indices and investigate their association with metrics of the pandemic growth.

DESIGN

Using publicly reported observational socioeconomic, demographic, health-based and epidemiological data from Indian national surveys, we compute contextual COVID-19 Vulnerability Indices (cVIs) across multiple thematic resolutions for different geographical and spatial administrative regions. These cVIs are then used in Bayesian regression models to assess their impact on indicators of the spread of COVID-19.

SETTING

This study uses district-level indicators and case counts data for the state of Odisha, India.

PRIMARY OUTCOME MEASURE

We use instantaneous R (temporal average of estimated time-varying reproduction number for COVID-19) as the primary outcome variable in our models.

RESULTS

Our observational study, focussing on 30 districts of Odisha, identified housing and hygiene conditions, COVID-19 preparedness and epidemiological factors as important indicators associated with COVID-19 vulnerability.

CONCLUSION

Having succeeded in containing COVID-19 to a reasonable level during the first wave, the second wave of COVID-19 made greater inroads into the hinterlands and peripheral districts of Odisha, burdening the already deficient public health system in these areas, as identified by the cVIs. Improved understanding of the factors driving COVID-19 vulnerability will help policy makers prioritise resources and regions, leading to more effective mitigation strategies for the present and future.

Meta-analysis of nationwide SARS-CoV-2 infection fatality rates in India

There has been much discussion and debate around underreporting of deaths in India in media articles and in the scientific literature. In this brief report, we aim to meta-analyze the available/inferred estimates of infection fatality rates for SARS-CoV-2 in India based on the existent literature. These estimates account for uncaptured deaths and infections. We consider empirical excess death estimates based on all-cause mortality data as well as disease transmission-based estimates that rely on assumptions regarding infection transmission and ascertainment rates in India. Through an initial systematic review (Zimmermann et al., 2021) that followed PRISMA guidelines and comprised a search of databases PubMed, Embase, Global Index Medicus, as well as BioRxiv, MedRxiv, and SSRN for preprints (accessed through iSearch) on July 3, 2021, we further extended the search verification through May 26, 2022. The screening process yielded 15 studies qualitatively analyzed, of which 9 studies with 11 quantitative estimates were included in the meta-analysis. Using a random effects meta-analysis framework, we obtain a pooled estimate of nationwide infection fatality rate (defined as the ratio of estimated deaths over estimated infections) and a corresponding confidence interval. Death underreporting from excess deaths studies varies by a factor of 6.1-13.0 with nationwide cumulative excess deaths ranging from 2.6-6.3 million, whereas the underreporting from disease transmission-based studies varies by a factor of 3.5-7.3 with SARS-CoV-2 related nationwide estimated total deaths ranging from 1.4-3.4 million, through June 2021 with some estimates extending to 31 December 2021. Underreporting of infections was found previously (Zimmermann et al., 2021) to be 24.9 (relying on the latest 4th nationwide serosurvey from 14 June-6 July 2021 prior to launch of the vaccination program). Conservatively, by considering the lower values of these available estimates, we infer that approximately 95% of infections and 71% of deaths were not accounted for in the reported figures in India. Nationwide pooled infection fatality rate estimate for India is 0.51% (95% confidence interval [CI]: 0.45%- 0.58%). We often tend to compare countries across the world in terms of total reported cases and deaths. Although the US has the highest number of reported cumulative deaths globally, after accounting for underreporting, India appears to have the highest number of cumulative total deaths (reported + unreported). However, the large number of estimated infections in India leads to a lower infection fatality rate estimate than the US, which in part is due to the younger population in India. We emphasize that the age-structure of different countries must be taken into consideration while making such comparisons. More granular data are needed to examine heterogeneities across various demographic groups to identify at-risk and underserved populations with high COVID mortality; the hope is that such disaggregated mortality data will soon be made available for India.

All-cause excess mortality across 90 municipalities in Gujarat, India, during the COVID-19 pandemic (March 2020-April 2021)

Official COVID-19 mortality statistics are strongly influenced by local diagnostic capacity, strength of the healthcare and vital registration systems, and death certification criteria and capacity, often resulting in significant undercounting of COVID-19 attributable deaths. Excess mortality, which is defined as the increase in observed death counts compared to a baseline expectation, provides an alternate measure of the mortality shock-both direct and indirect-of the COVID-19 pandemic. Here, we use data from civil death registers from a convenience sample of 90 (of 162) municipalities across the state of Gujarat, India, to estimate the impact of the COVID-19 pandemic on all-cause mortality. Using a model fit to weekly data from January 2019 to February 2020, we estimated excess mortality over the course of the pandemic from March 2020 to April 2021. During this period, the official government data reported 10,098 deaths attributable to COVID-19 for the entire state of Gujarat. We estimated 21,300 [95% CI: 20, 700, 22, 000] excess deaths across these 90 municipalities in this period, representing a 44% [95% CI: 43%, 45%] increase over the expected baseline. The sharpest increase in deaths in our sample was observed in late April 2021, with an estimated 678% [95% CI: 649%, 707%] increase in mortality from expected counts. The 40 to 65 age group experienced the highest increase in mortality relative to the other age groups. We found substantial increases in mortality for males and females. Our excess mortality estimate for these 90 municipalities, representing approximately at least 8% of the population, based on the 2011 census, exceeds the official COVID-19 death count for the entire state of Gujarat, even before the delta wave of the pandemic in India peaked in May 2021. Prior studies have concluded that true pandemic-related mortality in India greatly exceeds official counts. This study, using data directly from the first point of official death registration data recording, provides incontrovertible evidence of the high excess mortality in Gujarat from March 2020 to April 2021.

Extending the susceptible-exposed-infected-removed (SEIR) model to handle the false negative rate and symptom-based administration of COVID-19 diagnostic tests: SEIR-fansy

False negative rates of severe acute respiratory coronavirus 2 diagnostic tests, together with selection bias due to prioritized testing can result in inaccurate modeling of COVID-19 transmission dynamics based on reported "case" counts. We propose an extension of the widely used Susceptible-Exposed-Infected-Removed (SEIR) model that accounts for misclassification error and selection bias, and derive an analytic expression for the basic reproduction number R 0 as a function of false negative rates of the diagnostic tests and selection probabilities for getting tested. Analyzing data from the first two waves of the pandemic in India, we show that correcting for misclassification and selection leads to more accurate prediction in a test sample. We provide estimates of undetected infections and deaths between April 1, 2020 and August 31, 2021. At the end of the first wave in India, the estimated under-reporting factor for cases was at 11.1 (95% CI: 10.7,11.5) and for deaths at 3.58 (95% CI: 3.5,3.66) as of February 1, 2021, while they change to 19.2 (95% CI: 17.9, 19.9) and 4.55 (95% CI: 4.32, 4.68) as of July 1, 2021. Equivalently, 9.0% (95% CI: 8.7%, 9.3%) and 5.2% (95% CI: 5.0%, 5.6%) of total estimated infections were reported on these two dates, while 27.9% (95% CI: 27.3%, 28.6%) and 22% (95% CI: 21.4%, 23.1%) of estimated total deaths were reported. Extensive simulation studies demonstrate the effect of misclassification and selection on estimation of R 0 and prediction of future infections. A R-package SEIRfansy is developed for broader dissemination.

An alternative estimation of the death toll of the Covid-19 pandemic in India

The absence of reliable registration of Covid-19 deaths in India has prevented proper assessment and monitoring of the coronavirus pandemic. In addition, India's relatively young age structure tends to conceal the severity of Covid-19 mortality, which is concentrated in older age groups. In this paper, we present four different demographic samples of Indian populations for which we have information on both their demographic structures and death outcomes. We show that we can model the age distribution of Covid-19 mortality in India and use this modeling to estimate Covid-19 mortality in the country. Our findings point to a death toll of approximately 3.2-3.7 million persons by early November 2021. Once India's age structure is factored in, these figures correspond to one of the most severe cases of Covid-19 mortality in the world. India has recorded after February 2021 the second outbreak of coronavirus that has affected the entire country. The accuracy of official statistics of Covid-19 mortality has been questioned, and the real number of Covid-19 deaths is thought to be several times higher than reported. In this paper, we assembled four independent population samples to model and estimate the level of Covid-19 mortality in India. We first used a population sample with the age and sex of Covid-19 victims to develop a Gompertz model of Covid-19 mortality in India. We applied and adjusted this mortality model on two other national population samples after factoring in the demographic characteristics of these samples. We finally derive from these samples the most reasonable estimate of Covid-19 mortality level in India and confirm this result using a fourth population sample. Our findings point to a death toll of about 3.2-3.7 million persons by late May 2021. This is by far the largest number of Covid-19 deaths in the world. Once standardized for age and sex structure, India's Covid-19 mortality rate is above Brazil and the USA. Our analysis shows that existing population samples allow an alternative estimation of deaths due to Covid-19 in India. The results imply that only one out of 7-8 deaths appear to have been recorded as a Covid-19 death in India. The estimates also point to a very high Covid-19 mortality rate, which is even higher after age and sex standardization. The magnitude of the pandemic in India requires immediate attention. In the absence of effective remedies, this calls for a strong response based on a combination of non-pharmaceutical interventions and the scale-up of vaccination to make them accessible to all, with an improved surveillance system to monitor the progression of the pandemic and its spread across India's regions and social groups.

Assessment of COVID-19 data reporting in 100+ websites and apps in India

India is among the top three countries in the world both in COVID-19 case and death counts. With the pandemic far from over, timely, transparent, and accessible reporting of COVID-19 data continues to be critical for India's pandemic efforts. We systematically analyze the quality of reporting of COVID-19 data in over one hundred government platforms (web and mobile) from India. Our analyses reveal a lack of granular data in the reporting of COVID-19 surveillance, vaccination, and vacant bed availability. As of 5 June 2021, age and gender distribution are available for less than 22% of cases and deaths, and comorbidity distribution is available for less than 30% of deaths. Amid rising concerns of undercounting cases and deaths in India, our results highlight a patchy reporting of granular data even among the reported cases and deaths. Furthermore, total vaccination stratified by healthcare workers, frontline workers, and age brackets is reported by only 14 out of India's 36 subnationals (states and union territories). There is no reporting of adverse events following immunization by vaccine and event type. By showing what, where, and how much data is missing, we highlight the need for a more responsible and transparent reporting of granular COVID-19 data in India.

Comparing COVID-19 mortality across selected states in India: The role of age structure

Background

Mortality rates provide an opportunity to identify and act on the health system intervention for preventing deaths. Hence, it is essential to appreciate the influence of age structure while reporting mortality for a better summary of the magnitude of the epidemic.

Objectives

We described and compared the pattern of COVID-19 mortality standardized by age between selected states and India from January to November 2020.

Methods

We initially estimated the Indian population for 2020 using the decadal growth rate from the previous census (2011). This was followed by estimations of crude and age-adjusted mortality rate per million for India and the selected states. We used this information to perform indirect-standardization and derive the age-standardized mortality rates for the states for comparison. In addition, we derived a ratio for age-standardized mortality to compare across age groups within the state. We extracted information regarding COVID-19 deaths from the Integrated Disease Surveillance Programme special surveillance portal up to November 16, 2020.

Results

The crude mortality rate of India stands at 88.9 per million population (118,883/1,337,328,910). Age-adjusted mortality rate (per-million) was highest for Delhi (300.5) and lowest for Kerala (35.9). The age-standardized mortality rate (per million) for India is (<15 years = 1.6, 15–29 years = 6.3, 30–44 years = 35.9, 45–59 years = 198.8, 60–74 years = 571.2, ≥75 years = 931.6). The ratios for age-standardized mortality increase proportionately from 45 to 59 years age group across all the states.

Conclusion

There is high COVID-19 mortality not only among the elderly ages, but we also identified heavy impact of COVID-19 on the working population. Therefore, we recommend further evaluation of age-adjusted mortality for all States and inclusion of variables like gender, socio-economic status for standardization while identifying at-risk populations and implementing priority public health actions.