CoChat
Sign Up
Cited by 45COVID-19 epidemiological studiesInsurance, Mortality, Demography, Risk ManagementCOVID-19 and healthcare impacts

Estimates of pandemic excess mortality in India based on civil registration data

Open access
Banaji, Murad, Gupta, Aashish·2022·PLOS Global Public Health
DOI 10.1371/journal.pgph.0000803PMC PMC10021303

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

The population health impacts of the COVID-19 pandemic are less well understood in low and middle-income countries, where mortality surveillance before the pandemic was patchy. Interpreting the limited all-cause mortality data available in India is challenging. We use existing data on all-cause mortality from civil registration systems of twelve Indian states comprising around 60% of the national population to understand the scale and timing of excess deaths in India during the COVID-19 pandemic. We carefully characterize the reasons why registration is incomplete and estimate the extent of coverage in the data. Comparing the pandemic period to 2019, we estimate excess mortality in twelve Indian states, and extrapolate our estimates to the rest of India. We explore sensitivity of the estimates to various assumptions. For the 12 states with available all-cause mortality data, we document an increase of 28% in deaths during April 2020–May 2021 relative to expectations from 2019. This level of increase in mortality, if it applies nationally, would imply 2.8–2.9 million excess deaths. More limited data from June 2021 increases national estimates of excess deaths during April 2020–June 2021 to 3.8 million. With more optimistic or pessimistic assumptions, excess deaths during this period could credibly lie between 2.8 million and 5.2 million. The scale of estimated excess deaths is broadly consistent with expectations based on seroprevalence and COVID-19 fatality rates observed internationally. Moreover, the timing of excess deaths and recorded COVID-19 deaths is similar–they rise and fall at the same time. The surveillance of pandemic mortality in India has been extremely poor, with 8–10 times as many excess deaths as officially recorded COVID-19 deaths. India is among the countries most severely impacted by the pandemic. Our approach highlights the utility of all-cause mortality data, as well as the significant challenges in interpreting it.

AI snapshot

Written from the paper’s full text.

TL;DR

Using civil registration data from 12 Indian states covering about 60% of the population, the paper estimates substantial pandemic excess mortality in India. It finds roughly 2.8–2.9 million excess deaths nationally during April 2020–May 2021, rising to about 3.8 million through June 2021, with a plausible range of 2.8–5.2 million.

Key findings

  • In the 12 states with available all-cause mortality data (STAR12), there were 1.3 million excess registrations during April 2020–May 2021, 27% more than expected from 2019; after scaling for coverage, this became 1.7 million excess deaths, a P-score of 28% and 2.1 excess deaths per 1,000 population.
  • National extrapolation gives 2.8–2.9 million excess deaths during April 2020–May 2021; extending estimates to June 2021 using limited data from three states gives about 3.8 million excess deaths, or a 34% increase over expected deaths, with optimistic/pessimistic estimates of 2.8 million and 5.2 million.
  • Estimated excess deaths were 8.5 times official COVID-19 deaths nationally during April 2020–May 2021, rising to 9.5 times when June 2021 is included; in STAR12 the ratio was 7.2 times up to May 2021.
  • Monthly excess deaths and recorded COVID-19 deaths rose and fell together, with a correlation coefficient of 0.98 during April 2020–May 2021; the estimates are broadly consistent with expected COVID-19 mortality based on seroprevalence and international infection fatality rates.

Methods

  • The study uses civil registration system all-cause mortality data from 12 Indian states (Andhra Pradesh, Bihar, Haryana, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Punjab, Rajasthan, Tamil Nadu, West Bengal), comprising about 59% of India’s 2019 population and deaths, with monthly data from January 2018 to at least May 2021; June 2021 data are available only for Andhra Pradesh, Karnataka, and Punjab.
  • Expected deaths are set at 2019 monthly levels; excess registrations are scaled by state-level estimates of coverage in the available data from the 2019 CRS report, then extrapolated nationally using mortality-based and P-score-based approaches.
  • Robustness checks include optimistic and pessimistic scenarios and Monte Carlo simulations (10,000 runs) varying baseline coverage by ±15%, pandemic-period coverage by ±5%, and mortality impact outside STAR12 between 80% and 120% of STAR12.

Limitations

  • The paper states that the data are not up to date, June 2021 data come from only three states, and there is uncertainty about changes in registration coverage, baseline crude death rate, pre-pandemic registration completion, and whether mortality impact outside STAR12 matched STAR12; small shifts in coverage can substantially change excess mortality estimates.
  • The paper states that the association between excess deaths and recorded COVID-19 deaths is ecological and associational, not causal, and that credible state-level estimates would require more careful case-by-case analysis.
  • Evident from the text: the estimates depend heavily on assumptions about incomplete civil registration data, registration disruption and recovery during the pandemic, and the absence of direct cause-of-death information.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1371/journal.pgph.0000803

Cites

36 of 36 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 69 cited sources checked · 0 supported · 0 flagged

  • Findingreasoning

    The estimated crude death rate in STAR12 was close to the national value.

    “The estimated CDR in STAR12 was thus close to the national value.”
    Quote in the paperNot a substantial claim
  • Findingown data

    In the remainder of India outside STAR12, around 78% of deaths occurring during 2019 were registered.

    “around 92% of deaths occurring during 2019 in STAR12 were registered, as against 78% in the remainder of the country.”
    Quote in the paperSubstantial claim
  • Findingown data

    In STAR12, around 92% of deaths occurring during 2019 were registered.

    “around 92% of deaths occurring during 2019 in STAR12 were registered, as against 78% in the remainder of the country.”
    • Also stated in: § 2.2 The available data from twelve states · plain
      “Total (STAR12) | 5,222,286 | 4,811,595 | 92.1 | 4,102,882 | 78.6”
    Quote in the paperSubstantial claim
  • Findingown data

    The ratio of excess deaths to officially recorded COVID-19 deaths in India was 8-10 times.

    “The surveillance of pandemic mortality in India has been extremely poor, with 8–10 times as many excess deaths as officially recorded COVID-19 deaths.”
    • Also stated in: § 1. Introduction · hedged
      “by June 2021, we estimate a ratio of excess deaths to official COVID-19 deaths of over 9”
    Quote in the paperSubstantial claim
  • Findingown data

    In the twelve Indian states with available all-cause mortality data (STAR12), deaths during April 2020-May 2021 were 28% higher than expected from 2019 data.

    “For the 12 states with available all-cause mortality data, we document an increase of 28% in deaths during April 2020–May 2021 relative to expectations from 2019.”
    • Also stated in: § 3.1 Central estimates of excess mortality in STAR12 during April 2020–May 2021 · plain
      “we find 1.7M excess deaths during April 2020–May 2021 in STAR12, corresponding to a P-score of 28% in these states relative to expectations from 2019.”
    Quote in the paperSubstantial claim
  • May combine two claims. 34
  • Classified as probably not a substantial claim. 16

  • May combine two claims.

    Findingreasoning

    In the pessimistic scenario, national excess deaths up to May 2021 were 4.0-4.1 million, rising to 5.2 million by June 2021.

    “This equates to a total of 4.0–4.1M excess deaths up to May 2021, rising to 5.2M by June.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingreasoning

    In the optimistic scenario, national excess deaths up to May 2021 were 1.9-2.0 million, rising to 2.8 million by June 2021.

    “This equates to a total of 1.9–2.0M excess deaths up to May 2021, rising to 2.8M by June.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In STAR12, there were 1.3 million excess registrations during April 2020-May 2021, amounting to 27% more registrations than expected from 2019 data.

    “We find 1.3M excess registrations in STAR12 during April 2020–May 2021, amounting to 27% more registrations than expected from 2019 data.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In individual STAR12 states in 2019, death registration varied between 51.6% and 100%, and coverage in the data varied from 48% to 100%.

    “in individual states in STAR12 in 2019, death registration varied between 51.6% and 100%. Coverage in the data during 2019 varied from 48% to 100%.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The limited data from June 2021 shows considerable further rises in deaths, perhaps partly as a result of delays in registration.

    “The limited data from June 2021 shows considerable further rises, perhaps partly as a result of delays in registration.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingreasoning

    The estimated population growth rate in the STAR12 states is also around 1% per year.

    “This is also the estimated population growth rate in states whose data we use below.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In the optimistic scenario the ratio of excess deaths to recorded COVID-19 deaths is 6.9, while in the pessimistic scenario it is around 13.0.

    “In the optimistic scenario detailed in the previous Section this ratio is 6.9, while in the pessimistic scenario it is around 13.0.”
    Quote in the paperNot a substantial claim
    • The authors state this as an assumption: The optimistic and pessimistic scenarios are based on uniform changes, most importantly uniform decreases or increases in registration coverage, across the states of STAR12.

  • May combine two claims.

    Findingreasoning

    These coverage changes would have little effect on the overall estimate of pandemic excess deaths but would raise the first-wave ratio of excess deaths to official COVID-19 deaths to 7.8 and lower the second-wave ratio to 10.3.

    “These changes would have little effect on the overall estimate of pandemic excess deaths but would raise the first wave ratio of excess deaths to official COVID-19 deaths to 7.8, and lower the second–wave ratio to 10.3.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Karnataka saw 15% more death registrations during January-March 2021 than during the same period in 2019, despite estimated 100% coverage in 2019 and low official COVID-19 deaths in January-March 2021.

    “Karnataka, for example, saw 15% more death registrations during January–March 2021 than during the same period in 2019; this was despite the fact that coverage in the data was estimated to be 100% during 2019, and that during January–March 2021 official COVID-19 deaths in Karnataka were low.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    A 2% decrease in coverage during April 2020-February 2021 leads to a 17% increase in estimated excess deaths, while a 5% increase in coverage during March-May 2021 reduces excess death estimates by around 9%.

    “For example, during April 2020–February 2021 a modest 2% decrease in coverage in the data relative to 2019 levels leads to a 17% increase in estimated excess deaths during this period; in the other direction, a 5% increase in coverage in the data during March–May 2021 relative to 2019 levels, would reduce excess deaths estimates during this period by around 9%.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The correlation between estimated monthly excess deaths and recorded COVID-19 deaths rises to 0.98 during April 2020-May 2021 and drops slightly to 0.96 over April 2020-June 2021.

    “During April 2020–May 2021, this rises to 0.98, and drops slightly to 0.96 over April 2020–June 2021.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    STAR12 accounted for 59% of the estimated 2019 national population and 59% of estimated total deaths occurring during 2019.

    “STAR12 accounted for 59% of the estimated 2019 national population and also 59% of estimated total deaths occurring during 2019 [40].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [40] National Commission on Population, 2019, Population Projections for India and States 2011–2036, Report of the Technical Group on Population Projections Constituted by the National Commission on Population

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductnone given

    The study carefully characterized the reasons why registration is incomplete and estimated the extent of coverage in the data.

    “We carefully characterize the reasons why registration is incomplete and estimate the extent of coverage in the data.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The study explored the sensitivity of the estimates to various assumptions.

    “We explore sensitivity of the estimates to various assumptions.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    The study used Monte-Carlo simulations to generate confidence intervals on mortality estimates for April 2020-June 2021.

    “In the simulation approach, we ran Monte-Carlo simulations allowing baseline coverage in the data to vary by ±15% around the midpoint value estimated from the 2019 CRS report, and pandemic period coverage in the data to change by ± 5% in each state relative to its baseline value.”
    • Also stated in: § 2.3 Methods · plain
      “running 10,000 simulations allowed us to generate confidence intervals on the mortality estimates for the period April 2020–June 2021.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    The estimates of registration completion may be somewhat too high in some states, making the estimates of pandemic excess mortality conservative.

    “We note that the estimates of completion may be somewhat too high in some states. This makes our estimates of pandemic excess mortality conservative.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    According to UN estimates, India's estimated crude death rate was falling prior to 2015 but saw a 1% increase during 2015-2019.

    “According to UN estimates [39], however, India’s estimated CDR was falling prior to 2015 and but saw a 1% increase during 2015–2019.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [39] World Bank, 2021, Death rate, crude (per 1,000 people)—India | Data

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The paper transparently lays out the assumptions needed to estimate excess deaths from the data.

    “We transparently lay out the many assumptions needed to estimate excess deaths on the basis of these data.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    States differ in level of mortality registration before the pandemic, recording systems, and the way mortality data are organized.

    “States differ in terms of level of mortality registration before the pandemic; recording systems; (for instance, for online and offline registration); and the way mortality data are organized (e.g., by date of death or date of registration) [19].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [19] India COVID Mapping. Mapping the impact of the COVID-19 pandemic in India. In: https://www.indiacovidmapping.org/ [Internet]. 2022. Available: https://www.indiacovidmapping.org/.

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    The study considers the association in the timing of recorded COVID-19 deaths and excess deaths, moving beyond existing literature.

    “Moving beyond the existing literature, we consider the association in the timing of recorded COVID-19 deaths and excess deaths.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Estimates of India's national crude death rate in 2019 range from 6.0 to 7.5 per 1,000, corresponding to registration completion from 76% to 96%.

    “A variety of data sources and approaches detailed in Appendix 1, including from the UN population division [37] and the National Family Health Survey-5 [21], give estimates of the national CDR in 2019 ranging from 6.0 to 7.5 per 1K, corresponding to registration completion in 2019 from 76% to 96%.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [37] United Nations, Department of Economic and Social Affairs, Population Division, 2019, World Population Prospects

      Not checked in the free run.

    • cites [21] International Institute for Population Sciences (IIPS), ICF, 2022

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Death registration was often slowly increasing prior to the COVID-19 pandemic but was disrupted during the early phase of the pandemic.

    “Often, registration was slowly increasing prior to the COVID-19 pandemic [6, 23], but was disrupted during the early phase of the pandemic.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [6] A. Gupta, 2020, COVID-19 and the importance of improving civil registration in India, Center for the Advanced Study of India

      Not checked in the free run.

    • cites [23] C Rao, 2020, The civil registration system is a potentially viable data source for reliable subnational mortality measurement in India, BMJ Global Health source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    The central estimates and overall levels of uncertainty are not greatly affected by omitting any individual state from the analysis.

    “The results indicate that the central estimates, and the overall levels of uncertainty in the estimates, are not greatly affected by the omission of any individual state from the analysis.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    All-cause mortality data, where available, has been used to understand the overall mortality impact of the pandemic.

    “All-cause mortality data, where available, has been used to understand the overall mortality impact of the pandemic [11, 12].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [11] JM Aburto, 2022, Quantifying impacts of the COVID-19 pandemic through life-expectancy losses: a population-level study of 29 countries, International journal of epidemiology source

      Not checked in the free run.

    • cites [12] JM Aburto, 2021, Estimating the burden of the COVID-19 pandemic on mortality, life expectancy and lifespan inequality in England and Wales: a population-level analysis, J Epidemiol Community Health

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Numerous media reports have reported large increases in registered deaths in India.

    “In India, numerous media reports have reported large increases in registered deaths [13–15].”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [13] S. Ramani, 2021, On ‘excess deaths’ in Rajasthan, The Hindu

      Not checked in the free run.

    • cites [14] S. Rukmini, 2021, Madhya Pradesh saw nearly three times more deaths than normal after second wave of Covid-19 struck

      Not checked in the free run.

    • cites [15] The Hindu Data Team, 2021, Data | India’s excess deaths could be highest among nations with the most recorded COVID-19 fatalities, The Hindu

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    India's national population has been growing by around 1% per year.

    “population projections suggest that the national population has been growing by around 1% per year [40].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [40] National Commission on Population, 2019, Population Projections for India and States 2011–2036, Report of the Technical Group on Population Projections Constituted by the National Commission on Population

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    The approach applies a careful adjustment process accounting for both registration completion and coverage in online systems.

    “Our approach applies a careful adjustment process, accounting for both registration completion and coverage in online systems.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    SRS estimates imply that up to 2019 year-on-year deaths were steady or falling slightly, while UN estimates suggest they could have been rising by a little over 1% per year.

    “Thus, the SRS estimates imply that up to 2019 year-on-year deaths were steady or falling slightly; while UN estimates suggest they could have been rising by a little over 1% per year.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Available data may miss not only deaths that were not registered, but also sometimes deaths that were registered in an offline system.

    “Available data may miss not only the deaths that were not registered, but also sometimes deaths that were registered in an offline system [13].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [13] S. Ramani, 2021, On ‘excess deaths’ in Rajasthan, The Hindu

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    The full data and code used in the analysis are available on a replication repository.

    “The full data and code used in our analysis are available on our replication repository on GitHub [41].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [41] M. Banaji, 2022, IndiaACMdata/National Estimates, In: GitHub

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    There are reasons to believe that the estimated crude death rate of 6.2 is too low and that registration completion in 2019 was less than 92%.

    “There are, however, several reasons to believe that the estimated CDR of 6.2 is too low, and that completion in 2019 was less than 92% [17].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [17] M Banaji, 2022, India: notes on estimates of crude death rate and registration coverage

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    The study assesses the degree to which its estimates are sensitive to various uncertainties, including fluctuations in registration and differences in mortality impact between STAR12 and the rest of the country.

    “We also assess the degree to which our estimates are sensitive to the various uncertainties, for example due to fluctuations in levels of registration, or possible differences in the mortality impact of the pandemic in the states for which we have data and the rest of the country.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Reported COVID-19 cases generally capture a small fraction of total infections, and deaths in these systems are undercounts.

    “it is widely recognized that reported cases generally capture a small fraction of total infections, and that deaths in these systems are undercounts [8–10].”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [8] RJ Acosta, 2021, All-cause excess mortality in the State of Gujarat, India, during the COVID-19 pandemic (March 2020-April 2021), medRxiv

      Not checked in the free run.

    • cites [9] M. Banaji, 2021, Estimating COVID-19 infection fatality rate in Mumbai during 2020, medRxiv

      Not checked in the free run.

    • cites [10] MV Murhekar, 2021, SARS-CoV-2 seroprevalence among the general population and healthcare workers in India, December 2020–January 2021, International Journal of Infectious Diseases source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The population health impacts of the COVID-19 pandemic are less well understood in low- and middle-income countries, where routine mortality surveillance before the pandemic was patchy.

    “These impacts are less well understood in low- and middle-income countries [1–4], where routine mortality surveillance before the pandemic was patchy [5–7].”
    Quote in the paperSubstantial claim0 of 7 sources checked
    • cites [1] S Helleringer, 2021, Measuring excess mortality due to the COVID-19 pandemic: progress and persistent challenges, International journal of epidemiology

      Not checked in the free run.

    • cites [2] V Knutson, 2022, Estimating Global and Country-Specific Excess Mortality During the COVID-19 Pandemic, arXiv preprint arXiv

      Not checked in the free run.

    • cites [3] AT Levin, 2022, Assessing the burden of COVID-19 in developing countries: systematic review, meta-analysis and public policy implications, BMJ Global Health source

      Not checked in the free run.

    • cites [4] JA Lewnard, 2022, All-cause mortality during the COVID-19 pandemic in Chennai, India: an observational study, The Lancet Infectious Diseases source

      Not checked in the free run.

    • cites [5] World Health Organization, 2014

      Not checked in the free run.

    • cites [6] A. Gupta, 2020, COVID-19 and the importance of improving civil registration in India, Center for the Advanced Study of India

      Not checked in the free run.

    • cites [7] A. Karlinsky, 2021, International completeness of death registration 2015–2019., medRxiv

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationnone given

    The approach highlights the utility of all-cause mortality data as well as the significant challenges in interpreting it.

    “Our approach highlights the utility of all-cause mortality data, as well as the significant challenges in interpreting it.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductown data

    The data for June 2021 is from only three states, and estimates using this data carry greater uncertainty.

    “The data for June 2021 is from only three states. Estimates using this data carry greater uncertainty.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    Excess mortality in India has been high, and surveillance of COVID-19 deaths has been very weak.

    “excess mortality has been high, and surveillance of COVID-19 deaths has been very weak.”
    • Also stated in: § 5. Conclusion · plain
      “We find that the surveillance of pandemic mortality in India has been extremely poor”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Between 750 and 1000 million SARS-CoV-2 infections had occurred in India by June 2021.
    • The authors state this as an assumption: The infection fatality rate predictions assume even spread of disease across different age groups.

  • May combine two claims.

    Premisecitation

    The third national serosurvey, carried out during December 2020-January 2021, estimated national seroprevalence of 24.1%, corresponding to approximately 325 million infections nationwide.

    “The third national serosurvey carried out during December 2020–January 2021 estimated seroprevalence of 24.1% nationally, corresponding to approximately 325M infections nationwide [10].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [10] MV Murhekar, 2021, SARS-CoV-2 seroprevalence among the general population and healthcare workers in India, December 2020–January 2021, International Journal of Infectious Diseases source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Death registration completion prior to the pandemic may have been overestimated; national-level completion is likely overestimated in the 2019 CRS report, and sub-national estimates probably overestimate completion.

    “Death registration completion prior to the pandemic may have been overestimated. We discussed earlier how death registration completion at the national level is likely overestimated in the 2019 CRS report, and even the sub-national estimates probably overestimate completion [22].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [22] M Banaji, 2022, Mortality and Death Registration in India, The India Forum

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationown data

    The increase in the ratio of excess deaths to recorded COVID-19 deaths could reflect an increase in under-ascertainment of COVID-19 deaths during the 2021 surge or an increase in non-COVID-19 excess deaths.

    “This could reflect an increase in under-ascertainment of COVID-19 deaths during the huge 2021 surge or, possibly, an increase in non-COVID-19 excess deaths.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationnone given

    Optimistic or pessimistic assumptions could shift the excess death estimates by 20%-30% in either direction, while more up-to-date data are likely to push them up.

    “Optimistic or pessimistic assumptions could shift these estimates by 20%–30% in either direction, while more up-to-date data are likely to push them up.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    India is among the countries most severely impacted by the pandemic, and the pandemic is the gravest mortality crisis India has faced since its independence.

    “India is among the countries most severely impacted by the pandemic, and the pandemic is the gravest mortality crisis India has faced since its independence.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationown data

    The higher than expected registrations in January-March 2021 could reflect improved coverage in the data by early 2021.

    “This could reflect improved coverage in the data by early 2021.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    COVID-19 may have hit hardest in marginalised communities where death registration is weaker, lowering overall registration levels.

    “Moreover, COVID-19 may have hit hardest in marginalised communities where death registration is weaker [50], lowering overall registration levels.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [50] A Bamezai, 2021, Survey evidence of excess mortality in Bihar in the second COVID-19 surge, SocArxiv

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    If most excess deaths were from COVID-19, under-ascertainment of COVID-19 deaths has been high, with around 8-10 excess deaths for every recorded COVID-19 death.

    “with around 8–10 excess deaths for every recorded COVID-19 death.”
    • Also stated in: § 5. Conclusion · plain
      “with around 8–10 times as many excess deaths as officially recorded COVID-19 deaths.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Between 750 and 1000 million SARS-CoV-2 infections had occurred in India by June 2021.
    • The authors state this as an assumption: The infection fatality rate predictions assume even spread of disease across different age groups.

  • May combine two claims.

    Premisecitation

    Preliminary results from the fourth national serosurvey found antibodies to SARS-CoV-2 in 62.3% of unvaccinated individuals sampled, corresponding to an estimated 839 million COVID-19 infections by June 2021.

    “Preliminary results from the fourth national serosurvey [45] found antibodies to SARS-CoV-2 in 62.3% of unvaccinated individuals sampled, corresponding to an estimated 839M COVID-19 infections by June 2021.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [45] PIB India, 2021, Implications of 4th Round of National Sero-Survey show that there is a ray of hope but there is no room for complacency, Non-essential travel must be discouraged and travel only if fully vaccinated—DG @ICMRDELHI #IndiaFightsCorona https://t.co/IwOs72LBAR. In: @PIB_India

      Not checked in the free run.

46 of 46

  • The authors state this as an assumption.

    Per capita pandemic excess deaths nationwide were equal to those in STAR12.

    “We assume that per capita pandemic excess deaths nationwide were equal to in STAR12.”

  • The authors state this as an assumption.

    The P-score nationwide was similar to that in STAR12.

    “we assume that the P-score nationwide was similar to that in STAR12.”

  • The authors state this as an assumption.

    There were no variations in expected mortality, or in coverage in the data between 2019 and the pandemic period.

    “These estimates assume there were no variations in expected mortality, or in coverage in the data between 2019 and the pandemic period.”

  • The authors state this as an assumption.

    There was no change in national or sub-national values of the crude death rate between 2018 and 2019.

    “All of these estimates assume no change in national or sub-national values of the CDR between 2018 and 2019.”

  • The authors state this as an assumption.

    The optimistic and pessimistic scenarios are based on uniform changes, most importantly uniform decreases or increases in registration coverage, across the states of STAR12.

    “The optimistic and pessimistic scenarios presented above are based on the assumption of uniform changes–most importantly, uniform decreases or increases in registration coverage, across the states of STAR12.”

  • The authors state this as an assumption.

    The analysis in section 3.6 assumes no changes in expected deaths or registration completion.

    “In this section we assume no changes in expected deaths or registration completion, and use mortality-based extrapolations to get national estimates.”

  • The authors state this as an assumption.

    Between 750 and 1000 million SARS-CoV-2 infections had occurred in India by June 2021.

    “let us suppose that 750–1000M infections (equivalent to infection rates of 56% to 74%) had occurred by this point.”

  • The authors state this as an assumption.

    The infection fatality rate predictions assume even spread of disease across different age groups.

    “Moreover, the predictions assume even spread of disease across different age groups.”
8 of 8

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Pessimistic estimates rise by more than the stated 20%-30%

    Minor

    The pessimistic June estimate of 5.2M is about 37% above the central estimate of 3.8M, not 20%-30%. For May, the pessimistic mortality-based estimate of 4.0M is about 43% above 2.8M.

1 of 1

Cites

Flagged first, then by citations

Research works printed but not linked: 19

  1. World Health Organization, 2014, Global Civil Registration and Vital Statistics: A Scaling Up Investment Plan 2015–2024
  2. A. Gupta, 2020, COVID-19 and the importance of improving civil registration in India, Center for the Advanced Study of India
  3. S. Ramani, 2021, On ‘excess deaths’ in Rajasthan, The Hindu
  4. The Hindu Data Team, 2021, Data | India’s excess deaths could be highest among nations with the most recorded COVID-19 fatalities, The Hindu
  5. Government of India, 2021, Vital Statistics of India Based on the Civil Registration System 2019
  6. International Institute for Population Sciences (IIPS), ICF, 2022, National Family Health Survey (NFHS-5), 2019–21: India
  7. M Banaji, 2022, Mortality and Death Registration in India, The India Forum
  8. The Economist, 2021, The pandemic’s true death toll, The Economist
  9. C Giattino, 2020, Excess mortality during the Coronavirus pandemic (COVID-19), Our World in Data
  10. ORGI, 2020, Office of the Registrar General and Census Commissioner of India
  11. ORGI, 2022, Sample registration system statistical report 2019. Office of the Registrar General and Census Commissioner of India
  12. United Nations, Department of Economic and Social Affairs, Population Division, 2019, World Population Prospects
  13. ORGI, 2020, Office of the Registrar General and Census Commissioner of India
  14. World Bank, 2021, Death rate, crude (per 1,000 people)—India | Data
  15. M. Banaji, 2022, IndiaACMdata/National Estimates, In: GitHub
  16. Government of India, 2022, Vital Statistics of India Based on the Civil Registration System 2020
  17. PIB India, 2021, Centre advises States to conduct State-specific Sero Surveys in consultation with ICMR to generate district-level data on sero-prevalence
  18. PIB India, 2021, Implications of 4th Round of National Sero-Survey show that there is a ray of hope but there is no room for complacency, Non-essential travel must be discouraged and travel only if fully vaccinated—DG @ICMRDELHI #IndiaFightsCorona https://t.co/IwOs72LBAR. In: @PIB_India
  19. S. Das, 2021, Death Count In 24 UP Districts 43 Times More Than Official Covid-19 Toll—Article 14, Article 14

Cited by

Sorted by citations

Related

Authors
Banaji, M., & Gupta, A.
Affiliations
  • Middlesex University London (GB)
  • Harvard University (US)
Corresponding author
  • Banaji, Murad
  • Gupta, Aashish
ORCID
Venue
PLOS Global Public Health
Publisher
Public Library of Science (PLoS)
Published
2022-12-09
Volume
2
Issue
12
Pages
e0000803
ISSN
2767-3375 (ISSN-L)
DOI
10.1371/journal.pgph.0000803
PMC
PMC10021303
OpenAlex
W4312030267
Semantic Scholar
d1028b8d221b24ad09632f4b6e7195d6e4046322
Cited by
45
References
35
Citations by year
2026: 6 · 2025: 4 · 2024: 12 · 2023: 11 · 2022: 9 · 2021: 2 · 2020: 1
FWCI
5.33
Type
journal article
Language
en
Open access
Licence
Locations
  • PLOS Global Public Health (publishedVersion, cc-by)
  • medRxiv (acceptedVersion, cc-by-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Middlesex University Research Repository (Middlesex University Of London) (acceptedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Lancaster EPrints (Lancaster University) (submittedVersion)
Topics
  • Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
  • Social Sciences › Demography › Insurance, Mortality, Demography, Risk Management
  • Health Sciences › Medicine › Oncology › COVID-19 and healthcare impacts
Keywords
COVID-19 pandemic, excess mortality, all-cause mortality, mortality surveillance, underreporting of deaths, India, low- and middle-income countries, sensitivity analysis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-06.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-06.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review