Evidence map›Paper›PMID 34610940›Full record

ArticleBMJ open2021

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

Rebecca Cai, Paul Novosad, Vaidehi Tandel, Sam Asher, Anup Malani

Abstract read
In one paragraph

Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Observational
  3. Article
  4. A Statistical Model of COVID-19 Infection Incidence in the Southern Indian State of Tamil Nadu.International journal of environmental research and public health · 2022
    Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Rebecca CaiDevelopment Data Lab, Washington, District of Columbia, USA.
Paul NovosadEconomics, Dartmouth College, Hanover, New Hampshire, USA.ORCID 0000-0001-8331-6630
Vaidehi TandelReal Estate and Planning Henley Business School, University of Reading, Reading, UK.
Sam AsherEconomics, Johns Hopkins University School of Advanced International Studies, Washington, District of Columbia, USA.
Anup MalaniUniversity of Chicago Law School, Chicago, Illinois, USA amalani@uchicago.edu.ORCID 0000-0002-2594-5778

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo 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.

designCross-sectional study.

settingIndia, multiple regions representing combined population >150 million.

participantsAggregate 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.

resultsAmong 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.

conclusionsEstimated 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.

Indexed as

COVID-19AgedCross-Sectional StudiesHumansIndiaMiddle AgedSARS-CoV-2Seroepidemiologic StudiesCOVID-19epidemiologypublic health

Identifiers

PMID34610940
PMCPMC8493602

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.