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

  • Cai, Rebecca1
  • Novosad, Paul2
  • Tandel, Vaidehi3
  • Asher, Sam4
  • Malani, Anup5
  • 1 Development Data Lab, Washington, District of Columbia, USA , Washington
  • 2 Dartmouth College, Hanover, New Hampshire, USA , Hanover
  • 3 University of Reading, Reading, UK , Reading
  • 4 Johns Hopkins University School of Advanced International Studies, Washington, District of Columbia, USA , Washington
  • 5 University of Chicago Law School, Chicago, Illinois, USA , Chicago
Published Article
BMJ Open
Publication Date
Oct 05, 2021
DOI: 10.1136/bmjopen-2021-050920
PMID: 34610940
PMCID: PMC8493602
PubMed Central
  • 1506
  • 2474
  • 1692


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.

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