ArticleLancet (London, England)2022
Variation in the COVID-19 infection-fatality ratio by age, time, and geography during the pre-vaccine era: a systematic analysis.
Article in Lancet (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 161 papers, 9 of them syntheses that pooled 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.
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.
Who cites it
161 citing papers in PubMed, 9 syntheses or guidelines pooled it.
- The Asymptomatic Proportion of SARS-CoV-2 Omicron Variant Infections in Households: A Systematic Review.Influenza and other respiratory viruses · 2024Pooled it
- COVID-19 and the Use of Angiotensin II Receptor Blockers in Older Chronic Hypertensive Patients: Systematic Review and Meta-Analysis.Medicina (Kaunas, Lithuania) · 2023Pooled it
- Past SARS-CoV-2 infection protection against re-infection: a systematic review and meta-analysis.Lancet (London, England) · 2023Pooled it
- Hyperimmune immunoglobulin for people with COVID-19.The Cochrane database of systematic reviews · 2023Pooled it
- Age-stratified infection fatality rate of COVID-19 in the non-elderly population.Environmental research · 2023Pooled it
- Global variations in the burden of SARS-CoV-2 infection and its outcomes in pregnant women by geographical region and country's income status: a meta-analysis.BMJ global health · 2022Pooled it
- Inferring the true number of SARS-CoV-2 infections in Japan.Journal of infection and chemotherapy : official journal of the Japan Society of Chemotherapy · 2022Pooled it
- Pooled it
- Assessing the burden of COVID-19 in developing countries: systematic review, meta-analysis and public policy implications.BMJ global health · 2022Pooled it
- Safety and immunogenicity of SCB-2019, an adjuvanted, recombinant SARS-CoV-2 trimeric S-protein subunit COVID-19 vaccine in healthy 12-17 year-old adolescents.Human vaccines & immunotherapeutics · 2023Trial
- Agreement between seroprevalence- and model-based estimates of COVID-19 burden.Global health action · 2026Article
- How Many Veteran COVID-19 Cases Were There during the Pandemic?Journal of medical virology · 2026Article
- Using forensic autopsy data to estimate the age-specific infection fatality risk of COVID-19.Epidemiology and infection · 2026Article
- Multi-event dynamic capture-recapture model for big data: Estimating undetected COVID-19 cases in British Columbia, Canada.Infectious Disease Modelling · 2026Article
- Machine Learning Identification of Cell-Type-Specific Molecular Signatures Distinguishing COVID-19 from Other Lower Respiratory Tract Diseases.Life (Basel, Switzerland) · 2026Article
- Post-Pandemic Resurgence and Seasonal Patterns of Influenza Viruses and Respiratory Syncytial Virus in Arequipa, Peru (2021-2023).Epidemiologia (Basel, Switzerland) · 2026Article
- Estimating the incidence of SARS-CoV-2 infections by jointly modelling seroprevalence, hospitalization and mortality data, February 2020 - January 2021, Belgium.Archives of public health = Archives belges de sante publique · 2026Article
- COVID-19, infection fatality rate (IFR) implied by the serology, antibody, testing in New York City.Infectious Disease Modelling · 2026Article
- Development and validation of age-specific predictive models on the risk of post-acute mortality within 1 year of COVID-19 infection.QJM : monthly journal of the Association of Physicians · 2026Article
- 'When I had concerns about my own patients…I was told to keep quiet': experiences of moral injury among healthcare workers under Covid-19 vaccine mandates in Alberta, Canada.Open medicine (Warsaw, Poland) · 2026Article
101 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
- Erratum issued
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe infection-fatality ratio (IFR) is a metric that quantifies the likelihood of an individual dying once infected with a pathogen. Understanding the determinants of IFR variation for COVID-19, the disease caused by the SARS-CoV-2 virus, has direct implications for mitigation efforts with respect to clinical practice, non-pharmaceutical interventions, and the prioritisation of risk groups for targeted vaccine delivery. The IFR is also a crucial parameter in COVID-19 dynamic transmission models, providing a way to convert a population's mortality rate into an estimate of infections.
methodsWe estimated age-specific and all-age IFR by matching seroprevalence surveys to total COVID-19 mortality rates in a population. The term total COVID-19 mortality refers to an estimate of the total number of deaths directly attributable to COVID-19. After applying exclusion criteria to 5131 seroprevalence surveys, the IFR analyses were informed by 2073 all-age surveys and 718 age-specific surveys (3012 age-specific observations). When seroprevalence was reported by age group, we split total COVID-19 mortality into corresponding age groups using a Bayesian hierarchical model to characterise the non-linear age pattern of reported deaths for a given location. To remove the impact of vaccines on the estimated IFR age pattern, we excluded age-specific observations of seroprevalence and deaths that occurred after vaccines were introduced in a location. We estimated age-specific IFR with a non-linear meta-regression and used the resulting age pattern to standardise all-age IFR observations to the global age distribution. All IFR observations were adjusted for baseline and waning antibody-test sensitivity. We then modelled age-standardised IFR as a function of time, geography, and an ensemble of 100 of the top-performing covariate sets. The covariates included seven clinical predictors (eg, age-standardised obesity prevalence) and two measures of health system performance. Final estimates for 190 countries and territories, as well as subnational locations in 11 countries and territories, were obtained by predicting age-standardised IFR conditional on covariates and reversing the age standardisation.
findingsWe report IFR estimates for April 15, 2020, to January 1, 2021, the period before the introduction of vaccines and widespread evolution of variants. We found substantial heterogeneity in the IFR by age, location, and time. Age-specific IFR estimates form a J shape, with the lowest IFR occurring at age 7 years (0·0023%, 95% uncertainty interval [UI] 0·0015-0·0039) and increasing exponentially through ages 30 years (0·0573%, 0·0418-0·0870), 60 years (1·0035%, 0·7002-1·5727), and 90 years (20·3292%, 14·6888-28·9754). The countries with the highest IFR on July 15, 2020, were Portugal (2·085%, 0·946-4·395), Monaco (1·778%, 1·265-2·915), Japan (1·750%, 1·302-2·690), Spain (1·710%, 0·991-2·718), and Greece (1·637%, 1·155-2·678). All-age IFR varied by a factor of more than 30 among 190 countries and territories. After age standardisation, the countries with the highest IFR on July 15, 2020, were Peru (0·911%, 0·636-1·538), Portugal (0·850%, 0·386-1·793), Oman (0·762%, 0·381-1·399), Spain (0·751%, 0·435-1·193), and Mexico (0·717%, 0·426-1·404). Subnational locations with high IFRs also included hotspots in the UK and southern and eastern states of the USA. Sub-Saharan African countries and Asian countries generally had the lowest all-age and age-standardised IFRs. Population age structure accounted for 74% of logit-scale variation in IFRs estimated for 39 in-sample countries on July 15, 2020. A post-hoc analysis showed that high rates of transmission in the care home population might account for higher IFRs in some locations. Among all countries and territories, we found that the median IFR decreased from 0·466% (interquartile range 0·223-0·840) to 0·314% (0·143-0·551) between April 15, 2020, and Jan 1, 2021.
interpretationEstimating the IFR for global populations helps to identify relative vulnerabilities to COVID-19. Information about how IFR varies by age, time, and location informs clinical practice and non-pharmaceutical interventions like physical distancing measures, and underpins vaccine risk stratification. IFR and mortality risk form a J shape with respect to age, which previous research, such as that by Glynn and Moss in 2020, has identified to be a common pattern among infectious diseases. Understanding the experience of a population with COVID-19 mortality requires consideration for local factors; IFRs varied by a factor of more than 30 among 190 countries and territories in this analysis. In particular, the presence of elevated age-standardised IFRs in countries with well resourced health-care systems indicates that factors beyond health-care capacity are important. Potential extenuating circumstances include outbreaks among care home residents, variable burdens of severe cases, and the population prevalence of comorbid conditions that increase the severity of COVID-19 disease. During the pre-vaccine period, the estimated 33% decrease in median IFR over 8 months suggests that treatment for COVID-19 has improved over time. Estimating IFR for the pre-vaccine era provides an important baseline for describing the progression of COVID-19 mortality patterns.
fundingBill & Melinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.