Assessing the age specificity of infection fatality rates for COVID-19: systematic review, meta-analysis, and public policy implications
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Abstract
Determine age-specific infection fatality rates for COVID-19 to inform public health policies and communications that help protect vulnerable age groups. Studies of COVID-19 prevalence were collected by conducting an online search of published articles, preprints, and government reports that were publicly disseminated prior to 18 September 2020. The systematic review encompassed 113 studies, of which 27 studies (covering 34 geographical locations) satisfied the inclusion criteria and were included in the meta-analysis. Age-specific IFRs were computed using the prevalence data in conjunction with reported fatalities 4 weeks after the midpoint date of the study, reflecting typical lags in fatalities and reporting. Meta-regression procedures in Stata were used to analyze the infection fatality rate (IFR) by age. Our analysis finds a exponential relationship between age and IFR for COVID-19. The estimated age-specific IFR is very low for children and younger adults (e.g., 0.002% at age 10 and 0.01% at age 25) but increases progressively to 0.4% at age 55, 1.4% at age 65, 4.6% at age 75, and 15% at age 85. Moreover, our results indicate that about 90% of the variation in population IFR across geographical locations reflects differences in the age composition of the population and the extent to which relatively vulnerable age groups were exposed to the virus. These results indicate that COVID-19 is hazardous not only for the elderly but also for middle-aged adults, for whom the infection fatality rate is two orders of magnitude greater than the annualized risk of a fatal automobile accident and far more dangerous than seasonal influenza. Moreover, the overall IFR for COVID-19 should not be viewed as a fixed parameter but as intrinsically linked to the age-specific pattern of infections. Consequently, public health measures to mitigate infections in older adults could substantially decrease total deaths.
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SciScore for 10.1101/2020.07.23.20160895: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources As described in Supplementary Appendix B, we systematically performed online searches in MedRxiv, Medline, PubMed, Google Scholar, and EMBASE, and we identified other studies listed in reports by government institutions such as the U.K. Parliament Office.[16] Data was extracted from studies by three authors and verified prior to inclusion. Medlinesuggested: (MEDLINE, RRID:SCR_002185)PubMedsuggested: (PubMed, RRID:SCR_004846)Google Scholarsuggested: (Google Scholar, RRID:SCR_0088…SciScore for 10.1101/2020.07.23.20160895: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources As described in Supplementary Appendix B, we systematically performed online searches in MedRxiv, Medline, PubMed, Google Scholar, and EMBASE, and we identified other studies listed in reports by government institutions such as the U.K. Parliament Office.[16] Data was extracted from studies by three authors and verified prior to inclusion. Medlinesuggested: (MEDLINE, RRID:SCR_002185)PubMedsuggested: (PubMed, RRID:SCR_004846)Google Scholarsuggested: (Google Scholar, RRID:SCR_008878)EMBASEsuggested: (EMBASE, RRID:SCR_001650)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:A substantial limitation of our work is that we have not considered factors apart from age that affect the IFR of COVID-19. For example, we have not considered the extent to which IFRs may vary with demographic factors such as race and ethnicity or potential causal interactions between these factors.[41, 70] Likewise, our metaregression does not include measures of comorbidities such as diabetes or obesity.[156] However, a recent study of data from a large representative and longitudinal sample collected by U.K. Biobank found that measures of frailty and comorbidity had only moderate effects in predicting COVID-19 mortality risk (i.e., increased odds of about 10%); moreover, that link was negligible among positive COVID-19 cases after accounting for age and sex.[157] See Supplementary Appendix S for additional evidence. Further research on these issues is clearly warranted. Another limitation of our meta-analysis is that we have focused exclusively on assessing IFRs in advanced economies to facilitate comparability regarding health care provision and reporting of fatalities. Nonetheless, it is absolutely clear that the COVID-19 pandemic has had devastating consequences for lower-income and developing countries. For example, as of late October 2020, the reported COVID-19 death counts were nearly 160 thousand in Brazil, 120 thousand in India, and 90 thousand in Mexico. And in many countries, measures of excess mortality are much higher than official tabulations of COVID-19 fata...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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SciScore for 10.1101/2020.07.23.20160895: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Antibodies Sentences Resources Most studies of COVID-19 prevalence have proceeded using serological analysis to determine what fraction of the population has developed either IgG or IgM antibodies to the virus. IgMsuggested: NoneIgM antibodies develop earlier, but decrease over time, while IgG antibodies develop later and remain in high concentrations for several months. while IgGsuggested: NoneKobe, Japan87 This study tested for IgG antibodies in 1,000 specimens from an outpatient clinic and found 33 … SciScore for 10.1101/2020.07.23.20160895: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Antibodies Sentences Resources Most studies of COVID-19 prevalence have proceeded using serological analysis to determine what fraction of the population has developed either IgG or IgM antibodies to the virus. IgMsuggested: NoneIgM antibodies develop earlier, but decrease over time, while IgG antibodies develop later and remain in high concentrations for several months. while IgGsuggested: NoneKobe, Japan87 This study tested for IgG antibodies in 1,000 specimens from an outpatient clinic and found 33 positive cases. IgGsuggested: NoneSoftware and Algorithms Sentences Resources Four studies were identified as benchmarks and used in meta-regression of the infection fatality rate (IFR) as a function of age, using the STATA metareg procedure. STATAsuggested: (Stata, SCR_012763)To identify these studies, we performed online searches in MedRxiv and Medline using the criterion ((“infection fatality rate” or “IFR” or “seroprevalence”) and (“COVID-19” or “SARS-Cov-2”)).4 We identified other studies listed in reports by government agencies such as the U.S. Center for Disease Control & Prevention and the U.K. Parliament Office.5 Finally, we confirmed the comprehensiveness of our literature search by referring to two recent meta-analysis studies that have assessed overall IFR for COVID-19 and a recent meta-analysis study comparing seroprevalence with reported cases. Medlinesuggested: (MEDLINE, SCR_002185)Data from additional tools added to each annotation on a weekly basis.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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