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      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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          The online version of this article (10.1007/s10654-020-00698-1) contains supplementary material, which is available to authorized users.

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          Estimates of the severity of coronavirus disease 2019: a model-based analysis

          Robert Verity, Lucy C Okell, Ilaria Dorigatti … (2020)
          Summary Background In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases. Methods We collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation. Findings Using data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9–19·2) and to hospital discharge to be 24·7 days (22·9–28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70 117), we estimated a crude case fatality ratio (adjusted for censoring) of 3·67% (95% CrI 3·56–3·80). However, after further adjusting for demography and under-ascertainment, we obtained a best estimate of the case fatality ratio in China of 1·38% (1·23–1·53), with substantially higher ratios in older age groups (0·32% [0·27–0·38] in those aged <60 years vs 6·4% [5·7–7·2] in those aged ≥60 years), up to 13·4% (11·2–15·9) in those aged 80 years or older. Estimates of case fatality ratio from international cases stratified by age were consistent with those from China (parametric estimate 1·4% [0·4–3·5] in those aged <60 years [n=360] and 4·5% [1·8–11·1] in those aged ≥60 years [n=151]). Our estimated overall infection fatality ratio for China was 0·66% (0·39–1·33), with an increasing profile with age. Similarly, estimates of the proportion of infected individuals likely to be hospitalised increased with age up to a maximum of 18·4% (11·0–7·6) in those aged 80 years or older. Interpretation These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death. Funding UK Medical Research Council.
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            Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2)

            Ruiyun Li, Sen Pei, Bin Chen … (2020)
            Estimation of the prevalence and contagiousness of undocumented novel coronavirus (SARS-CoV2) infections is critical for understanding the overall prevalence and pandemic potential of this disease. Here we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV2, including the fraction of undocumented infections and their contagiousness. We estimate 86% of all infections were undocumented (95% CI: [82%–90%]) prior to 23 January 2020 travel restrictions. Per person, the transmission rate of undocumented infections was 55% of documented infections ([46%–62%]), yet, due to their greater numbers, undocumented infections were the infection source for 79% of documented cases. These findings explain the rapid geographic spread of SARS-CoV2 and indicate containment of this virus will be particularly challenging.
            Article 0 comments Cited 1623 times – based on 0 reviews     Review now
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              Antibody responses to SARS-CoV-2 in patients with COVID-19

              Quan-Xin Long, Bai-Zhong Liu, Hai-Jun Deng … (2020)
              We report acute antibody responses to SARS-CoV-2 in 285 patients with COVID-19. Within 19 days after symptom onset, 100% of patients tested positive for antiviral immunoglobulin-G (IgG). Seroconversion for IgG and IgM occurred simultaneously or sequentially. Both IgG and IgM titers plateaued within 6 days after seroconversion. Serological testing may be helpful for the diagnosis of suspected patients with negative RT-PCR results and for the identification of asymptomatic infections.
              Article 0 comments Cited 1479 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Gideon Meyerowitz-Katz:
                ORCID: http://orcid.org/0000-0002-1156-9345
                gideon.meyerowitzkatz@health.nsw.gov.au
                Journal
                Journal ID (nlm-ta): Eur J Epidemiol
                Journal ID (iso-abbrev): Eur J Epidemiol
                Title: European Journal of Epidemiology
                Publisher: Springer Netherlands (Dordrecht )
                ISSN (Print): 0393-2990
                ISSN (Electronic): 1573-7284
                Publication date (Electronic): 8 December 2020
                Publication date PMC-release: 8 December 2020
                Pages: 1-16
                Affiliations
                [1 ]GRID grid.254880.3, ISNI 0000 0001 2179 2404, Dartmouth College, ; Hanover, USA
                [2 ]GRID grid.250279.b, ISNI 0000 0001 0940 3170, National Bureau for Economic Research, ; Cambridge, USA
                [3 ]GRID grid.410315.2, ISNI 0000 0001 1954 7426, Centre for Economic Policy Research, ; London, United Kingdom
                [4 ]GRID grid.38142.3c, ISNI 000000041936754X, Harvard T.H. Chan School of Public Health, ; Boston, USA
                [5 ]GRID grid.1007.6, ISNI 0000 0004 0486 528X, University of Wollongong, ; Wollongong, Australia
                [6 ]Western Sydney Local Health District, PO Box 792, Seven Hills, NSW 2147 Australia
                [7 ]GRID grid.67105.35, ISNI 0000 0001 2164 3847, Case Western Reserve University School of Medicine, ; Cleveland, USA
                Author information
                http://orcid.org/0000-0002-1156-9345
                Article
                Publisher ID: 698
                DOI: 10.1007/s10654-020-00698-1
                PMC ID: 7721859
                PubMed ID: 33289900
                SO-VID: 09942452-b6bf-4034-a6ea-acd40169a1ff
                Copyright © © The Author(s) 2020
                License:

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 5 October 2020
                Date accepted : 10 November 2020
                Categories
                Subject: Meta-Analysis

                ScienceOpen disciplines: Public health
                Keywords: covid-19,sars-cov-2,infection-fatality ratio,infection-fatality rate,systematic review,meta-regression
                Data availability:
                ScienceOpen disciplines: Public health
                Keywords: covid-19, sars-cov-2, infection-fatality ratio, infection-fatality rate, systematic review, meta-regression

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