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Age-stratified infection fatality rate of COVID-19 in the non-elderly informed from pre-vaccination national seroprevalence studies

October 2022

DOI:10.1101/2022.10.11.22280963

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Authors:

Cathrine Axfors

Universitätsspital Basel

Alexandre Apostolatos

Université de Montréal

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The infection fatality rate (IFR) of COVID-19 among non-elderly people in the absence of vaccination or prior infection is important to estimate accurately, since 94% of the global population is younger than 70 years and 86% is younger than 60 years. In systematic searches in SeroTracker and PubMed (protocol: https://osf.io/xvupr ), we identified 40 eligible national seroprevalence studies covering 38 countries with pre-vaccination seroprevalence data. For 29 countries (24 high-income, 5 others), publicly available age-stratified COVID-19 death data and age-stratified seroprevalence information were available and were included in the primary analysis. The IFRs had a median of 0.035% (interquartile range (IQR) 0.013 - 0.056%) for the 0-59 years old population, and 0.095% (IQR 0.036 - 0.125%,) for the 0-69 years old. The median IFR was 0.0003% at 0-19 years, 0.003% at 20-29 years, 0.011% at 30-39 years, 0.035% at 40-49 years, 0.129% at 50-59 years, and 0.501% at 60-69 years. Including data from another 9 countries with imputed age distribution of COVID-19 deaths yielded median IFR of 0.025-0.032% for 0-59 years and 0.063-0.082% for 0-69 years. Meta-regression analyses also suggested global IFR of 0.03% and 0.07%, respectively in these age groups. The current analysis suggests a much lower pre-vaccination IFR in non-elderly populations than previously suggested. Large differences did exist between countries and may reflect differences in comorbidities and other factors. These estimates provide a baseline from which to fathom further IFR declines with the widespread use of vaccination, prior infections, and evolution of new variants. Highlights *Across 31 systematically identified national seroprevalence studies in the pre-vaccination era, the median infection fatality rate of COVID-19 was estimated to be 0.035% for people aged 0-59 years people and 0.095% for those aged 0-69 years. *The median IFR was 0.0003% at 0-19 years, 0.003% at 20-29 years, 0.011% at 30-39 years, 0.035% at 40-49 years, 0.129% at 50-59 years, and 0.501% at 60-69 years. *At a global level, pre-vaccination IFR may have been as low as 0.03% and 0.07% for 0-59 and 0-69 year old people, respectively. *These IFR estimates in non-elderly populations are lower than previous calculations had suggested.

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Age-stratified infection fatality rate of COVID-19 in the non-elderly informed

from pre-vaccination national seroprevalence studies

Angelo Maria Pezzulloa,b*, Cathrine Axforsa*, Despina G. Contopoulos-Ioannidis,a,c Alexandre

Apostolatos,a,d John P.A. Ioannidisa,e

*equal first authors

aMeta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford,

California, USA

bSezione di Igiene, Dipartimento di Scienze della Vita e Sanità Pubblica, Università Cattolica del

Sacro Cuore, Rome, Italy

cDivision of Infectious Diseases, Department of Pediatrics, Stanford University School of

Medicine, Stanford, California, USA

dFaculty of Medicine, Université de Montréal, Montreal, Canada

eDepartments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science,

and of Statistics, Stanford University, Stanford, California, USA

Keywords: COVID-19; Infection fatality rate; Seroprevalence; Bias; Epidemics

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is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22280963doi: medRxiv preprint

NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

ABSTRACT

The infection fatality rate (IFR) of COVID-19 among non-elderly people in the absence

of vaccination or prior infection is important to estimate accurately, since 94% of the global

population is younger than 70 years and 86% is younger than 60 years. In systematic searches in

SeroTracker and PubMed (protocol: https://osf.io/xvupr), we identified 40 eligible national

seroprevalence studies covering 38 countries with pre-vaccination seroprevalence data. For 29

countries (24 high-income, 5 others), publicly available age-stratified COVID-19 death data and

age-stratified seroprevalence information were available and were included in the primary

analysis. The IFRs had a median of 0.035% (interquartile range (IQR) 0.013 - 0.056%) for the 0-

59 years old population, and 0.095% (IQR 0.036 - 0.125%,) for the 0-69 years old. The median

IFR was 0.0003% at 0-19 years, 0.003% at 20-29 years, 0.011% at 30-39 years, 0.035% at 40-49

years, 0.129% at 50-59 years, and 0.501% at 60-69 years. Including data from another 9

countries with imputed age distribution of COVID-19 deaths yielded median IFR of 0.025-

0.032% for 0-59 years and 0.063-0.082% for 0-69 years. Meta-regression analyses also

suggested global IFR of 0.03% and 0.07%, respectively in these age groups. The current analysis

suggests a much lower pre-vaccination IFR in non-elderly populations than previously

suggested. Large differences did exist between countries and may reflect differences in

comorbidities and other factors. These estimates provide a baseline from which to fathom further

IFR declines with the widespread use of vaccination, prior infections, and evolution of new

variants.

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Highlights

*Across 31 systematically identified national seroprevalence studies in the pre-vaccination era,

the median infection fatality rate of COVID-19 was estimated to be 0.035% for people aged 0-59

years people and 0.095% for those aged 0-69 years.

*The median IFR was 0.0003% at 0-19 years, 0.003% at 20-29 years, 0.011% at 30-39 years,

0.035% at 40-49 years, 0.129% at 50-59 years, and 0.501% at 60-69 years.

*At a global level, pre-vaccination IFR may have been as low as 0.03% and 0.07% for 0-59 and

0-69 year old people, respectively.

*These IFR estimates in non-elderly populations are lower than previous calculations had

suggested.

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1. INTRODUCTION

The Coronavirus Disease 2019 (COVID-19) pandemic has had grave worldwide

consequences. Among people dying from COVID-19, the largest burden is carried by the elderly

(1), and persons living in nursing homes are particularly vulnerable (2). However, non-elderly

people represent the vast majority of the global population, with 94% of the global population

being younger than 70 years old, 91% being younger than 65 years old, and 86% being younger

than 60 years old. It is therefore important to get accurate estimates of the infection fatality rate

(IFR) of COVID-19 among non-elderly people, i.e., the proportion of deceased among those

infected, and to assess the age-stratification of IFR among non-elderly strata. Such assessments

carry profound implications in public health, from evaluating the pertinence of prevention

measures to vaccine strategies. Several previous evaluations (3-6) have already synthesized

information on age-stratified estimates of IFR. Most of those used data from early published

studies, and these tended to have information from mostly hard hit countries, thus potentially

with inflated IFR estimates. Moreover, several analytical and design choices for these reviews

and data syntheses can be contested (7) and many more potentially informative seroprevalence

studies have been published since then. We recently examined age stratified IFR in the non-

elderly populations as a secondary analysis of a project focused primarily on the IFR in the

elderly (8); however, in this evaluation only studies with sampling until the end of 2020 and

which had a large number of elderly individuals were considered. The median IFR considering

available data from fully representative general population studies was 0.0009% at 0-19 years,

0.012% at 20-29 years, 0.035% at 30-39 years, 0.109% at 40-49 years, 0.34% at 50-59 years, and

1.07% at 60-69 years without accounting for seroreversion (loss of antibodies over time in

previously infected individuals)Including also convenience sample studies (and again without

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accounting for seroreversion) the respective age groups median IFR estimates were 0.001%,

0.010%, 0.023%, 0.050%, 0.15%, and 0.49%.(8).

Here, we extended the analysis of COVID-19 IFR in non-elderly age-strata pertaining to

the pre-vaccination era to examine studies published until mid-2022 regardless of whether they

had many elderly participants as well, while using rigorous methods for study selection and

analysis. We focused on studies that evaluated seroprevalence in representative general

population samples at a national level. We also explored whether population and other features

were associated with the IFR in the non-elderly population.

2. METHODS

2.1 Design and protocol

This was a mixed-methods analysis combining data from different sources. Analyses of

IFR estimation in the non-elderly were performed in countries where information on age-

stratified COVID-19 deaths was available so as to be able to separate deaths among the non-

elderly. The protocol for this study was registered at the Open Science Framework

(https://osf.io/xvupr) prior to full data analysis but after piloting data availability and after having

done analyses on some studies as part of a related project focused on IFR estimates in the elderly

(8). A secondary project using similar search strategies and eligibility criteria but focusing on

relative seroprevalence ratios in different age groups (9) has been included in the same protocol

and published separately.

2.2 Eligible seroprevalence studies

We identified seroprevalence studies (peer-reviewed publications, official reports, or

preprints) in the live systematic review SeroTracker (10) and performed a PubMed search using

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the string “seroprevalence AND (national OR stratified) AND COVID-19” to identify potentially

eligible studies that were recently published and thus may not have been yet indexed in

SeroTracker. The initial search was performed on February 8, 2022 and updated on May 25,

2022.

We included only those studies on SARS-CoV-2 seroprevalence that met the following

criteria:

(i) Sampled any number of participants aged <70 years in a national representative

sample.

(ii) Sampling was completed by end February, 2021 and at least 90% of the samples

had been collected before the end January 2021 (to avoid the impact of

vaccination on IFR calculations).

(iii) Adults (

≥

21 years old) were included, regardless of whether children and/or

adolescents were included or not.

(iv) Provided an estimate of seroprevalence for non-elderly people (preferably for <70

years and/or <60 years, but any cut-off between 54 and 70 years was acceptable)

(v) Explicitly aimed to generate samples reflecting the general population.

We excluded studies focusing on patient cohorts (including residual clinical samples),

blood donors, workers (healthcare or other), and insurance applicants and studies where the

examined population might have had lower or higher risk than the general population, as

explained and justified elsewhere (9).

Similar to the respective protocol for estimating IFR in the elderly (8) and the project on

seroprevalence ratios in non-elderly vs elderly (9), we used predefined rules (i) for studies done

in the USA (only those that had adjusted the seroprevalence estimates for race/ethnicity were

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retained, since this factor is known to associate strongly with the risk of SARS-CoV-2 infection);

(ii) for studies with several sampled (sub)regions of a country (we accepted those where the

sampling locations were dispersed across the country to form a reasonable representation of the

entire country); (iii) for studies where crude seroprevalence was less than [1-test specificity]

and/or the 95% confidence interval of the seroprevalence went to 0% (excluded, since the

uncertainty on seroprevalence [and thus also IFR] for them was very large); and (iv) for age

boundaries (excluded studies that included in their sampling only children and/or adolescents

without any adults 21 years or older; otherwise studies were accepted regardless of presence or

not of upper or lower boundaries).

Finally, the main analyses considered only studies from countries where information was

available on the proportion of cumulative COVID-19 deaths among non-elderly with an upper

cutoff placed between 60-70 years. Countries without this information were considered in

sensitivity analyses while making certain assumptions for imputation of the age distribution of

COVID-19 deaths (as discussed below).

2.3 Extracted information

Data extraction for eligible articles was performed in duplicate by at least two authors

independently (AA, AMP, DCI) and disagreements were discussed. In cases of persistent

disagreements, a third author arbitrated.

For each potentially eligible study, we tried to identify available data on the proportion of

cumulative COVID-19 deaths among people <70 years old and among people <60 years old,

which are the two main definitions for the non-elderly population in our analysis. If data were

not available for these two cut-offs, but were available for a cut-off of <65, we imputed the

respective death data for cut-offs of <70 and <60. For the imputations, we assumed that in a 10-

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year interval in that age vicinity, 1/3 of the deaths had occurred in the lower 5-year bin and 2/3 of

the deaths had occurred in the upper 5-year bin. For example, if data on deaths were given for the

age bins <55, 55-65 and 65-75 years, we assumed that 1/3 of the deaths in the age bin 55-65

occurred in the 55-60 years group so as to estimate deaths <60 years; and we assumed that 2/3 of

deaths in the age bin 65-75 occurred in the 65-70 years group so as to estimate deaths <70 years.

Studies done in countries where there was no available information on age-stratified COVID-19

deaths with an age-cutoff in the 60-70 range were considered only in sensitivity analyses with

imputation of age distribution of COVID-19 deaths (as discussed below).

Similar to previous projects (3, 9), we extracted from all eligible seroprevalence studies

their information on country, recruitment and sampling strategy, dates of sample collection,

sample size in the non-elderly group (using age cutoffs <70, <65, and <60, whichever were

available), and types of SARS-CoV2 antibodies measured (immunoglobulin G (IgG), IgM and

IgA).

For the non-elderly population, we extracted the estimated unadjusted seroprevalence

(positive samples divided by all samples tested), the most fully adjusted seroprevalence, and the

factors that the authors considered for adjustment in the most fully adjusted calculations.

Antibody titers may decline over time. For example, a modelling study estimating the average

time from seroconversion to seroreversion at 3-4 months (11) and other investigators have also

found steep decreases in antibody assay sensitivity over time (12) and a systematic review found

large variability in seroreversion rates across assays and studies (13). Therefore, for consistency,

if there were multiple different time points when seroprevalence was assessed in a given study,

we selected the one that gave the highest seroprevalence estimate and when there was a tie we

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chose the earliest one (in a sensitivity analysis, we excluded from the calculations studies where

the chosen time point was not the latest).

Whenever authors had already adjusted for seroreversion, we used the seroreversion-

adjusted estimate. When the authors had not adjusted for seroreversion, we adjusted for 5%

monthly rate of seroreversion, correcting the observed seroprevalence by 0.95m-fold, where m is

the number of months from the peak of the first epidemic wave in the specific location. The peak

of the first epidemic wave was defined as one week before the date with the highest rolling

average 7-day mortality (according to Worldometer) until August 31, 2020. If two or more dates

were tied for peak values, we chose the date corresponding to the midpoint between the first and

last one.

Whenever authors had not adjusted for antibody test performance (sensitivity and

specificity), we used the Gladen-Rogan formula (14) to make this adjustment.

The population size overall and in the non-elderly population (using cut-offs of 70 years

and of 60 years) in the relevant country were primarily obtained from the seroprevalence study.

If not provided in the study, we used either populationpyramid.net, official population data (e.g.,

the latest available national census), or worldpopulationreview.com, in that order, to retrieve the

relevant number for the end of 2020 (or as close as possible to that date).

Cumulative COVID-19 deaths overall and in the non-elderly population (using separately

the <70 and <60 year cut-offs) for the relevant country were extracted, whenever available, from

COVerAGE-DB (15) [https://osf.io/mpwjq/], The Demography of COVID-19 Deaths database

of Institut national d'études démographiques (DCD-INED) (16) [https://dc-covid.site.ined.fr/en/],

official reports, or Worldometer, in that order. Both COVerAGE-DB and DCD-INED are

compilations of official reports. The total number of deaths (confirmed and probable) was

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preferred whenever available. We extracted the accumulated deaths until the date 1 week after

the midpoint of the seroprevalence study period (or the date closest to this that had available

data) to account for different delays in developing antibodies versus dying from infection. For a

sensitivity analysis, we extracted data on accumulated deaths until the date 2 weeks after the

midpoint. By midpoint, one refers to the median date of sampling, or (if the rate of sampling over

time is unclear and there is no suggestion that it was uneven in different time periods) the time

point that is equidistant from the start and end dates. If the seroprevalence study claimed strong

arguments to use another time point or approach, while reporting official statistics on the number

of COVID-19 deaths overall and in the non-elderly population, we extracted that number instead.

The number of deaths is only an approximation and may be biased for various reasons, including

different time lag from infection to death and imperfect diagnostic documentation of COVID-19

potentially leading to either under- or over-counting (17).

2.4 Estimation of the number of infected and deceased non-elderly

The number of infected people was estimated by multiplying the adjusted estimate of

seroprevalence and the population size in non-elderly. If a study did not give an adjusted

seroprevalence estimate, we used the unadjusted seroprevalence instead, as mentioned above.

Both adjusted and unadjusted estimates were corrected for test performance and seroreversion,

unless already corrected by the authors. For locations that did not report seroprevalence data for

the non-elderly group for the <60 and <70 cut-offs, we used the seroprevalence estimate for the

closest cut-off available in the 60-70 range. We applied a correction for studies that excluded

persons with diagnosed COVID-19 from participating in their sample, primarily using study

authors’ corrections (e.g., PCR tests) or adding the number of identified COVID-19 cases in

community-dwelling non-elderly for the location until the seroprevalence study midpoint. For

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studies that performed surveys using both seroprevalence and PCR testing and presented as main

analyses data for being positive in either test, we used the data that reflect infection documented

with either way.

The total number of COVID-19 fatalities in non-elderly (for the <60 and <70 cut-offs)

were counted from available sources until 1 week after the midpoint of the seroprevalence study

period. If the age distribution of COVID-19 deaths was only available for a date more than 1

week apart from the preferred one, we assumed that the proportions of age-stratified deaths were

stable between the time points and inferred the total number of fatalities for the preferred date.

That is, we calculated the percentage of fatalities in non-elderly for the available date (namely,

the number of deaths in non-elderly divided by total number of deaths) and multiplied it with the

total number of deaths for the preferred date to obtain the COVID-19 fatalities in non-elderly for

the preferred date. When COVID-19 deaths were not available for the <60 and <70 cut-offs (e.g.

given only for the age bin 65-75), we imputed them using the 1/3-rule imputation for breaking

down 10-year bins to 5-year bins, as mentioned above.

2.5 IFR estimation

We calculated the inferred IFR in the non-elderly, by dividing the number of deaths in

this population group by the number of infected people for the same population group. We

performed separate calculations defining the non-elderly as those being <60 and those being <70

years old.

2.6 Data extraction for age-stratified analyses within the non-elderly group

The same considerations outlined above for the entire non-elderly population were

applied for extracting information on seroprevalence, population size and the number of COVID-

19 deaths for separate age strata bins within the non-elderly population, whenever available.

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Whenever seroprevalence estimates and COVID-19 mortality data were available for

specific granular age groups, we complemented data extraction for all available age strata.

Studies were excluded from the age-stratified analysis if no mortality data were available for any

age stratum of maximum width 20 years and maximum age 70 years. We used the same time

points as those selected for the overall non-elderly data analysis. We included all age strata with

a maximum width of 20 years and available COVID-19 mortality information.

We corresponded the respective seroprevalence estimates for each age stratum with

eligible mortality data. Consecutive strata of 1-5 years were merged to generate 10-year bins. For

seroprevalence estimates we used the age strata that most fully cover/correspond to the age bin

for which mortality data are available; specifically for the youngest age groups, seroprevalence

data from the closest available group with any sampled persons

≤

20 years were accepted. E.g. for

the Ward et al UK study (18), the youngest stratum with seroprevalence data is 18-24 years old.

Population statistics for each analyzed age bin were obtained from the same sources as for the

overall analysis for the non-elderly.

For countries for which age information was missing for a proportion of the cumulative

COVID-19 deaths, we assumed the age distribution to be the same as for the non-missing

proportion.

2.7. Data synthesis

The main outcomes were the IFR in people <60 years old and <70 years old, as well as

age-stratified IFR estimates in smaller age bins among the non-elderly.

Similar to previous work on IFR-estimating studies (3,8), we estimated the sample size-

weighted IFR of non-elderly (separately for <60 and <70 years old) for each country (if multiple

studies were available for that country) and then estimated the median and range of IFRs across

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countries. We expected very large heterogeneity among IFR estimates, therefore we did not use

meta-analysis methods.

To generate plots of IFRs with some estimates of uncertainty, we performed calculation

of 95% CIs of IFRs based on extracted 95% CIs from seroprevalence estimates. Primarily, 95%

confidence intervals are direct extractions from the seroprevalence studies. For studies that did

not report such intervals, we complemented the analysis with a calculation using the number of

sampled and seropositive non-elderly individuals (Clopper Pearson interval calculation). For

those that provided adjusted estimates for age brackets, we combined estimates for each study

using a fixed effects inverse variance meta-analysis (of arcsine transformed proportions) to

obtain 95% CIs. No further factors were introduced in the calculation beyond the adjustments

made by seroprevalence study authors (except adjusting estimates for test performance using the

Gladen-Rogan formula and adjusting also for seroreversion -assuming 5% monthly

seroreversion-, where applicable).

Similar to the overall non-elderly analyses, for age strata with multiple estimates from the

same country, we calculated the sample size-weighted IFR per country before estimating median

IFRs across countries for age groups 0-19, 20-29, 30-39, 40-49, 50-59, and 60-69 years. IFR

estimates were placed in these age groups according to their midpoint, regardless of whether they

perfectly match the age group or not, e.g. an IFR estimate for age 18-29 years was placed in the

20-29 years group. As for the main analysis, whenever no adjustment had been made for test

performance, we adjusted the estimates for test performance using the Gladen-Rogan formula;

and whenever there had been no adjustment for seroreversion, we corrected the results assuming

5% monthly seroreversion.

2.8 Sensitivity analyses

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We performed the following sensitivity analyses:

1. Limited to high-income countries. Under- and over-counting of deaths may occur also in

high-income countries (17), but the concern for under-counting is more serious in other

countries (19). Nevertheless, under-counting may be much less of a problem in non-

elderly people than in the elderly.

2. Considering deaths up to 2 weeks after the midpoint of seroprevalence sampling, instead

of just one week.

3. Excluding studies where the chosen time point was not the latest available (observed

seroprevalence has declined subsequently).

4. Exploring different seroreversion corrections of the IFR by Xm-fold, where m is the

number of months from the peak of the first epidemic wave in the specific location.X was

given values of 1.00, 0.99, and 0.90 corresponding to no seroreversion, 1%, and 10%

relative rate of seroreversion every month from the peak of the first epidemic wave in the

specific location to the date of seroprevalence estimate.

5. Including in the overall calculations of IFR in the non-elderly also imputed data from

countries where the proportion of COVID-19 deaths occurring among the non-elderly

was not available. This is a post-hoc sensitivity analysis and it was adopted because a

substantial number of studies fell in this category. Specifically, we assumed that the

proportion of COVID-19 deaths represented by the non-elderly was a minimum of 10%

for 0-59 years (and 20% for 0-69 years) and a maximum of 60% for 0-59 years (and 90%

for 0-69 years).

2.9 Evaluation of heterogeneity

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We explored whether the estimated IFR for the non-elderly across different countries was

associated with the structure of the age pyramid in the population of each country. Specifically,

we performed meta-regression analyses of the country IFR in the non-elderly against the

proportion of the non-elderly population that is <50 years old. Separate regression analyses were

performed using the definition of non-elderly as being <70 years old and <60 years old.

Additional factors that were explored for association with IFR in the non-elderly were country-

income (high-income country versus other), and the population-level annual mortality rate in

each country (https://worldpopulationreview.com/country-rankings/death-rate-by-country). We

used these observations in trying to extrapolate to the respective features of the global

population, to try to approximate the IFR among the non-elderly in the global population.

3. RESULTS

3.1 Eligible studies

By February 8, 2022, Serotracker had 2930 seroprevalence studies, of which 547 entries

were described as "national". Of those, 420 had their sampling end date before February 28,

2021. 183 were characterized as “household and community samples” or “multiple populations”.

Of those, 107 were of low, moderate or unclear risk of bias. We screened in-depth the 107 entries

and 73 were excluded. Therefore, 34 studies were eligible from this source. Our search on

PubMed yielded 474 items, of which four additional eligible studies were identified. On May 25,

2022, we updated the search and found 2 additional studies to be included. In total, data from 40

studies which covered national seroprevalence estimates for 38 different countries were extracted

and analyzed (18,20-58). 30 countries had publicly available age-stratified COVID-19 death

data. The report of one of these countries (Austria) did not report any information on age-

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stratified seroprevalence. Therefore, 29 countries with data from 31 studies were included in the

primary analysis (Appendix Figure 1).

3.2 Characteristics of eligible studies

Table 1 shows the main characteristics for the 31 studies with publicly available age-

stratified COVID-19 death and seroprevalence data. As shown, these data originated from 24

high income countries and 5 other countries.

3.3 IFR estimates in the non-elderly

In 29 countries of the primary analysis, with age-stratified COVID-19 death and

seroprevalence data, IFRs in non-elderly (Figure 1, Table 1) had a median of 0.035%

(interquartile range (IQR) 0.013 - 0.056%, Figure 1A) for the 0-59 years old population, and of

0.095% (IQR 0.036 - 0.125%, Figure 1B) for the 0-69 years old population. Figure 1 also shows

95% CIs for IFRs based on 95% CIs for seroprevalence estimates.

3.4 IFR estimates per narrow age strata

For the narrow age bins analysis (Figure 2), the median IFR was 0.0003% (IQR, 0.0000

to 0.002) at 0-19 years, 0.003% (IQR, 0.000 to 0.007) at 20-29 years, 0.011% (IQR, 0.005 to

0.031) at 30-39 years, 0.035% (IQR, 0.011 to 0.077) at 40-49 years, 0.129% (IQR 0.047 to

0.220) at 50-59 years, and 0.501% (IQR, 0.208 to 0.879) at 60-69 years. Excluding from the

calculations age bins with 0 deaths (where IFR is thus calculated as 0.000% but has very large

uncertainty), the median IFR was 0.001%, 0.006%, 0.012%, 0.048%, 0.158%, and 0.544% in

these age bins, respectively.

3.5 Sensitivity analyses

Among high-income countries, the median IFR was 0.038% in the 0-59 years old age

group and 0.098% in the 0-69 years old age group. Sensitivity analysis considering deaths up to

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2 weeks after the midpoint of seroprevalence sampling, instead of just one week, yielded largely

similar results (not shown). Sensitivity analysis excluding studies where the chosen time point of

peak seroprevalence was not the latest available (observed seroprevalence has declined

subsequently) yielded median IFR of 0.035% in the 0-59 years old group and 0.093% in the 0-69

years old age group. Appendix Table 2 shows results with different assumptions about

seroreversion.

In the post hoc sensitivity analysis aiming to include all countries in the calculations, for

countries without available age-stratified mortality data, 10-60% and 20-90% of COVID-19

deaths were assumed to have occurred among 0-59 and 0-69 year old people, respectively.

Moreover, since data on age stratified deaths for Austria had been collected but the

seroprevalence study report did not describe age stratified seroprevalence, we considered the

overall seroprevalence (4.7%) for 0-59 and 0-69 age groups in this additional analysis. Under the

minimum age-stratified mortality scenario, the median IFRs were 0.025% (IQR 0.006 - 0.043%)

for the 0-59 and 0.063% (IQR 0.011 - 0.113%) for the 0-69 age group. Under the maximum

scenario, the median IFRs were 0.032% (IQR 0.012 - 0.053%) for the 0-59 and 0.082% (IQR

0.034 - 0.117%) for the 0-69 age group.

3.5 Evaluation of heterogeneity

The pre-specified regression of IFR for the 0-59 years old age group against the

proportion of people <50 years old (Figure 3A) had a slope of -0.002 (p



0.08), suggesting an

IFR of 0.054%, 0.043%, and 0.026% when the proportion of people <50 years old in the 0-59

group was 77.5%, 82.5%, and 90%, respectively. The same analysis for the 0-69 years old age

group (Figure 3B) had a slope of -0.004 (p



0.01), suggesting an IFR of 0.139%, 0.117%,

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0.072%, and 0.027% when the proportion of people <50 years old in the 0-69 group was 65%,

70%, 80%, and 90%, respectively.

The median IFR for the 0-59 years old age group was 0.038% in high-income countries

versus 0.008% in other countries (p = 0.12 by Mann-Whitney U test). The median IFR for the 0-

69 years old group was 0.098% in high-income countries versus 0.012% in other countries (p =

0.04 by Mann-Whitney U test). A regression of IFR for the 0-59 years old age group against the

crude death rate per 1,000 people (of all ages) in each country had a slope of 0.002 (p = 0.46),

while for the 0-69 age group the slope was 0.009 (p = 0.16).

4. DISCUSSION

The current comprehensive systematic evaluation of national seroprevalence studies

suggests that the IFR of COVID-19 among non-elderly populations in the pre-vaccination era is

substantially lower than previously calculated (4-8,59), especially in the younger age strata.

Median IFRs show a clear age-gradient with approximately 3-4-fold increase for each decade but

it starts from as low as 0.0003% among children and adolescents and it reaches 0.5% in the 60-

69 years old age group. Sensitivity analyses considering all 38 countries with seroprevalence

data that were identified in our systematic search showed that median IFR might be up to a third

lower than the estimates produced by our main analysis, e.g. approximately 0.03% in the 0-59

years age group and 0.06-0.08% in the 0-69 years old group. Consistent with these estimates,

meta-regressions suggest IFR estimates in that range for the global population where 87% of the

0-59 years old people are <50 years old and 80% of the 0-69 years old people are <50 years old.

Our IFR estimates tend to be modestly to markedly lower than several previous

calculations (4-8, 59). The most comprehensive prior evaluation of COVID-19 IFR in the pre-

vaccination era (59) suggested a trough IFR at the age of 7 years (0.0023%, 95% uncertainty

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interval 0.0015–0.0039) and increasing exponentially through 30 years (0.0573%, 0.0418–

0.0870), 60 years (1.0035%, 0.7002–1.5727) and older ages. Conversely, our median IFR

estimates are roughly 10-fold lower than these previous calculations among children and young

adults and 3-6-fold lower among adults 40-69 years old. If we exclude study data from age bins

with 0 deaths in our calculations (a justifiable choice, since these estimates of 0% IFR are clearly

underestimates), our age-stratified IFR are still approximately 2-5-fold lower than those of (59)

across the entire age range. The previous IFR calculations (4-8, 59) were based on more limited

national representative studies’ data and also included data from non-national samples with

potentially larger bias. They also probably included mostly hard hit countries that may tend to

have the highest IFR estimates. While much of the diversity in IFR across countries is explained

by differences in age structure (59), additional substantial differences are possible. Another

major reason for the discrepancy versus prior calculations is due to the fact that some previous

calculations (e.g. ref. 59) have substantially increased their initial IFR estimates by multiplying

them for a factor of under-ascertainment of COVID-19 deaths. Aligning evaluations in terms of

this methodological difference would bring the estimates closer, but divergence would still be

present with our estimates remaining lower. Some other estimates for pre-vaccination IFR agree

more with our estimates overall, e.g. 0.107% across all ages combined (60).

The median IFR estimates should not diminish attention to the large heterogeneity that

was observed across different studies and countries. Some of the observed heterogeneity may be

data artefacts (e.g. if the number of deaths or seroprevalence are not accurately measured) and

some may reflect genuine differences across populations and settings. Fatality risk from COVID-

19 is strongly influenced by the presence and severity of comorbidities (61). While this is

extremely well documented from population studies, IFR estimates stratified for comorbidity are

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typically not available in national seroprevalence studies. A national study of blood donors in

Denmark has estimated an IFR of only 0.00336% for people < 51 years without comorbidity, and

0.281% for people aged 61-69 years old without comorbidity (62). The proportion of people with

some comorbidities that are very influential for COVID-19 outcomes such as obesity is very

different across different countries, even for the same age groups. For example, obesity affects

42% of the USA population (63), but the proportion of obese adults is only 2% in Vietnam, 4%

in India and <10% in most African countries (64). However, also within Africa, obesity affects

0% of Ethiopian women and almost 40% of South African women (65). Another influential

difference is the presence of frail individuals in long-term facilities, where IFRs may be much

higher and to what extent these highly vulnerable individuals are infected. Even though the vast

majority of frail individuals in long-term care are

≥

70 years old, a small proportion are younger

and they may account for a substantial proportion of deaths in the non-elderly strata that we

examined in the current analysis, especially in some high income countries, but not in others.

Other differences in management, health care, overall societal support and concomitant

epidemics, e.g. drug overdose (66), may have also shaped large differences across countries.

Some limitations should be acknowledged in this work. Data artefacts in the form of

measurement errors may have affected the results of some studies included in this analysis, and

therefore also the data synthesis. Seroprevalence studies have many caveats (7) and uncertainty

in seroprevalence estimates is larger than conveyed by typical 95% confidence intervals. Overall,

however, there is no reason to suggest that over-estimation of seroprevalence is far more or far

less common that under-estimation. Among the 40 studies in our evaluation, the Italian national

seroprevalence study provided estimates that are very far from any other study. A notable

difference that we found in this study is the requirement to isolate after a positive result to the

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antibody test (67,68). This might have discouraged the participation of people that expected to

test positive, thus likely overestimating the IFR (68). Outliers are more suspect for bias and

inaccuracies, hence, we primarily focused on the median values. For death counts, it is more

likely that COVID-19 deaths were under-counted in the first waves, but both over- and under-

counting may have occurred to some extent in different settings (17). Some of the studies that

suggest higher estimates of IFR use large corrections for under-counting of deaths (59,69).

However, it is unclear whether such large corrections are justified. In particular, for the non-

elderly age groups, deaths among young adults and children may be less likely to have been

missed, as opposed to deaths of elderly individuals where causal attribution to a single cause can

be more difficult and where even in high income countries under-reporting of COVID-19 may

have occurred if testing was not widespread. For example, in the Netherlands, the national

statistics service suggests that many COVID-19 deaths may have not been recorded in the first

wave; however, these pertained largely to elderly individuals (70).

Consistent with the very low IFR estimates in non-elderly that we have obtained in this

work, excess death calculations (71) show no excess deaths among children and adolescents

during the pandemic in almost any country that has highly reliable death registration data. In

most of these countries, moreover, excess deaths in non-elderly adults are very limited, but

exceptions do occur, most notably in the USA where almost 40% of excess deaths were in

populations younger than 65 years (71). This picture is very consistent with the overall very low

IFR in the non-elderly, but also the large diversity in the risk profiles of populations in different

countries.

Finally, the data that we analyzed pertain to the pre-vaccination period. During 2021 and

2022, the use of vaccination and the advent of new variants plus pre-existing immunity from

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prior infections resulted in a marked decline in the IFR. Studies in Denmark (72) and Shanghai

(73) suggest that in 2022, IFRs in vaccinated, previously not infected populations were

extremely low. For example, in Denmark, IFR was only

1.6 per 100,000 infections for ages 17-

35 and even in ages 61-72 it was only 15.1 per 100,000 infections. In Shanghai, in 2022, IFR

was 0.01% among vaccinated individuals aged 40-59 and close to 0% for younger vaccinated

people, while it was practically 0% for children and adolescents regardless of vaccination. Other

population studies, e.g. in Vojvodina, Serbia (74), suggest that fatality rates may be ten times

lower in re-infections versus primary infections. The relative contributions of vaccination, prior

infection and new variants in the IFR decline needs careful study and continued monitoring.

However, it is reassuring that even in the wild strains that dominated the first year of the

pandemic, the IFR in non-elderly individuals was much lower than previously thought.

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Credit author statement

A.M.P.: Conceptualization; Data curation; Formal analysis; Investigation; Writing –

original draft; Writing – review & editing. C.A.: Conceptualization; Data curation; Investigation;

Writing – review & editing. D.G.C.-I.: Conceptualization; Data curation; Investigation; Writing

– review and editing. A.A.: Conceptualization; Data curation; Investigation; Writing – review

and editing. J.P.A.I.: Conceptualization; Data curation; Formal analysis; Investigation; Writing –

original draft; Writing – review & editing.

Funding

The work of John Ioannidis is supported by an unrestricted gift from Sue and Bob

O'Donnell. The work of Angelo Maria Pezzullo in this research has been supported by the

European Network Staff Exchange for Integrating Precision Health in the Healthcare Systems

project (Marie Skłodowska-Curie Research and Innovation Staff Exchange no. 823995).

Cathrine Axfors has received funding outside this work from the Knut and Alice Wallenberg

Foundation’s Postdoctoral Fellowship (KAW 2019.0561) and postdoctoral grants from Uppsala

University (E o R Börjesons stiftelse; Medicinska fakultetens i Uppsala stiftelse för psykiatrisk

och neurologisk forskning), The Sweden-America Foundation, Foundation Blanceflor, Swedish

Society of Medicine, and Märta och Nicke Nasvells fond. The funders had no role in the design

and conduct of the study; collection, management, analysis, and interpretation of the data;

preparation, review, or approval of the manuscript; or decision to submit the manuscript for

publication.

Data statement

The protocol, data, and code used for this analysis will be made available at the Open Science

Framework upon publication: https://osf.io/xvupr.

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Ethical approval

Not applicable.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal

relationships that could have appeared to influence the work reported in this paper.

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FIGURE LEGENDS

Figure 1. Infection fatality rate (IFR) and 95% confidence interval per country. Panel (A) for <60 years old people. Panel (B)

for <70 years old people.

Italy

Mexico

USA

Germany

England

Ireland

Hungary

Finland

Spain

Iceland

Oman

Portugal

Canada

Norway

France

Japan

Jersey

Netherlands

Andorra

Pakistan

Slovenia

Lithuania

Denmark

Czech Republic

Nepal

Israel

Afghanistan

Lao PDR

Faroe Islands

0.00 0.05 0.10 0.15

IFR (%)

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Note: For multiple estimates from the same country (France and USA), we calculated the sample size-weighted IFR per country. The 95% confidence intervals

are estimated primarily as direct extractions from the seroprevalence studies. For studies that did not report 95% confidence intervals, we complemented with a

Italy

Germany

Ireland

USA

England

Mexico

Hungary

Finland

Jersey

Iceland

Canada

Spain

Japan

Portugal

France

Norway

Netherlands

Oman

Andorra

Slovenia

Lithuania

Denmark

Czech Republic

Pakistan

Israel

Nepal

Afghanistan

Lao PDR

Faroe Islands

0.0 0.1 0.2 0.3 0.4 0.5

IFR (%)

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

calculation using the number of sampled and seropositive individuals. For those that provided adjusted estimates for age brackets (e.g., 0–9, 10–19, 20-29, etc.),

we combined estimates for each study using a fixed effects inverse variance meta-analysis (of arcsine transformed proportions) to obtain 95% confidence

intervals. Asymmetry around point estimates may be observed for these cases, since point estimates were calculated by multiplying age bracket seroprevalence

by the corresponding population count (which is preferable, since it takes into account population distribution). Please note that uncertainty in seroprevalence

estimates is larger than conveyed by typical 95% confidence intervals.

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Figure 2. IFR in each country per each specified age bin

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Figure 3. Meta-regressions of IFR as a function of the proportion of the population <50 years old among (A) among those 0-59

years old and (B) among those 0-69 years old.

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Table 1. Eligible studies for the main analysis (those countries that have age-stratified COVID-19 death data and

seroprevalence information)

Country (first

author) Sampling

period

Number

tested

<60

[<70]

Antibody

type(s)

Adjusted

seroprevalence

<60 [<70] (%) Adjustments made COVID-19

deaths <60

[<70] (n)

Population

<60 [<70]

(n)

IFR in <60

[<70] (%)

Afghanistan

(Saeedzai) 6/1/20 to

6/30/20 NA

[5168] IgG/IgM 35.1 (35.2) NA 355 (576) 37284465

(38341961) 0.003

(0.004)

Andorra (Royo-

Cebrecos) 5/4/20 to

5/28/20 49355

[55347] IgG/IgM 12.3 (12.46) NA 2 (6) 61881

(70384) 0.022

(0.058)

Canada (Tang) 5/1/20 to

9/30/20 5789

[7938] IgG only 2.12 (2.08) NA 280 (915) 28346618

(33059361) 0.039

(0.113)

Czech Republic

(Piler) 12/1/20 to

1/31/21 5665

[NA] IgG only 42.77 (42.77) NA 565 (2111) 7908150

(9232767) 0.011

(0.034)

Denmark

(Espenhain)

9/11/20 to

12/11/20

(median

date

12/16/20)

NA [NA] IgG/IgM/I

gA 4.64 (4.64)

Test sensitivity and

specificity using the

Rogan-Gladen

estimator

36 (129) 4278562

(4933092) 0.012

(0.036)

England (Ward) 6/20/20 to

7/13/20 77955* IgG only 6.76 (6.25)

Test performance, and

weighted to account

for sample design and

for variation in

response rate (age,

sex, ethnicity, region

and deprivation) to be

representative of the

England population

over 18 years

2586 (6425) 42889306

(48870419) 0.076

(0.179)

Faroe Islands

(Petersen) 11/21/20 to

11/30/20 40467

[46152] IgG/IgM/I

gA 0.54 (0.63) NA 0 (0) 40467

(46152) 0 (0)

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

Finland (Melin) 4/13/20 to

1/25/21 NA

[4887] IgG only 0.61 (0.61) NA 17 (43) 3934387

(4646712) 0.056

(0.119)

France

(Warszawski)

Median

date

11/24/20 48993* IgG only 7.04 (6.61) Sample design, non-

response, census

calibration 1968 (6024) 47753753

(55518538) 0.039

(0.109)

France (Carrat) 5/4/20 to

9/30/20 40193

[56843] IgG only 6.71 (5.9) NA 1306 (3684) 47753753

(55518538) 0.029

(0.079)

Germany

(Neuhauser)

10/1/20 to

2/28/21

(median

date

11/11/20)

11302* IgG only 1.96 (1.96) Non-response, test

performance and

seroreversion 903 (2708) 59792644

(70436786) 0.077

(0.196)

Hungary

(Merkely) 5/1/20 to

5/16/20 8088* IgG only 0.64 (0.64)

Design weighted.

Response sample

calibrated to known

population counts by

region, sex, and age

categories.

27 (95) 7076206

(8382638) 0.057 (0.17)

Iceland

(Gudbjartsson) 4/27/20 to

6/5/20 NA IgG/IgM/I

gA 0.8 (0.8) NA 1 (3) 267524

(305060) 0.043

(0.113)

Ireland (Heavey) 6/22/20 to

7/16/20 NA IgG only 1.69 (1.69)

Weighted to adjust for

varying response rates

in age-sex strata 64 (190) 4217964

(4779564) 0.073

(0.192)

Israel (Reicher)

6/28/20 to

9/14/20

(median

date

7/9/20)

38673

[47423] IgG only 5.18 (4.95)

Age, sex, time period,

RT-PCR status,

municipal strata,

sampling

21 (60) 7231052

(7934695) 0.004

(0.012)

Italy (Sabbadini) 5/25/20 to

7/15/20 NA IgG only 2.44 (2.46)

Non-response, region,

age, sex, working

status, province 1600 (5112) 41830101

(49314963) 0.135

(0.361)

Japan

(Yoshiyama) 6/1/20 to

6/7/20 5156

[6476] IgG/IgM/I

gA 0.13 (0.12) NA 37 (122) 83064470

(98939705) 0.033

(0.098)

Jersey Median 1077* IgG/IgM 3.65 (3.65) NA 1 (4) 77734 0.032

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

(Government of

Jersey) date

6/16/20 (87432) (0.113)

Lao PDR

(Virachith) 8/12/20 to

9/25/20 2082

[NA] IgG/IgM 4.46 (4.46) Weighted for complex

survey sample design,

age and sex 0 (0) 6781408

(7099379) 0 (0)

Lithuania

(Smigelskas) 8/10/20 to

9/10/20 NA IgG/IgM 1.38 (1.38) NA 5 (17) 1974425

(2327236) 0.013

(0.038)

Mexico (Basto-

Abreu) 8/18/20 to

11/13/20 NA IgG/IgM/I

gA 25.77 (25.77)

Test performance, and

used sampling weights

to adjust for selection

probabilities and non-

response rates (with

post-stratification on

region, sex and age

group)

36779

(62926) 114441068

(122706021) 0.108

(0.172)

Nepal

(Government of

Nepal)

10/9/20 to

10/22/20

(median

date

10/16/20)

NA IgG/IgM/I

gA 13.54 (13.64) Survey design

weights, age 340 (504) 26615582

(28103660) 0.008

(0.011)

Netherlands (Vos)

6/9/20 to

8/24/20

(median

date

6/14/20)

4600

[5817] IgG only 4.46 (4.56)

Adjusted for survey

design, weighted to

match the distribution

of the general Dutch

population (based on

sex, age, ethnic

background, and

degree of

urbanization) and

controlled for test

characteristics

196 (694) 12576973

(14706474) 0.03 (0.09)

Norway (Eik

Anda)

11/25/20 to

2/15/21

(median

date

22264* IgG only 0.94 (0.94)

Rake weighting for

population estimates

of seroprevalence by

age, sex, place of birth

23 (64) 4159899

(4746055) 0.037

(0.091)

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

12/20/20) and county based on

individual-level data

for the invited sample

(participants and non-

responders) together

with the corresponding

distributions from the

source population,

provided by the

Norwegian Population

propensity scores for

nonresponse

adjustment and

jackknife replicate

weights for the raking

procedure. Estimates

subsequently corrected

for test performance

Oman (Al Abri) 11/8/20 to

11/13/20 NA IgG only 22.32 (22.32)

Age group, sex,

nationality 553 (930) 4888809

(5031596) 0.042

(0.068)

Pakistan (Ahmad) 10/21/20 to

11/8/20 4022

[NA] IgG/IgM 6.33 (6.33) NA 3287 (5448) 206007412

(214909826) 0.02 (0.031)

Portugal (Canto e

Castro)

9/8/20 to

10/14/20

(>90% of

the test

were

performed

9/8/20 to

9/20/20)

NA IgG/IgM/I

gA 2.32 (2.32)

Adjusted for test

performance, used

sample weights and

post-stratified by sex

to adjust the

seroprevalence

extrapolating from the

strata to the whole

population

89 (257) 7202167

(8495991) 0.04 (0.098)

Slovenia (Poljak) 10/17/20 to

11/10/20 NA IgG/IgM/I

gA 5.32 (5.32) NA 18 (55) 1502217

(1787158) 0.015

(0.041)

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

Spain

(Government of

Spain)

11/16/20 to

11/29/20 NA IgG only 9.58 (9.71)

Characteristics of the

random subsample of

the fourth round 2329 (6593) 34605241

(39945895) 0.046

(0.111)

USA (Sullivan)

8/9/20 to

12/8/20

(median

date

10/30/20)

3481* IgG/IgM/I

gA 16.48 (16.48) Test performance,

design weights 32487

(73947) 255284698

(293772868) 0.077

(0.153)

USA (Kalish)

4/1/20 to

8/4/20

(>90% of

the tests

were

performed

5/10/20 to

7/31/20)

6785

[NA] IgG/IgM/I

gA 4.8 (4.8)

Age, region, sex,

urban/rural, race,

Hispanic, BRFSS

survey response,

sensitivity, specificity

16411

(37410) 255284698

(293772868) 0.097

(0.192)

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

Avoidable Intensive Care Resource Use of Unvaccinated COVID-19 Patients: Interpretation and Policy Implications

Preprint

Full-text available

Nov 2022

We aim to use a recently published research study as an example in order to demonstrate how data can be misinterpreted and result in deriving misleading policy implications. Bagshaw et al wrote that unvaccinated patients with COVID-19 in Alberta, Canada “had substantially greater rates of ICU admissions, ICU bed days, and ICU related costs than vaccinated patients did. This increased resource use would have been potentially avoidable had these unvaccinated patients been vaccinated.” The authors in Bagshaw et al then concluded that their findings “have important implications for discourse on the relative balance of increasingly stringent public health protection (restrictions), including mandatory vaccination policies, and the sustainability and function of health system infrastructure and capacity during the ongoing COVID-19 pandemic.” Here we show the following. First, the effect of vaccination on intensive care admissions were grossly over-estimated. Second, an effect of vaccination on access to acute care and on all-cause excess deaths was grossly over-stated. Third, policy implications were overstated and at best unclear. Overall, the data cannot support what Bagshaw et al called “increasingly stringent public health protection (restrictions), including mandatory vaccination policies”.

Judeo-Christian Analysis of the COVID-19 Crisis and Its Management

Article

Full-text available

Feb 2023

The present coronavirus crisis caused major worldwide disruption. Numerous experts admit now that the crisis management was far from optimal from the very beginning. In the paper, we first digest the available information on the crisis and its management. We then list factors that led to the chosen way of crisis management. Afterwards, we question whether religious leaders could have gained enough information for them not to have supported lockdowns etc. back in March, 2020. In our opinion, they could have if they would have addressed trustworthy experts with a list of reasonable professional questions. We then analyze the question if hypothetical coercive measures are justified in the case when they are effective in decreasing the overall mortality but cause the death of several people. Our conclusion is that such measures are not justified ethically, and implementing emergency powers is justified only in the case of war. Finally, we formulate several important problems highlighted by COVID-19 to be discussed in the future.

Short-term Vaccine Fatality Ratio of booster and 4th dose in The Netherlands

Research

Full-text available

Oct 2022

André Redert

This report proposes a new and simple method to measure vaccination-mortality correlation, test for causality, and if passed, obtain Vaccine Fatality Ratio (VFR), in the short-term of 0-4 weeks after vaccination. Only data of weekly administered number of doses and weekly all-cause-deaths are required, without reference to e.g. mortality prognoses, covid-labeled deaths or excess mortality. This report presents evidence of a short-term causal relation from 4th-dose covid-vaccination to mortality in The Netherlands, with a VFR of 0.18% corresponding to ca. 4400 vaccine-related deaths, in the order of magnitudes of covid IFR and observed excess mortality.

Seroprevalence and infection fatality rate of the SARS-CoV-2 Omicron variant in Denmark: A nationwide serosurveillance study

Article

Full-text available

Oct 2022

Background Introduction of the Omicron variant caused a steep rise in SARS-CoV-2 infections despite high vaccination coverage in the Danish population. We used blood donor serosurveillance to estimate the percentage of recently infected residents in the similarly aged background population with no known comorbidity. Methods To detect SARS-CoV-2 antibodies induced due to recent infection, and not vaccination, we assessed anti-nucleocapsid (anti-N) immunoglobulin G (IgG) in blood donor samples. Individual level data on SARS-CoV-2 RT-PCR results and vaccination status were available. Anti-N IgG was measured fortnightly from January 18 to April 3, 2022. Samples from November 2021 were analysed to assess seroprevalence before introduction of the Omicron variant in Denmark. Findings A total of 43 088 donations from 35 309 Danish blood donors aged 17–72 years were screened. In November 2021, 1·2% (103/8 701) of donors had detectable anti-N IgG antibodies. Adjusting for test sensitivity (estimates ranging from 74%–81%) and November seroprevalence, we estimate that 66% (95% confidence intervals (CI): 63%–70%) of the healthy, similarly aged Danish population had been infected between November 1, 2021, and March 15, 2022. One third of infections were not captured by SARS-CoV-2 RT-PCR testing. The infection fatality rate (IFR) was 6·2 (CI: 5·1–7·5) per 100 000 infections. Interpretation Screening for anti-N IgG and linkage to national registers allowed us to detect recent infections and accurately assess assay sensitivity in vaccinated or previously infected individuals during the Omicron outbreak. The IFR was lower than during previous waves. Funding The Danish Ministry of Health.

Comparison of pandemic excess mortality in 2020-2021 across different empirical calculations

Article

Full-text available

Jun 2022
ENVIRON RES

Different modeling approaches can be used to calculate excess deaths for the COVID-19 pandemic period. We compared 6 calculations of excess deaths (4 previously published [3 without age-adjustment] and two new ones that we performed with and without age-adjustment) for 2020-2021. With each approach, we calculated excess deaths metrics and the ratio R of excess deaths over recorded COVID-19 deaths. The main analysis focused on 33 high-income countries with weekly deaths in the Human Mortality Database (HMD at mortality.org) and reliable death registration. Secondary analyses compared calculations for other countries, whenever available. Across the 33 high-income countries, excess deaths were 2.0-2.8 million without age-adjustment, and 1.6-2.1 million with age-adjustment with large differences across countries. In our analyses after age-adjustment, 8 of 33 countries had no overall excess deaths; there was a death deficit in children; and 0.478 million (29.7%) of the excess deaths were in people <65 years old. In countries like France, Germany, Italy, and Spain excess death estimates differed 2 to 4-fold between highest and lowest figures. The R values' range exceeded 0.3 in all 33 countries. In 16 of 33 countries, the range of R exceeded 1. In 25 of 33 countries some calculations suggest R > 1 (excess deaths exceeding COVID-19 deaths) while others suggest R < 1 (excess deaths smaller than COVID-19 deaths). Inferred data from 4 evaluations for 42 countries and from 3 evaluations for another 98 countries are very tenuous. Estimates of excess deaths are analysis-dependent and age-adjustment is important to consider. Excess deaths may be lower than previously calculated.

Seroprevalence of antibodies against SARS-CoV-2 in the adult population during the pre-vaccination period, Norway, winter 2020/21

Article

Full-text available

Mar 2022
Euro Surveill

Background Since March 2020, 440 million people worldwide have been diagnosed with COVID-19, but the true number of infections with SARS-CoV-2 is higher. SARS-CoV-2 antibody seroprevalence can add crucial epidemiological information about population infection dynamics. Aim To provide a large population-based SARS-CoV-2 seroprevalence survey from Norway; we estimated SARS-CoV-2 seroprevalence before introduction of vaccines and described its distribution across demographic groups. Methods In this population-based cross-sectional study, a total of 110,000 people aged 16 years or older were randomly selected during November–December 2020 and invited to complete a questionnaire and provide a dried blood spot (DBS) sample. Results The response rate was 30% (31,458/104,637); compliance rate for return of DBS samples was 88% (27,700/31,458). National weighted and adjusted seroprevalence was 0.9% (95% CI (confidence interval): 0.7–1.0). Seroprevalence was highest among those aged 16–19 years (1.9%; 95% CI: 0.9–2.9), those born outside the Nordic countries 1.4% (95% CI: 1.0–1.9), and in the counties of Oslo 1.7% (95% CI: 1.2–2.2) and Vestland 1.4% (95% CI: 0.9–1.8). The ratio of SARS-CoV-2 seroprevalence (0.9%) to cumulative incidence of virologically detected cases by mid-December 2020 (0.8%) was slightly above one. SARS-CoV-2 seroprevalence was low before introduction of vaccines in Norway and was comparable to virologically detected cases, indicating that most cases in the first 10 months of the pandemic were detected. Conclusion Findings suggest that preventive measures including contact tracing have been effective, people complied with physical distancing recommendations, and local efforts to contain outbreaks have been essential.

SARS-CoV-2 seroprevalence in Mongolia: Results from a national population survey

Article

Full-text available

Dec 2021

Background With the global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in early 2020, Mongolia implemented rapid emergency measures and did not report local transmission until November 2020. We conducted a national seroprevalence survey to monitor the burden of SARS-CoV-2 in Mongolia in the months surrounding the first local transmission. Methods During October-December 2020, participants were randomly selected using age stratification and invited for interviews and blood samples at local primary health centres. We screened for total SARS-CoV-2 antibodies, followed by two-step quantitative SARS-CoV-2 IgG serology tests for positive samples. Weighted and test-adjusted seroprevalences were estimated. We used chi-square, Fisher's exact and other tests to identify variables associated with seropositivity. Findings A total of 5000 subjects were enrolled. We detected SARS-CoV-2 IgG antibodies in 72 samples. Crude seroprevalence of SARS-CoV-2 antibodies was 1·44% (95%CI,1·21-1·67). Population weighted and test-adjusted seroprevalences were 1·36% (95%CI,1·11-1·63) and 1·45% (95%CI,1·11-1·63), respectively. Age, sex, geographical, and occupational factors were not associated with seropositivity (p>0·05). Symptoms and signs within past 3 months and seropositivity were not associated at the time of the survey (p>0·05). Interpretation SARS-CoV-2 seroprevalence in Mongolia was low in the first year of the pandemic potentially due to strong public health measures, including border restrictions, educational facilities closure, earlier adoption of mask-wearing and others. Our findings suggest large-scale community transmission could not have occurred up to November 2020 in Mongolia. Additional serosurveys are needed to monitor the local pandemic dynamic and estimate how far from herd immunity Mongolia will be following-up with vaccination programme in 2021 and 2022. Funding World Health Organisation, WHO UNITY Studies initiative, with funding by the COVID-19 Solidarity Response Fund and the German Federal Ministry of Health (BMG) COVID-19 Research and development. Translation Cyrillic and Traditional Mongolian translation of abstract is available on appendix section.

Estimates of global SARS-CoV-2 infection exposure, infection morbidity, and infection mortality rates in 2020

Article

Full-text available

Nov 2021

We aimed to estimate, albeit crudely and provisionally, national, regional, and global proportions of respective populations that have been infected with SARS-CoV-2 in the first year after the introduction of this virus into human circulation, and to assess infection morbidity and mortality rates, factoring both documented and undocumented infections. The estimates were generated by applying mathematical models to 159 countries and territories. The percentage of the world's population that has been infected as of 31 December 2020 was estimated at 12.56% (95% CI: 11.17–14.05%). It was lowest in the Western Pacific Region at 0.66% (95% CI: 0.59–0.75%) and highest in the Americas at 41.92% (95% CI: 37.95–46.09%). The global infection fatality rate was 10.73 (95% CI: 10.21–11.29) per 10,000 infections. Globally per 1000 infections, the infection acute-care bed hospitalization rate was 19.22 (95% CI: 18.73–19.51), the infection ICU bed hospitalization rate was 4.14 (95% CI: 4.10–4.18). If left unchecked with no vaccination and no other public health interventions, and assuming circulation of only wild-type variants and no variants of concern, the pandemic would eventually cause 8.18 million deaths (95% CI: 7.30–9.18), 163.67 million acute-care hospitalizations (95% CI: 148.12–179.51), and 33.01 million ICU hospitalizations (95% CI: 30.52–35.70), by the time the herd immunity threshold is reached at 60–70% infection exposure. The global population remained far below the herd immunity threshold by end of 2020. Global epidemiology reveals immense regional variation in infection exposure and morbidity and mortality rates.

Low Seroprevalence among Undetected COVID-19 Cases, Faroe Islands, November 2020

Article

Full-text available

Nov 2021
EMERG INFECT DIS

We conducted a second nationwide severe acute respiratory syndrome coronavirus 2 seroprevalence study in the Faroe Islands during November 2020. We found crude seroprevalence was 0.3% and prevalence was 0.4% after adjusting for test sensitivity and specificity. This low seroprevalence supports the prevention strategies used in the Faroe Islands.

SARS-COV-2 antibody seroprevalence in the general population of Oman: results from four successive nationwide seroepidemiological surveys

Article

Full-text available

Sep 2021
INT J INFECT DIS

Objective To assess SARS-COV-2 seroprevalence in Oman and longitudinal changes in antibody levels over time within the first 11 months of the COVID-19 pandemic. Methods This nationwide cross-sectional study was conducted as a four-cycle serosurvey using a multistage stratified sampling method from July–November 2020. A questionnaire was used and included demographics, history of acute respiratory infection and list of symptoms, COVID-19 contact, previous diagnosis or admission, travel history, and risk factors. Results In total, 17,457 participants were surveyed. Thirty percent were female, and 66.3% Omani. There was significant increase in seroprevalence throughout the study cycles, starting from [5.5 (4.8–6.2)] for the first cycle and ending with [22 (19.6–24.6)] for the forth cycle. There was no difference in seroprevalence between genders, but significant differences between age groups. There was a transition of seroprevalence from being higher in non-Omanis in cycle one, [9.1 (7.6–10.9)] to Omanis [3.2 (2.6–3.9)] to being higher in Omanis [24.3 (21.0–27.9)] to non-Omanis [16.8 (14.9–18.9)] in cycle four. There was remarkable variation in seroprevalence of COVID-19 according to governorate. Close contacts of people with COVID-19 had a 96% higher risk of having the disease, (adjusted odds ratio [AOR]=1.96, 95% confidence interval [CI] 1.64–2.34); laborers have 58% higher risk of infection compared to office workers (AOR=1.58, 95% CI; 1.04–2.35). Conclusion The study showed a wide variation of SARS-CoV-2 dissemination between governorates in Oman, with higher seroprevalence estimates in migrants in the first two cycles. Prevalence estimates remain low and are insufficient to provide herd immunity.

Seroprevalence of Anti-SARS-CoV-2 Antibodies in Senegal: A National Population-Based Cross-Sectional Survey, between October and November 2020

Article

Full-text available

Jan 2021

Risk and severity of SARS-CoV-2 reinfections during 2020−2022 in Vojvodina, Serbia: A population-level observational study

Article

Jul 2022

Background: Data on the rate and severity of SARS-CoV-2 reinfections in real-world settings are scarce and the effects of vaccine boosters on reinfection risk are unknown. Methods: In a population-level observational study, registered SARS-CoV-2 laboratory-confirmed Vojvodina residents, between March 6, 2020 and October 31, 2021, were followed for reinfection ≥90 days after primary infection. Data were censored at the end of follow-up (January 31, 2022) or death. The reinfection risk was visualized with Kaplan-Meier plots. To examine the protective effect of vaccination, the subset of individuals with primary infection in 2020 (March 6-December 31) were matched (1:2) with controls without reinfection. Findings: Until January 31, 2022, 13,792 reinfections were recorded among 251,104 COVID-19 primary infections (5.49%). Most reinfections (86.77%, 11,967/13,792) were recorded in January 2022. Reinfections were mostly mild (99.17%, 13,678/13,792). Hospitalizations were uncommon [1.08% (149/13,792) vs. 3.66% (505/13,792) in primary infection] and COVID-19 deaths were very rare (20/13,792, case fatality rate 0.15%). The overall incidence rate of reinfections was 5.99 (95% CI 5.89-6.09) per 1000 person-months. The reinfection risk was estimated as 0.76% at six months, 1.36% at nine months, 4.96% at 12 months, 16.68% at 15 months, and 18.86% at 18 months. Unvaccinated (OR=1.23; 95%CI=1.14-1.33), incompletely (OR=1.33; 95%CI=1.08-1.64) or completely vaccinated (OR=1.50; 95%CI=1.37-1.63), were modestly more likely to be reinfected compared with recipients of a third (booster) vaccine dose. Interpretation: SARS-CoV-2 reinfections were uncommon until the end of 2021 but became common with the advent of Omicron. Very few reinfections were severe. Boosters may modestly reduce reinfection risk. Funding: No specific funding was obtained for this study.

Trend in sensitivity of SARS-CoV-2 serology one year after mild and asymptomatic COVID-19: unpacking potential bias in seroprevalence studies

Article

Jan 2022
CLIN INFECT DIS

A key aim of serosurveillance during the COVID-19 pandemic has been to estimate the prevalence of prior infection, by correcting crude seroprevalence against estimated test performance for PCR-confirmed COVID-19. We show that poor generalisability of sensitivity estimates to some target populations may lead to substantial underestimation of case numbers.

Age-stratified infection fatality rate of COVID-19 in the non-elderly informed from pre-vaccination national seroprevalence studies

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