Evidence map›Paper›PMID 38448400›Full record

ArticleNature communications2024

Comparing frequency of booster vaccination to prevent severe COVID-19 by risk group in the United States.

Hailey J Park, Gregg S Gonsalves, Sophia T Tan, J Daniel Kelly, George W Rutherford, Robert M Wachter, Robert Schechter, A David Paltiel, Nathan C Lo

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
14.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.

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  6. A simple and powerful test of vaccine waning.American journal of epidemiology · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 7 institutions in 1 country.

Hailey J ParkDivision of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.
Gregg S GonsalvesDepartment of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA.ORCID 0000-0002-5789-9841
Sophia T TanDivision of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.
J Daniel KellyDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.ORCID 0000-0002-7616-0321
George W RutherfordDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
Robert M WachterDepartment of Medicine, University of California, San Francisco, San Francisco, CA, USA.ORCID 0000-0003-3226-2737
Robert SchechterCalifornia Department of Public Health, Richmond, CA, USA.
A David PaltielDepartment of Health Policy and Management and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA.
Nathan C LoDivision of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA, USA. Nathan.Lo@stanford.edu.ORCID 0000-0002-5407-2005
Stanford University · USUniversity of California, San Francisco · USCalifornia Department of Public Health · USSan Francisco VA Medical Center · USStanford Medicine · USYale New Haven Health System · USYale University · US

Funding

Real-time predictive modeling for public health departments to control infectious diseasesDP2AI170485 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Nathan Lo · 2022 to 2026
$2.3M
VAccine failure: natural history and determinants of post-vaccination Covid-19I01CX002417 · VA · VETERANS AFFAIRS MED CTR SAN FRANCISCO · PI John Daniel Kelly, Salomeh Keyhani · 2022 to 2026
–
CSRD VA I01 CX002417NIAID NIH HHS DP2 AI170485NIAID NIH HHS L30 AI171975
6 · The paper itself

Abstract

There is a public health need to understand how different frequencies of COVID-19 booster vaccines may mitigate the risk of severe COVID-19, while accounting for waning of protection and differential risk by age and immune status. By analyzing United States COVID-19 surveillance and seroprevalence data in a microsimulation model, here we show that more frequent COVID-19 booster vaccination (every 6-12 months) in older age groups and the immunocompromised population would effectively reduce the burden of severe COVID-19, while frequent boosters in the younger population may only provide modest benefit against severe disease. In persons 75+ years, the model estimated that annual boosters would reduce absolute annual risk of severe COVID-19 by 199 (uncertainty interval: 183-232) cases per 100,000 persons, compared to a one-time booster vaccination. In contrast, for persons 18-49 years, the model estimated that annual boosters would reduce this risk by 14 (10-19) cases per 100,000 persons. Those with prior infection had lower benefit of more frequent boosting, and immunocompromised persons had larger benefit. Scenarios with emerging variants with immune evasion increased the benefit of more frequent variant-targeted boosters. This study underscores the benefit of considering key risk factors to inform frequency of COVID-19 booster vaccines in public health guidance and ensuring at least annual boosters in high-risk populations.

Indexed as

COVID-19AgedCOVID-19 VaccinesHumansRisk FactorsSeroepidemiologic StudiesVaccinationCOVID-19 Vaccines

Identifiers

PMID38448400
PMCPMC10917753
OpenAlexW4392508496

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.