Evidence map›Paper›PMID 36964146›Full record

SynthesisNature communications2023

Predicting vaccine effectiveness against severe COVID-19 over time and against variants: a meta-analysis.

Deborah Cromer, Megan Steain, Arnold Reynaldi, Timothy E Schlub, Shanchita R Khan, Sarah C Sasson, Stephen J Kent, David S Khoury, Miles P Davenport

Open access · goldFull text readMeta-AnalysisSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
66citing papers in PubMed, 3 pooled it
23.1field-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

66 citing papers in PubMed, 3 syntheses or guidelines pooled it, 120 citations in OpenAlex.

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  8. Stochastic interventional approach to assessing immune correlates of protection: Application to the COVE messenger RNA-1273 vaccine trial.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2023
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6 more citing papers are in PubMed but not listed here.

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 3 institutions in 1 country.

Deborah CromerKirby Institute, University of New South Wales, Sydney, Australia. d.cromer@unsw.edu.au.ORCID 0000-0002-5276-5094
Megan SteainSydney Institute of Infectious Diseases and Charles Perkins Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
Arnold ReynaldiKirby Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0002-5529-5542
Timothy E SchlubKirby Institute, University of New South Wales, Sydney, Australia.
Shanchita R KhanKirby Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0003-0772-6122
Sarah C SassonKirby Institute, University of New South Wales, Sydney, Australia.
Stephen J KentDepartment of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia.ORCID 0000-0002-8539-4891
David S KhouryKirby Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0002-2663-1551
Miles P DavenportKirby Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0002-4751-1831
UNSW Sydney · AUThe University of Sydney · AUThe University of Melbourne · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Vaccine protection from symptomatic SARS-CoV-2 infection has been shown to be strongly correlated with neutralising antibody titres; however, this has not yet been demonstrated for severe COVID-19. To explore whether this relationship also holds for severe COVID-19, we performed a systematic search for studies reporting on protection against different SARS-CoV-2 clinical endpoints and extracted data from 15 studies. Since matched neutralising antibody titres were not available, we used the vaccine regimen, time since vaccination and variant of concern to predict corresponding neutralising antibody titres. We then compared the observed vaccine effectiveness reported in these studies to the protection predicted by a previously published model of the relationship between neutralising antibody titre and vaccine effectiveness against severe COVID-19. We find that predicted neutralising antibody titres are strongly correlated with observed vaccine effectiveness against symptomatic (Spearman [Formula: see text] = 0.95, p < 0.001) and severe (Spearman [Formula: see text] = 0.72, p < 0.001 for both) COVID-19 and that the loss of neutralising antibodies over time and to new variants are strongly predictive of observed vaccine protection against severe COVID-19.

Indexed as

COVID-19Antibodies, NeutralizingAntibodies, ViralHumansSARS-CoV-2VaccinationVaccine EfficacyAntibodies, NeutralizingAntibodies, Viral

Identifiers

PMID36964146
PMCPMC10036966
OpenAlexW4360871696

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read61
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.