Evidence map›Paper›PMID 40534212›Full record

ArticleInternational journal of epidemiology2025

The potential bias introduced into COVID-19 vaccine effectiveness studies at primary care level due to the availability of SARS-CoV-2 tests in the general population.

Charlotte Lanièce Delaunay, Baltazar Nunes, Susana Monge, Marit de Lange, Gergő Túri, Ausenda Machado, Neus Latorre-Margalef, Ivan Mlinarić, Mihaela Lazar, Paloma Botella Rocamora and 21 more

Abstract read
In one paragraph

Article in International journal of epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

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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

31 authors.

Charlotte Lanièce DelaunayEpidemiology Department, Epiconcept, Paris, France.ORCID 0000-0002-7038-325X
Baltazar NunesEpidemiology Department, Epiconcept, Paris, France.
Susana MongeDepartment of Communicable Diseases, National Centre of Epidemiology, Institute of Health Carlos III, Madrid, Spain.
Marit de LangeCentre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
Gergő TúriNational Laboratory for Health Security, Epidemiology and Surveillance Centre, Semmelweis University, Budapest, Hungary.ORCID 0000-0003-3465-8321
Ausenda MachadoEpidemiology Department, National Health Institute Doutor Ricardo Jorge, Lisbon, Portugal.ORCID 0000-0002-1849-1499
Neus Latorre-MargalefDepartment of Microbiology, The Public Health Agency of Sweden, Stockholm, Sweden.
Ivan MlinarićDivision for Epidemiology of Communicable Diseases, Croatian Institute of Public Health, Zagreb, Croatia.ORCID 0009-0008-8546-6331
Mihaela LazarNational Influenza Centre, Cantacuzino National Military-Medical Institute for Research and Development, Bucharest, Romania.
Paloma Botella RocamoraSubdirecció General d'Epidemiologia i Vigilància de la Salut, Direcció General de Salut Pública, Generalitat Valenciana, Valencia, Spain.
Annika ErdwiensDepartment for Infectious Disease Epidemiology, Respiratory Infections Unit, Robert Koch Institute, Berlin, Germany.
Noémie SèveSorbonne Université, INSERM, Institut Pierre Louis d'épidémiologie et de Santé Publique (IPLESP), Paris, France.
Lisa DomeganHealth Service Executive-Health Protection Surveillance Centre, Dublin, Ireland.
Iván Martínez-BazInstituto de Salud Pública de Navarra-IdiSNA, Pamplona, Spain.
Mariëtte HooiveldNivel (Netherlands Institute for Health Services Research), Utrecht, The Netherlands.
Beatrix OrosziNational Laboratory for Health Security, Epidemiology and Surveillance Centre, Semmelweis University, Budapest, Hungary.
Raquel GuiomarReference Laboratory for Influenza and Other Respiratory Virus, National Institute of Health Doutor Ricardo Jorge, Lisbon, Portugal.ORCID 0000-0002-4563-6315
Maike SperkDepartment of Microbiology, The Public Health Agency of Sweden, Stockholm, Sweden.
Sanja Kurečić FilipovićDivision for Epidemiology of Communicable Diseases, Croatian Institute of Public Health, Zagreb, Croatia.
Catalina PascuNational Influenza Centre, Cantacuzino National Military-Medical Institute for Research and Development, Bucharest, Romania.
Juan Antonio Linares DopidoSubdirección de Epidemiología, Dirección General de Salud Pública, Servicio Extremeño de Salud, Mérida, Spain.
Ralf DürrwaldNational Reference Centre for Influenza, Robert Koch Institute, Berlin, Germany.
Marie-Anne Rameix-WeltiCentre National de Référence Virus des Infections Respiratoires (CNR VIR), M3P, Institut Pasteur, Université Paris Cité, Paris, France.
Adele McKennaHealth Service Executive-Health Protection Surveillance Centre, Dublin, Ireland.
Jesús CastillaInstituto de Salud Pública de Navarra-IdiSNA, Pamplona, Spain.ORCID 0000-0002-6396-7265
Cheyenne van HagenCentre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
Mirjam KnolCentre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
Sabrina BacciEuropean Centre for Disease Prevention and Control, Stockholm, Sweden.
Marlena KaczmarekEuropean Centre for Disease Prevention and Control, Stockholm, Sweden.
Esther KisslingEpidemiology Department, Epiconcept, Paris, France.
VEBIS Primary Care Vaccine Effectiveness Group

Funding

European Centre for Disease Prevention and Control
6 · The paper itself

Abstract

backgroundWith SARS-CoV-2 self-tests, persons with acute respiratory infections (ARI) can know their COVID-19 status. This may alter their decision to consult a general practitioner (GP), potentially biasing COVID-19 vaccine effectiveness (VE) studies. We explore bias mechanisms, simulate magnitude, and verify control methods.

methodsWe used directed acyclic graphs (DAGs) to illustrate the bias mechanisms. Based on the European primary care VEBIS multicentre test-negative design (TND) study, we simulated populations with varying true VE (20%-60%), proportions of persons with ARI self-testing (10%-30%), effect of COVID-19 vaccination on self-testing (1.5-2.5), and effect of self-test result on GP consultation (0.5-2). We performed 5000 runs per scenario, estimating VE among those consulting a GP. We calculated bias as true VE minus mean simulated VE, unadjusted and adjusted for self-testing, using logistic regression.

resultsDAGs suggested collider stratification bias if vaccination had an effect on self-testing and if self-test results affected GP consultation. Bias was -12% to 18% at 20% true VE, with the most extreme associations and 30% self-testing. With 60% true VE and 10%-20% self-testing, bias was lower. Bias was higher (-18% to 45%) if both positive and negative self-test results affected GP consultation. Adjusting for self-testing removed the bias.

conclusionsSelf-testing may bias COVID-19 VE TND studies in primary care if self-testing is high, particularly with low VE. We recommend primary care TND VE studies collect self-testing information to eliminate potential bias. Observational studies are needed to understand the relationship between vaccination, self-testing, and GP consultation, in these studies' source population.

Indexed as

COVID-19COVID-19 TestingCOVID-19 VaccinesPrimary Health CareVaccine EfficacyBiasFemaleHumansMaleSARS-CoV-2COVID-19 VaccinesbiasCOVID-19primary health careSARS-CoV-2self-testingvaccine effectiveness

Identifiers

PMID40534212
PMCPMC12204317

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.