Evidence map›Paper›PMID 40624510›Full record

ArticleBMC medicine2025

Dynamics of contact behaviour by self-reported COVID-19 vaccination and infection status during the COVID-19 pandemic in Germany: an analysis of two large population-based studies.

Lena Böff, Antonia Bartz, Manuela Harries, MuSPAD Consortium Group, COVIMOD Consortium Group, RESPINOW Consortium Group, André Karch, Annette Aigner, Veronika K Jaeger, Berit Lange

Abstract read
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Article in BMC medicine, 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

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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

10 authors.

Lena Böff *Department of Epidemiology, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany.
Antonia Bartz *Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.
Manuela HarriesDepartment of Epidemiology, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany.
MuSPAD Consortium Group
COVIMOD Consortium Group
RESPINOW Consortium Group
André KarchInstitute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.
Annette Aigner *Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Biometry and Clinical Epidemiology, Berlin, Germany.
Veronika K Jaeger *Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany. veronika.jaeger@ukmuenster.de.
Berit Lange *Department of Epidemiology, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany. berit.lange@helmholtz-hzi.de.

Funding

Bundesministerium für Bildung und Forschung 01KX2021Bundesministerium für Bildung und Forschung 01KX2121Bundesministerium für Bildung und Forschung MV2021-012Deutsche Forschungsgemeinschaft 458526380European Union's Horizon 2020 research and innovation program 101003480; 10107382; 101095606Helmholtz Association SO-096HZEPiAdHoc & LOKI: KA1-Co-08
6 · The paper itself

Abstract

backgroundContact behaviour is crucial to assess and predict transmission of respiratory pathogens like SARS-CoV-2. Contact behaviour has traditionally been assessed in cross-sectional surveys and not as part of longitudinal population-based studies which simultaneously measure infection frequency and vaccination coverage. During the COVID-19 pandemic, several studies assessed contact behaviour over longer periods and correlated this to data on immunity. This can inform future dynamic modelling. Here, we assess how contact behaviour varied based on SARS-CoV-2 infection or vaccination status in two large population-based studies in Germany during 2021.

methodsWe assessed direct encounters, separated into household and non-household contacts, in participants of MuSPAD (n = 12,641), a population-based cohort study, and COVIMOD (n = 31,260), a longitudinal contact survey. We calculated mean numbers of reported contacts and fitted negative binomial mixed-effects models to estimate the impact of immunity status, defined by vaccination or previous infection, on contact numbers; logistic mixed-effects models were used to examine the relationship between contact behaviour and seropositivity due to infection.

resultsContact numbers varied over the course of the pandemic from 7.6 to 10.8 per 24 h in MuSPAD and 2.1 to 3.1 per 24 h in COVIMOD. The number of non-household contacts was higher in participants who reported previous infections and vaccinations (contact ratio (CR) MuSPAD: 1.22 (95%CI 0.94-1.60); COVIMOD: 1.35 (CI 1.12-1.62)) compared to unvaccinated and uninfected individuals. Non-household contact numbers were also higher in fully vaccinated participants (MUSPAD: CR 1.15 (CI 1.05-1.26); COVIMOD: 1.43 (CI 1.32-1.56)) compared to unvaccinated individuals. Compared to individuals without household contacts, the odds for seropositivity due to infection were higher among MuSPAD individuals with three or more household contacts (odds ratio (OR) 1.54 (CI 1.12-2.13)) and eleven or more non-household contacts (OR 1.29 (CI 1.01-1.65)).

conclusionsDifferent contact behaviours based on infection and/or vaccination status suggest that public health policies targeting immunity status may influence the contact behaviour of those affected. A combined assessment of self-reported contacts, infections, and vaccinations as well as laboratory-confirmed serostatus in the population can support modelling of the spread of infections. This could help target containment policies and evaluate the impact of public health measures.

Indexed as

Contact TracingCOVID-19COVID-19 VaccinesVaccinationAdolescentAdultAgedFemaleGermanyHumansLongitudinal StudiesMaleMiddle AgedPandemicsSARS-CoV-2Self ReportCOVID-19 VaccinesCovid-19Immunity statusPandemicPrevious infectionsSARS-CoV-2Seroprevalence studySocial contact behaviourSocial contact surveyVaccination status

Identifiers

PMID40624510
PMCPMC12235905

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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.