Evidence map›Paper›PMID 37024810›Full record

ArticleBMC infectious diseases2023

Effect of risk status for severe COVID-19 on individual contact behaviour during the SARS-CoV-2 pandemic in 2020/2021-an analysis based on the German COVIMOD study.

Jasmin Walde, Madhav Chaturvedi, Tom Berger, Antonia Bartz, Robin Killewald, Damilola Victoria Tomori, Nicole Rübsamen, Berit Lange, Stefan Scholz, Marina Treskova and 5 more

Open access · goldAbstract read
In one paragraph

Article in BMC infectious diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.0field-weighted citation impact, top 15% 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

6 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
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  6. Contact behaviour before, during and after the COVID-19 pandemic in the Netherlands: evidence from contact surveys, 2016 to 2017 and 2020 to 2023.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2024
    Article
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

15 authors at 7 institutions in 2 countries.

Jasmin Walde *Institute of Epidemiology and Social Medicine, University of Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany.
Madhav Chaturvedi *Institute of Epidemiology and Social Medicine, University of Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany.
Tom BergerInstitute of Epidemiology and Social Medicine, University of Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany.
Antonia BartzInstitute of Epidemiology and Social Medicine, University of Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany.
Robin KillewaldInstitute of Epidemiology and Social Medicine, University of Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany.
Damilola Victoria TomoriInstitute of Epidemiology and Social Medicine, University of Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany.
Nicole RübsamenInstitute of Epidemiology and Social Medicine, University of Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany.
Berit LangeDepartment of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany.
Stefan ScholzImmunization Unit, Infectious Disease Epidemiology, Robert Koch-Institute, Berlin, Germany.
Marina TreskovaImmunization Unit, Infectious Disease Epidemiology, Robert Koch-Institute, Berlin, Germany.
Karolin BuckschInstitute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany.
Christopher I JarvisLondon School of Hygiene and Tropical Medicine, London, UK.
Rafael MikolajczykInstitute for Medical Epidemiology, Biometrics and Informatics, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle, Germany.
André KarchInstitute of Epidemiology and Social Medicine, University of Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany.
Veronika K JaegerInstitute of Epidemiology and Social Medicine, University of Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany. Veronika.Jaeger@ukmuenster.de.ORCID http://orcid.org/0000-0002-6913-0976
University of Münster · DEGerman Center for Infection Research · DEHeidelberg University · DELeipzig University · DELondon School of Hygiene & Tropical Medicine · GBMartin Luther University Halle-Wittenberg · DERobert Koch Institute · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOne of the primary aims of contact restriction measures during the SARS-CoV-2 pandemic has been to protect people at increased risk of severe disease from the virus. Knowledge about the uptake of contact restriction measures in this group is critical for public health decision-making. We analysed data from the German contact survey COVIMOD to assess differences in contact patterns based on risk status, and compared this to pre-pandemic data to establish whether there was a differential response to contact reduction measures.

methodsWe quantified differences in contact patterns according to risk status by fitting a generalised linear model accounting for within-participant clustering to contact data from 31 COVIMOD survey waves (April 2020-December 2021), and estimated the population-averaged ratio of mean contacts of persons with high risk for a severe COVID-19 outcome due to age or underlying health conditions, to those without. We then compared the results to pre-pandemic data from the contact surveys HaBIDS and POLYMOD.

resultsAveraged across all analysed waves, COVIMOD participants reported a mean of 3.21 (95% confidence interval (95%CI) 3.14,3.28) daily contacts (truncated at 100), compared to 18.10 (95%CI 17.12,19.06) in POLYMOD and 28.27 (95%CI 26.49,30.15) in HaBIDS. After adjusting for confounders, COVIMOD participants aged 65 or above had 0.83 times (95%CI 0.79,0.87) the number of contacts as younger age groups. In POLYMOD, this ratio was 0.36 (95%CI 0.30,0.43). There was no clear difference in contact patterns due to increased risk from underlying health conditions in either HaBIDS or COVIMOD. We also found that persons in COVIMOD at high risk due to old age increased their non-household contacts less than those not at such risk after strict restriction measures were lifted.

conclusionsOver the course of the SARS-CoV-2 pandemic, there was a general reduction in contact numbers in the German population and also a differential response to contact restriction measures based on risk status for severe COVID-19. This differential response needs to be taken into account for parametrisations of mathematical models in a pandemic setting.

Indexed as

COVID-19HumansPandemicsPublic HealthSARS-CoV-2Surveys and QuestionnairesContact behaviourRisk groupsSARS-CoV-2

Identifiers

PMID37024810
PMCPMC10078023
OpenAlexW4362666269

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.