Evidence map›Paper›PMID 32605549›Full record

ArticleBMC public health2020

Protocol of a population-based prospective COVID-19 cohort study Munich, Germany (KoCo19).

Katja Radon, Elmar Saathoff, Michael Pritsch, Jessica Michelle Guggenbühl Noller, Inge Kroidl, Laura Olbrich, Verena Thiel, Max Diefenbach, Friedrich Riess, Felix Forster and 4 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in BMC public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
1.5field-weighted citation impact, top 9% 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

33 citing papers in PubMed, 64 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 3 institutions in 1 country.

Katja RadonInstitute and Outpatient Clinic for Occupational, Social and Environmental Medicine, Ludwig-Maximilians-Universität (LMU) University Hospital Munich, Ziemssenstr. 1, 80336, Munich, Germany. katja.radon@med.lmu.de.ORCID http://orcid.org/0000-0002-5271-3972
Elmar SaathoffDivision of Infectious Diseases and Tropical Medicine, LMU University Hospital Munich, Germany, Leopoldstrasse 5, 80802, Munich, Germany.
Michael PritschDivision of Infectious Diseases and Tropical Medicine, LMU University Hospital Munich, Germany, Leopoldstrasse 5, 80802, Munich, Germany.
Jessica Michelle Guggenbühl NollerDivision of Infectious Diseases and Tropical Medicine, LMU University Hospital Munich, Germany, Leopoldstrasse 5, 80802, Munich, Germany.
Inge KroidlDivision of Infectious Diseases and Tropical Medicine, LMU University Hospital Munich, Germany, Leopoldstrasse 5, 80802, Munich, Germany.
Laura OlbrichDivision of Infectious Diseases and Tropical Medicine, LMU University Hospital Munich, Germany, Leopoldstrasse 5, 80802, Munich, Germany.
Verena ThielDivision of Infectious Diseases and Tropical Medicine, LMU University Hospital Munich, Germany, Leopoldstrasse 5, 80802, Munich, Germany.
Max DiefenbachDivision of Infectious Diseases and Tropical Medicine, LMU University Hospital Munich, Germany, Leopoldstrasse 5, 80802, Munich, Germany.
Friedrich RiessDivision of Infectious Diseases and Tropical Medicine, LMU University Hospital Munich, Germany, Leopoldstrasse 5, 80802, Munich, Germany.
Felix ForsterInstitute and Outpatient Clinic for Occupational, Social and Environmental Medicine, Ludwig-Maximilians-Universität (LMU) University Hospital Munich, Ziemssenstr. 1, 80336, Munich, Germany.
Fabian TheisHelmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.
Andreas WieserDivision of Infectious Diseases and Tropical Medicine, LMU University Hospital Munich, Germany, Leopoldstrasse 5, 80802, Munich, Germany.
Michael HoelscherCenter for International Health, LMU University Hospital, Munich, Germany.
KoCo19 collaboration group
LMU Klinikum · DEHelmholtz Zentrum München · DELudwig-Maximilians-Universität München · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDue to the SARS-CoV-2 pandemic, public health interventions have been introduced globally in order to prevent the spread of the virus and avoid the overload of health care systems, especially for the most severely affected patients. Scientific studies to date have focused primarily on describing the clinical course of patients, identifying treatment options and developing vaccines. In Germany, as in many other regions, current tests for SARS-CoV2 are not conducted on a representative basis and in a longitudinal design. Furthermore, knowledge about the immune status of the population is lacking. Nonetheless, these data are needed to understand the dynamics of the pandemic and hence to appropriately design and evaluate interventions. For this purpose, we recently started a prospective population-based cohort in Munich, Germany, with the aim to develop a better understanding of the state and dynamics of the pandemic.

methodsIn 100 out of 755 randomly selected constituencies, 3000 Munich households are identified via random route and offered enrollment into the study. All household members are asked to complete a baseline questionnaire and subjects ≥14 years of age are asked to provide a venous blood sample of ≤3 ml for the determination of SARS-CoV-2 IgG/IgA status. The residual plasma and the blood pellet are preserved for later genetic and molecular biological investigations. For twelve months, each household member is asked to keep a diary of daily symptoms, whereabouts and contacts via WebApp. If symptoms suggestive for COVID-19 are reported, family members, including children < 14 years, are offered a pharyngeal swab taken at the Division of Infectious Diseases and Tropical Medicine, LMU University Hospital Munich, for molecular testing for SARS-CoV-2. In case of severe symptoms, participants will be transferred to a Munich hospital. For one year, the study teams re-visits the households for blood sampling every six weeks. DISCUSSION: With the planned study we will establish a reliable epidemiological tool to improve the understanding of the spread of SARS-CoV-2 and to better assess the effectiveness of public health measures as well as their socio-economic effects. This will support policy makers in managing the epidemic based on scientific evidence.

Indexed as

Coronavirus InfectionsCOVID-19GermanyHumansPandemicsPneumonia, ViralProspective StudiesResearch DesignCoronavirus infections/epidemiologyCOVID-19Enzyme-linked Immunosorbent assayGeographic information systemsModels, economicPandemicsPanel studySocio-economic factorsSpatial analysisStress, psychological

Identifiers

PMID32605549
PMCPMC7324773
OpenAlexW3038970457

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.