Evidence map›Paper›PMID 36575771›Full record

ArticleInfection prevention in practice2023

Semi-automated contact tracing and management of contact precautions during the COVID-19 pandemic within a tertiary hospital.

Lukas Bechmann, Gernot Geginat

Abstract read
In one paragraph

Article in Infection prevention in practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
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

2 authors.

Lukas BechmannDepartment of Medical Microbiology and Infection Control, Otto-von-Guericke University Magdeburg, Germany.
Gernot GeginatDepartment of Medical Microbiology and Infection Control, Otto-von-Guericke University Magdeburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Evaluation of a spreadsheet-based COVID-19 contact-tracing tool (CTT) and determination of risk factors for SARS-CoV-2 transmission among hospital staff members. Design: Observational descriptive study on the application and acceptance of the CTT. Retrospective case-control study for SARS-CoV-2 transmission risk factor determination and for evaluation of the CTT's risk stratification algorithm. Participants: 3514 contacts of hospital staff members to 322 SARS-CoV-2-positive cases. Methods: A case-control study was performed to identify risk factors for SARS-CoV-2 transmission and for unprotected contacts among staff members. To evaluate strengths and weaknesses of the CTT performance statistics were analyzed and users completed a questionnaire measuring satisfaction and acceptance of the tool. Results: In 2021, the CTT was used for the algorithm-based semi-automated management of 3514 in-hospital contacts. The tool determined the risk category of individual contacts and generated messages for the information of the local public health department, the in-hospital SARS-CoV-2 test center and all staff members who had contact to the index case. Staff members without regular contacts to patients had significantly ( Conclusions: Digital tools can increase the efficiency of in-hospital contact tracing. The CTT enable a timely systematic analysis of risk factors among staff members.

Indexed as

Contact tracingCovid-19HospitalRisk factorToolTransmission

Identifiers

PMID36575771
PMCPMC9780020

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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.