Evidence map›Paper›PMID 40979863›Full record

ArticleGMS hygiene and infection control2025

Prevalence and predictors of post-COVID-19-related symptoms: an extended follow-up among employees in health and welfare services in Germany: a short report.

Peter Koch, Lara Steinke, Claudia Peters, Albert Nienhaus

Abstract read
In one paragraph

Article in GMS hygiene and infection control, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. 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

4 authors.

Peter KochCompetence Center for Epidemiology and Health Services Research for Healthcare Professionals (CVcare), Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.
Lara SteinkeUniversity of Lübeck, Lübeck, Germany.
Claudia PetersCompetence Center for Epidemiology and Health Services Research for Healthcare Professionals (CVcare), Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.
Albert NienhausDepartment for Occupational Medicine, Hazardous Substances and Health Sciences (AGG), Institution for Statutory Accident Insurance in the Health and Welfare Services (BGW), Hamburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To describe persistent symptoms after a work-related COVID-19 infection in health and welfare workers and the identification of predictors of these symptoms. Methods: This short report summarises updated results on a bidirectional cohort study of employees in the health and welfare services who had reported a work-related SARS-CoV-2 infection in 2020. Participants were interviewed for the fourth time (T4) in April 2023 using a paper-and-pencil questionnaire. In this extended follow-up study (total prospective follow-up time: 26 months, maximum observation time 32 months), questions were asked about the type and severity of persistent symptoms. Kaplan-Meier curves were used to visualize cumulative survival rates, and Cox regression was used to identify predictors. Results: Of the 2,053 participants in the baseline study (response rate: 47%), 1,075 people took part in the 4 Conclusion: Minimal further recovery was observed in this cohort of insured persons after a 26-month prospective follow-up. Identified risk factors for persistent symptoms, i.e. female gender, older age, severe acute symptoms, and pre-existing illnesses, define a high-risk group of individuals, who should receive sufficient attention in the early phase of their COVID-19 disease and receive appropriate therapy to minimize the risk of post-COVID-19 syndrome.

Indexed as

COVID-19follow-uphealth personnelpersistent symptomspost-acute COVID-19 syndromepost-COVID-19 syndromesocial workerstime to symptom-free

Identifiers

PMID40979863
PMCPMC12447766

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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.