Cargando…

Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna

BACKGROUND: Most clinical studies report the symptoms experienced by those infected with coronavirus disease 2019 (COVID-19) via patients already hospitalized. Here we analyzed the symptoms experienced outside of a hospital setting. METHODS: The Vienna Social Fund (FSW; Vienna, Austria), the Public...

Descripción completa

Detalles Bibliográficos
Autores principales: Munsch, Nicolas, Gruarin, Stefanie, Nateqi, Jama, Lutz, Thomas, Binder, Michael, Aberle, Judith H., Martin, Alistair, Knapp, Bernhard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9007045/
https://www.ncbi.nlm.nih.gov/pubmed/35416543
http://dx.doi.org/10.1007/s00508-022-02028-9
_version_ 1784686780600025088
author Munsch, Nicolas
Gruarin, Stefanie
Nateqi, Jama
Lutz, Thomas
Binder, Michael
Aberle, Judith H.
Martin, Alistair
Knapp, Bernhard
author_facet Munsch, Nicolas
Gruarin, Stefanie
Nateqi, Jama
Lutz, Thomas
Binder, Michael
Aberle, Judith H.
Martin, Alistair
Knapp, Bernhard
author_sort Munsch, Nicolas
collection PubMed
description BACKGROUND: Most clinical studies report the symptoms experienced by those infected with coronavirus disease 2019 (COVID-19) via patients already hospitalized. Here we analyzed the symptoms experienced outside of a hospital setting. METHODS: The Vienna Social Fund (FSW; Vienna, Austria), the Public Health Services of the City of Vienna (MA15) and the private company Symptoma collaborated to implement Vienna’s official online COVID-19 symptom checker. Users answered 12 yes/no questions about symptoms to assess their risk for COVID-19. They could also specify their age and sex, and whether they had contact with someone who tested positive for COVID-19. Depending on the assessed risk of COVID-19 positivity, a SARS-CoV‑2 nucleic acid amplification test (NAAT) was performed. In this publication, we analyzed which factors (symptoms, sex or age) are associated with COVID-19 positivity. We also trained a classifier to correctly predict COVID-19 positivity from the collected data. RESULTS: Between 2 November 2020 and 18 November 2021, 9133 people experiencing COVID-19-like symptoms were assessed as high risk by the chatbot and were subsequently tested by a NAAT. Symptoms significantly associated with a positive COVID-19 test were malaise, fatigue, headache, cough, fever, dysgeusia and hyposmia. Our classifier could successfully predict COVID-19 positivity with an area under the curve (AUC) of 0.74. CONCLUSION: This study provides reliable COVID-19 symptom statistics based on the general population verified by NAATs. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s00508-022-02028-9) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-9007045
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Springer Vienna
record_format MEDLINE/PubMed
spelling pubmed-90070452022-04-14 Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna Munsch, Nicolas Gruarin, Stefanie Nateqi, Jama Lutz, Thomas Binder, Michael Aberle, Judith H. Martin, Alistair Knapp, Bernhard Wien Klin Wochenschr Original Article BACKGROUND: Most clinical studies report the symptoms experienced by those infected with coronavirus disease 2019 (COVID-19) via patients already hospitalized. Here we analyzed the symptoms experienced outside of a hospital setting. METHODS: The Vienna Social Fund (FSW; Vienna, Austria), the Public Health Services of the City of Vienna (MA15) and the private company Symptoma collaborated to implement Vienna’s official online COVID-19 symptom checker. Users answered 12 yes/no questions about symptoms to assess their risk for COVID-19. They could also specify their age and sex, and whether they had contact with someone who tested positive for COVID-19. Depending on the assessed risk of COVID-19 positivity, a SARS-CoV‑2 nucleic acid amplification test (NAAT) was performed. In this publication, we analyzed which factors (symptoms, sex or age) are associated with COVID-19 positivity. We also trained a classifier to correctly predict COVID-19 positivity from the collected data. RESULTS: Between 2 November 2020 and 18 November 2021, 9133 people experiencing COVID-19-like symptoms were assessed as high risk by the chatbot and were subsequently tested by a NAAT. Symptoms significantly associated with a positive COVID-19 test were malaise, fatigue, headache, cough, fever, dysgeusia and hyposmia. Our classifier could successfully predict COVID-19 positivity with an area under the curve (AUC) of 0.74. CONCLUSION: This study provides reliable COVID-19 symptom statistics based on the general population verified by NAATs. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s00508-022-02028-9) contains supplementary material, which is available to authorized users. Springer Vienna 2022-04-13 2022 /pmc/articles/PMC9007045/ /pubmed/35416543 http://dx.doi.org/10.1007/s00508-022-02028-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Munsch, Nicolas
Gruarin, Stefanie
Nateqi, Jama
Lutz, Thomas
Binder, Michael
Aberle, Judith H.
Martin, Alistair
Knapp, Bernhard
Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna
title Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna
title_full Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna
title_fullStr Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna
title_full_unstemmed Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna
title_short Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna
title_sort symptoms associated with a covid-19 infection among a non-hospitalized cohort in vienna
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9007045/
https://www.ncbi.nlm.nih.gov/pubmed/35416543
http://dx.doi.org/10.1007/s00508-022-02028-9
work_keys_str_mv AT munschnicolas symptomsassociatedwithacovid19infectionamonganonhospitalizedcohortinvienna
AT gruarinstefanie symptomsassociatedwithacovid19infectionamonganonhospitalizedcohortinvienna
AT nateqijama symptomsassociatedwithacovid19infectionamonganonhospitalizedcohortinvienna
AT lutzthomas symptomsassociatedwithacovid19infectionamonganonhospitalizedcohortinvienna
AT bindermichael symptomsassociatedwithacovid19infectionamonganonhospitalizedcohortinvienna
AT aberlejudithh symptomsassociatedwithacovid19infectionamonganonhospitalizedcohortinvienna
AT martinalistair symptomsassociatedwithacovid19infectionamonganonhospitalizedcohortinvienna
AT knappbernhard symptomsassociatedwithacovid19infectionamonganonhospitalizedcohortinvienna