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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Vienna
2022
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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 |
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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 |
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