The University of Southampton
University of Southampton Institutional Repository

Predictors of prolonged symptoms following COVID-19 and other respiratory infections

Predictors of prolonged symptoms following COVID-19 and other respiratory infections
Predictors of prolonged symptoms following COVID-19 and other respiratory infections
Background: post-viral syndromes following respiratory tract infections have been described for years. Early studies of COVID-19 have suggested that around 2% of people who develop acute infection will still have symptoms at 12 weeks. Understanding predictors of prolonged symptoms may help inform early management and healthcare planning.

Aim: to explore predictors of prolonged symptoms in a community cohort reporting acute respiratory tract infection during the first year of the COVID-19 pandemic.Methods:We conducted an online survey of adults through advertising to the public and invitations sent by general practices. Participants were asked to report details of any respiratory infections lasting 3 days or more and asked to report any prolonged symptoms in a follow-up survey sent 3-months later. Clustering of prolonged symptoms was explored using factor analysis. Demographics, past medical history, and features of the acute illness were all considered as potential predictors. We used LASSO to select predictors and then logistic regression to estimate the association with experiencing prolonged symptoms.

Results: 1,942 participants reported an ARI in the baseline questionnaire and completed a 3-month follow-upquestionnaire. Of these, 464 (23.9%) reported having prolonged symptoms. The most common prolonged symptoms were tiredness, ‘brain fog’ and shortness of breath. Preliminary analysis has identified having laboratory confirmed or ‘probable’ COVID, older age, female sex, greater socioeconomic deprivation, greater concern about the initial illness, and shortness of breath, loss of taste and skin rash, as predictors of prolonged symptoms.

Discussion: the analysis is ongoing and will be presented at the meeting.
Francis, N.A.
9b610883-605c-4fee-871d-defaa86ccf8e
Willcox, Merlin
dad5b622-9ac2-417d-9b2e-aad41b64ffea
Becque, Taeko
ecd1b4d5-4db8-4442-81c2-04aa291cf2fd
Little, Paul
1bf2d1f7-200c-47a5-ab16-fe5a8756a777
Alwan, Nisreen
0d37b320-f325-4ed3-ba51-0fe2866d5382
Hay, Alastair
daf3c71f-5e5a-4bee-9bb8-7df67ec2a79f
Stuart, Beth
626862fc-892b-4f6d-9cbb-7a8d7172b209
Moore, Michael
1be81dad-7120-45f0-bbed-f3b0cc0cfe99
Francis, N.A.
9b610883-605c-4fee-871d-defaa86ccf8e
Willcox, Merlin
dad5b622-9ac2-417d-9b2e-aad41b64ffea
Becque, Taeko
ecd1b4d5-4db8-4442-81c2-04aa291cf2fd
Little, Paul
1bf2d1f7-200c-47a5-ab16-fe5a8756a777
Alwan, Nisreen
0d37b320-f325-4ed3-ba51-0fe2866d5382
Hay, Alastair
daf3c71f-5e5a-4bee-9bb8-7df67ec2a79f
Stuart, Beth
626862fc-892b-4f6d-9cbb-7a8d7172b209
Moore, Michael
1be81dad-7120-45f0-bbed-f3b0cc0cfe99

Francis, N.A., Willcox, Merlin, Becque, Taeko, Little, Paul, Alwan, Nisreen, Hay, Alastair, Stuart, Beth and Moore, Michael (2022) Predictors of prolonged symptoms following COVID-19 and other respiratory infections. General Practice Resarch on Infections Network, University of Lund, Lund, Sweden. 23 - 24 Sep 2022.

Record type: Conference or Workshop Item (Paper)

Abstract

Background: post-viral syndromes following respiratory tract infections have been described for years. Early studies of COVID-19 have suggested that around 2% of people who develop acute infection will still have symptoms at 12 weeks. Understanding predictors of prolonged symptoms may help inform early management and healthcare planning.

Aim: to explore predictors of prolonged symptoms in a community cohort reporting acute respiratory tract infection during the first year of the COVID-19 pandemic.Methods:We conducted an online survey of adults through advertising to the public and invitations sent by general practices. Participants were asked to report details of any respiratory infections lasting 3 days or more and asked to report any prolonged symptoms in a follow-up survey sent 3-months later. Clustering of prolonged symptoms was explored using factor analysis. Demographics, past medical history, and features of the acute illness were all considered as potential predictors. We used LASSO to select predictors and then logistic regression to estimate the association with experiencing prolonged symptoms.

Results: 1,942 participants reported an ARI in the baseline questionnaire and completed a 3-month follow-upquestionnaire. Of these, 464 (23.9%) reported having prolonged symptoms. The most common prolonged symptoms were tiredness, ‘brain fog’ and shortness of breath. Preliminary analysis has identified having laboratory confirmed or ‘probable’ COVID, older age, female sex, greater socioeconomic deprivation, greater concern about the initial illness, and shortness of breath, loss of taste and skin rash, as predictors of prolonged symptoms.

Discussion: the analysis is ongoing and will be presented at the meeting.

This record has no associated files available for download.

More information

Published date: 23 September 2022
Venue - Dates: General Practice Resarch on Infections Network, University of Lund, Lund, Sweden, 2022-09-23 - 2022-09-24

Identifiers

Local EPrints ID: 475732
URI: http://eprints.soton.ac.uk/id/eprint/475732
PURE UUID: 90823119-9bb7-4f39-96a7-8916be24d429
ORCID for N.A. Francis: ORCID iD orcid.org/0000-0001-8939-7312
ORCID for Merlin Willcox: ORCID iD orcid.org/0000-0002-5227-3444
ORCID for Taeko Becque: ORCID iD orcid.org/0000-0002-0362-3794
ORCID for Nisreen Alwan: ORCID iD orcid.org/0000-0002-4134-8463
ORCID for Beth Stuart: ORCID iD orcid.org/0000-0001-5432-7437
ORCID for Michael Moore: ORCID iD orcid.org/0000-0002-5127-4509

Catalogue record

Date deposited: 27 Mar 2023 16:39
Last modified: 28 Mar 2023 01:58

Export record

Contributors

Author: N.A. Francis ORCID iD
Author: Merlin Willcox ORCID iD
Author: Taeko Becque ORCID iD
Author: Paul Little
Author: Nisreen Alwan ORCID iD
Author: Alastair Hay
Author: Beth Stuart ORCID iD
Author: Michael Moore ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×