Patient symptoms and experience following COVID-19: results from a UK-wide survey

  1. Sara Buttery
  2. Keir E J Philip
  3. Parris Williams
  4. Andrea Fallas
  5. Brigitte West
  6. Andrew Cumella
  7. Cheryl Cheung
  8. Samantha Walker
  9. Jennifer K Quint
  10. Michael I Polkey
  11. Nicholas S Hopkinson

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Abstract

To investigate the experience of people who continue to be unwell after acute COVID-19, often referred to as ‘long COVID’, both in terms of their symptoms and their interactions with healthcare.

Design

We conducted a mixed-methods analysis of responses to a survey accessed through a UK online post-COVID-19 support and information hub, between April and December 2020, about people’s experiences after having acute COVID-19.

Participants

3290 respondents, 78% female, 92.1% white ethnicity and median age range 45–54 years; 12.7% had been hospitalised. 494(16.5%) completed the survey between 4 and 8 weeks of the onset of their symptoms, 641(21.4%) between 8 and 12 weeks and 1865 (62.1%) >12 weeks after.

Results

The ongoing symptoms most frequently reported were: breathing problems (92.1%), fatigue (83.3%), muscle weakness or joint stiffness (50.6%), sleep disturbances (46.2%), problems with mental abilities (45.9%), changes in mood, including anxiety and depression (43.1%) and cough (42.3%). Symptoms did not appear to be related to the severity of the acute illness or to the presence of pre-existing medical conditions. Analysis of free-text responses revealed three main themes: (1) experience of living with COVID-19: physical and psychological symptoms that fluctuate unpredictably; (2) interactions with healthcare that were unsatisfactory; (3) implications for the future: their own condition, society and the healthcare system, and the need for research

Conclusion

Consideration of patient perspectives and experiences will assist in the planning of services to address problems persisting in people who remain symptomatic after the acute phase of COVID-19.

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  1. Version published to 10.1136/bmjresp-2021-001075
  2. ScreenIT

    SciScore for 10.1101/2021.04.15.21255348: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsConsent: Participants were required to give informed consent for the use of their data for this purpose at the start of the survey.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data analysis: Statistical analyses were performed using SPSS, version 27.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Some limitations should be noted. Firstly, although a large sample, it is unclear how representative it is of all people with long-COVID, as our sample has high proportions of women and white people. However, emerging research suggests that females may be at an increased risk of developing long-COVID (14). Although the prevalence of asthma was 26% in our cohort, this was similar to that seen in the ISARIC dataset; 21% for 16 to 49 year olds(32). As the population are self-selecting, based on a decision to visit an online post-COVID resource,, caution should be applied to inferences regarding the factors that may predict the development of long-COVID in any one individual. A degree of sampling bias towards those with higher levels of digital literacy, and individuals with more severe ongoing symptoms may have occurred, as such individuals could plausibly be more likely to be seeking online sources of support such as through the post-COVID hub.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.04.15.21255348v1 on medRxiv