Characteristics and impact of Long Covid: Findings from an online survey

  1. Nida Ziauddeen
  2. Deepti Gurdasani
  3. Margaret E. O’Hara
  4. Claire Hastie
  5. Paul Roderick
  6. Guiqing Yao
  7. Nisreen A. Alwan

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Abstract

Long Covid is a public health concern that needs defining, quantifying, and describing. We aimed to explore the initial and ongoing symptoms of Long Covid following SARS-CoV-2 infection and describe its impact on daily life.

Methods

We collected self-reported data through an online survey using convenience non-probability sampling. The survey enrolled adults who reported lab-confirmed (PCR or antibody) or suspected COVID-19 who were not hospitalised in the first two weeks of illness. This analysis was restricted to those with self-reported Long Covid. Univariate comparisons between those with and without confirmed COVID-19 infection were carried out and agglomerative hierarchical clustering was used to identify specific symptom clusters, and their demographic and functional correlates.

Results

We analysed data from 2550 participants with a median duration of illness of 7.6 months (interquartile range (IQR) 7.1–7.9). 26.5% reported lab-confirmation of infection. The mean age was 46.5 years (standard deviation 11 years) with 82.8% females and 79.9% of participants based in the UK. 89.5% described their health as good, very good or excellent before COVID-19. The most common initial symptoms that persisted were exhaustion, chest pressure/tightness, shortness of breath and headache. Cognitive dysfunction and palpitations became more prevalent later in the illness. Most participants described fluctuating (57.7%) or relapsing symptoms (17.6%). Physical activity, stress, and sleep disturbance commonly triggered symptoms. A third (32%) reported they were unable to live alone without any assistance at six weeks from start of illness. 16.9% reported being unable to work solely due to COVID-19 illness. 37.0% reported loss of income due to illness, and 64.4% said they were unable to perform usual activities/duties. Acute systems clustered broadly into two groups: a majority cluster (n = 2235, 88%) with cardiopulmonary predominant symptoms, and a minority cluster (n = 305, 12%) with multisystem symptoms. Similarly, ongoing symptoms broadly clustered in two groups; a majority cluster (n = 2243, 88.8%) exhibiting mainly cardiopulmonary, cognitive symptoms and exhaustion, and a minority cluster (n = 283, 11.2%) exhibiting more multisystem symptoms. Belonging to the more severe multisystem cluster was associated with more severe functional impact, lower income, younger age, being female, worse baseline health, and inadequate rest in the first two weeks of the illness, with no major differences in the cluster patterns when restricting analysis to the lab-confirmed subgroup.

Conclusion

This is an exploratory survey of Long Covid characteristics. Whilst this is a non-representative population sample, it highlights the heterogeneity of persistent symptoms, and the significant functional impact of prolonged illness following confirmed or suspected SARS-CoV-2 infection. To study prevalence, predictors and prognosis, research is needed in a representative population sample using standardised case definitions.

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  1. Version published to 10.1371/journal.pone.0264331
  2. ScreenIT

    SciScore for 10.1101/2021.03.21.21253968: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Participants gave their consent by answering ‘yes’ to a consent question.
    IRB: Ethical approval for the study was granted by the University of Southampton, Faculty of Medicine Ethics Committee (Reference 61434).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    R packages used included readstata13, mclust, stats, and ggplot2.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    We carried out hierarchical agglomerative clustering using hclust implemented in the R package stats using the complete method of clustering.
    hclust
    suggested: (HCLUST, RRID:SCR_009154)

    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:
    Limitations: This is a non-representative survey which recruited through online support groups as well as generally through social media. The survey sampling method was convenience non-probability sampling. This means that the sample was not randomly drawn from the population of interest to ensure representativeness, and therefore the findings cannot be generalised to the groups not represented among participants, nor can they be used in any way to calculate the prevalence of Long Covid. Respondents were predominantly White, female and of higher socioeconomic status. People living with Long Covid who use social media (and therefore were able to access the survey) could have different characteristics to those who do not use such platforms. Indeed, some of those with Long Covid in the community who are suffering ill health may not realise it is due to Long Covid, particularly if their infection was not lab-confirmed in the first place. In relation to the finding that the majority of participants being women, there is some evidence that Long Covid may be more common in women but not to the extent of the gender split seen in this survey28. We tried to keep the survey as short as possible to be manageable, therefore some of the details around baseline characteristics, such as body mass index which requires self-measurement, were not collected. Although we asked about previous health status in general, we did not ascertain the prevalence/absence of each reported symptom before COVI...

    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

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  3. Version published to 10.1101/2021.03.21.21253968v2 on medRxiv
  4. Version published to 10.1101/2021.03.21.21253968v1 on medRxiv