Long COVID burden and risk factors in 10 UK longitudinal studies and electronic health records

  1. Ellen J. Thompson
  2. Dylan M. Williams
  3. Alex J. Walker
  4. Ruth E. Mitchell
  5. Claire L. Niedzwiedz
  6. Tiffany C. Yang
  7. Charlotte F. Huggins
  8. Alex S. F. Kwong
  9. Richard J. Silverwood
  10. Giorgio Di Gessa
  11. Ruth C. E. Bowyer
  12. Kate Northstone
  13. Bo Hou
  14. Michael J. Green
  15. Brian Dodgeon
  16. Katie J. Doores
  17. Emma L. Duncan
  18. Frances M. K. Williams
  19. OpenSAFELY Collaborative
  20. Alex J. Walker
  21. Brian MacKenna
  22. Peter Inglesby
  23. Christopher T. Rentsch
  24. Helen J. Curtis
  25. Caroline E. Morton
  26. Jessica Morley
  27. Amir Mehrkar
  28. Seb Bacon
  29. George Hickman
  30. Chris Bates
  31. Richard Croker
  32. David Evans
  33. Tom Ward
  34. Jonathan Cockburn
  35. Simon Davy
  36. Krishnan Bhaskaran
  37. Anna Schultze
  38. Elizabeth J. Williamson
  39. William J. Hulme
  40. Helen I. McDonald
  41. Laurie Tomlinson
  42. Rohini Mathur
  43. Rosalind M. Eggo
  44. Kevin Wing
  45. Angel Y. S. Wong
  46. Harriet Forbes
  47. John Tazare
  48. John Parry
  49. Frank Hester
  50. Sam Harper
  51. Ian J. Douglas
  52. Stephen J. W. Evans
  53. Liam Smeeth
  54. Ben Goldacre
  55. Andrew Steptoe
  56. David J. Porteous
  57. Rosemary R. C. McEachan
  58. Laurie Tomlinson
  59. Ben Goldacre
  60. Praveetha Patalay
  61. George B. Ploubidis
  62. Srinivasa Vittal Katikireddi
  63. Kate Tilling
  64. Christopher T. Rentsch
  65. Nicholas J. Timpson
  66. Nishi Chaturvedi
  67. Claire J. Steves

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Abstract

The frequency of, and risk factors for, long COVID are unclear among community-based individuals with a history of COVID-19. To elucidate the burden and possible causes of long COVID in the community, we coordinated analyses of survey data from 6907 individuals with self-reported COVID-19 from 10 UK longitudinal study (LS) samples and 1.1 million individuals with COVID-19 diagnostic codes in electronic healthcare records (EHR) collected by spring 2021. Proportions of presumed COVID-19 cases in LS reporting any symptoms for 12+ weeks ranged from 7.8% and 17% (with 1.2 to 4.8% reporting debilitating symptoms). Increasing age, female sex, white ethnicity, poor pre-pandemic general and mental health, overweight/obesity, and asthma were associated with prolonged symptoms in both LS and EHR data, but findings for other factors, such as cardio-metabolic parameters, were inconclusive.

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  1. Version published to 10.1038/s41467-022-30836-0
  2. Version published to 10.1101/2021.06.24.21259277v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.06.24.21259277: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variableSociodemographic factors: These included: age, sex (female/male), ethnicity (white, non-white ethnic minority; in studies where possible), and socioeconomic position measured by highest education levels (degree, no degree), Index of Multiple Deprivation (IMD, a widely used geographical based measure of relative deprivation based on factors such as income, employment and education), and occupational class of own current/recent job (or parental occupational class for younger cohorts; four categories: managerial/professional; intermediate; routine; or not working/not available).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line AuthenticationAuthentication: Mental health: Mental health was captured in the most recent pre-pandemic survey using validated continuous scales of psychological distress that assessed symptoms of common mental health difficulties such as anxiety and depression (e.g., Hospital Anxiety and Depression scale, TwinsUK; Short Mood and Feelings Questionnaire, ALSPAC-G1; Edinburgh Postnatal Depression Scale, ALSPAC-G0; General Health Questionnaire-12, USOC).

    Table 2: Resources

    Antibodies
    SentencesResources
    For studies in which we were able to verify SARS-CoV-2 infection through collected serology data in summer/autumn 2020 (TwinsUK and ALSPAC-G0 and -G1), analyses were replicated on a sub-sample of those who had positive polymerase chain reaction (PCR) obtained through linkage to testing data and/or lateral flow antibody testing (ALSPAC) and enzyme-linked immunosorbent assay (ELISA) (TwinsUK)23 confirming exposure to COVID-19.
    COVID-19
    suggested: None

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Strengths and limitations: This analysis brings together data from 10 longitudinal study samples and EHR, with rich information on pre-pandemic risk factors and COVID-19 symptom length. Although several recent surveys are available, the lack of pre-pandemic measures makes it difficult to assess directional effects of risk factors on outcomes. This study is strengthened by the coordinated investigation in multiple LS that are each susceptible to different sources of bias, with differing study designs, target populations, and selection and attrition processes. Moreover, the use of multiple studies increased statistical power to look at subpopulations, such as ethnic minority groups, and allowed for greater examination of the influence of age on long COVID. Our novel approach to harnessing multiple datasets allowed research questions to be addressed which would not otherwise be possible. Differences between studies in a range of factors -including measurement of risk factors, timing of surveys, design, response rates, and differential selection into the COVID-19 sweeps -are potentially responsible for heterogeneity in estimates. However, despite this heterogeneity, the key findings were consistent across most datasets. Unmeasured/residual confounding bias cannot be ruled out in either LS or EHR; and our analysis was not able to assess causation. We attempted to assess any index event bias using a systematic, structured approach across LS, which produced consistent results, but t...

    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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  4. Version published to 10.1101/2021.06.24.21259277v1 on medRxiv