Clinical coding of long COVID in English primary care: a federated analysis of 58 million patient records in situ using OpenSAFELY

  1. Alex J Walker
  2. Brian MacKenna
  3. Peter Inglesby
  4. Laurie Tomlinson
  5. Christopher T Rentsch
  6. Helen J Curtis
  7. Caroline E Morton
  8. Jessica Morley
  9. Amir Mehrkar
  10. Seb Bacon
  11. George Hickman
  12. Chris Bates
  13. Richard Croker
  14. David Evans
  15. Tom Ward
  16. Jonathan Cockburn
  17. Simon Davy
  18. Krishnan Bhaskaran
  19. Anna Schultze
  20. Elizabeth J Williamson
  21. William J Hulme
  22. Helen I McDonald
  23. Rohini Mathur
  24. Rosalind M Eggo
  25. Kevin Wing
  26. Angel YS Wong
  27. Harriet Forbes
  28. John Tazare
  29. John Parry
  30. Frank Hester
  31. Sam Harper
  32. Shaun O’Hanlon
  33. Alex Eavis
  34. Richard Jarvis
  35. Dima Avramov
  36. Paul Griffiths
  37. Aaron Fowles
  38. Nasreen Parkes
  39. Ian J Douglas
  40. Stephen JW Evans
  41. Liam Smeeth
  42. Ben Goldacre
  43. (The OpenSAFELY Collaborative)

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Long COVID describes new or persistent symptoms at least 4 weeks after onset of acute COVID-19. Clinical codes to describe this phenomenon were recently created.

Aim

To describe the use of long-COVID codes, and variation of use by general practice, demographic variables, and over time.

Design and setting

Population-based cohort study in English primary care.

Method

Working on behalf of NHS England, OpenSAFELY data were used encompassing 96% of the English population between 1 February 2020 and 25 May 2021. The proportion of people with a recorded code for long COVID was measured overall and by demographic factors, electronic health record software system (EMIS or TPP), and week.

Results

Long COVID was recorded for 23 273 people. Coding was unevenly distributed among practices, with 26.7% of practices having never used the codes. Regional variation ranged between 20.3 per 100 000 people for East of England (95% confidence interval [CI] = 19.3 to 21.4) and 55.6 per 100 000 people in London (95% CI = 54.1 to 57.1). Coding was higher among females (52.1, 95% CI = 51.3 to 52.9) than males (28.1, 95% CI = 27.5 to 28.7), and higher among practices using EMIS (53.7, 95% CI = 52.9 to 54.4) than those using TPP (20.9, 95% CI = 20.3 to 21.4).

Conclusion

Current recording of long COVID in primary care is very low, and variable between practices. This may reflect patients not presenting; clinicians and patients holding different diagnostic thresholds; or challenges with the design and communication of diagnostic codes. Increased awareness of diagnostic codes is recommended to facilitate research and planning of services, and also surveys with qualitative work to better evaluate clinicians’ understanding of the diagnosis.

Article activity feed

  1. Version published to 10.3399/bjgp.2021.0301
  2. ScreenIT

    SciScore for 10.1101/2021.05.06.21256755: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Software and Reproducibility: Data management and analysis was performed using the OpenSAFELY software libraries and Jupyter notebooks, both implemented using Python 3.
    Python
    suggested: (IPython, RRID:SCR_001658)
    This is an analysis delivered using federated analysis through the OpenSAFELY platform: codelists and code for data management and data analysis were specified once using the OpenSAFELY tools; then transmitted securely from the OpenSAFELY jobs server to the OpenSAFELY-TPP platform within TPP’s secure environment, and separately to the OpenSAFELY-EMIS platform within EMIS’s secure environment, where they were each executed separately against local patient data; summary results were then reviewed for disclosiveness, released, and combined for the final outputs.
    EMIS’s
    suggested: None

    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:
    Strengths and weaknesses: The key strength of this study is its unprecedented scale: we include over 58 million people, 95% of the population in England. In contrast with many studies that use electronic health record data, we were also able to compare long COVID diagnostic codes between practices that use different software systems, and find a striking disparity: this has important implications for understanding whether clinicians are using the codes appropriately. A key weakness of this data for estimating true prevalence of long COVID in primary care, and factors associated with the condition, is that it relies on clinicians formally entering a diagnostic or referral code into the patient’s record: we note that this is a limitation of all electronic health record research for all clinical conditions and activity; however the emergence of a new diagnosis and recent launch of a new set of diagnostic codes may present new challenges in this regard. Research in Context: To our knowledge there are no other studies on prevalence of long COVID using clinicians’ diagnoses or electronic health records data. There are numerous studies using self-reported data from patients on the prevalence of continued symptoms following COVID-19, with estimates varying between 4.5% and 89%, largely due to highly variable case definitions4; individual symptoms characteristing long COVID have been reported as fatigue, headache, dyspnea and anosmia5. The ONS COVID Infection Survey estimates prevalenc...

    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

    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 10.1101/2021.05.06.21256755v1 on medRxiv
  4. Version published to 10.1101/2021.05.06.21256755v2 on medRxiv