OpenSAFELY NHS Service Restoration Observatory 1: primary care clinical activity in England during the first wave of COVID-19

  1. Helen J Curtis
  2. Brian MacKenna
  3. Richard Croker
  4. Peter Inglesby
  5. Alex J Walker
  6. Jessica Morley
  7. Amir Mehrkar
  8. Caroline E Morton
  9. Seb Bacon
  10. George Hickman
  11. Chris Bates
  12. David Evans
  13. Tom Ward
  14. Jonathan Cockburn
  15. Simon Davy
  16. Krishnan Bhaskaran
  17. Anna Schultze
  18. Christopher T Rentsch
  19. Elizabeth J Williamson
  20. William J Hulme
  21. Helen I McDonald
  22. Laurie Tomlinson
  23. Rohini Mathur
  24. Henry Drysdale
  25. Rosalind M Eggo
  26. Kevin Wing
  27. Angel YS Wong
  28. Harriet Forbes
  29. John Parry
  30. Frank Hester
  31. Sam Harper
  32. Stephen JW Evans
  33. Ian J Douglas
  34. Liam Smeeth
  35. Ben Goldacre
  36. (The OpenSAFELY Collaborative)

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Abstract

The COVID-19 pandemic has disrupted healthcare activity. The NHS stopped non-urgent work in March 2020, later recommending services be restored to near-normal levels before winter where possible.

Aim

To describe the volume and variation of coded clinical activity in general practice, taking respiratory disease and laboratory procedures as examples.

Design and setting

Working on behalf of NHS England, a cohort study was conducted of 23.8 million patient records in general practice, in situ using OpenSAFELY.

Method

Activity using Clinical Terms Version 3 codes and keyword searches from January 2019 to September 2020 are described.

Results

Activity recorded in general practice declined during the pandemic, but largely recovered by September. There was a large drop in coded activity for laboratory tests, with broad recovery to pre-pandemic levels by September. One exception was the international normalised ratio test, with a smaller reduction (median tests per 1000 patients in 2020: February 8.0; April 6.2; September 6.9). The pattern of recording for respiratory symptoms was less affected, following an expected seasonal pattern and classified as ‘no change’. Respiratory infections exhibited a sustained drop, not returning to pre-pandemic levels by September. Asthma reviews experienced a small drop but recovered, whereas chronic obstructive pulmonary disease reviews remained below baseline.

Conclusion

An open-source software framework was delivered to describe trends and variation in clinical activity across an unprecedented scale of primary care data. The COVD-19 pandemic led to a substantial change in healthcare activity. Most laboratory tests showed substantial reduction, largely recovering to near-normal levels by September, with some important tests less affected and recording of respiratory disease codes was mixed.

Article activity feed

  1. Version published to 10.3399/bjgp.2021.0380
  2. Version published to 10.1101/2021.01.06.21249352v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.01.06.21249352: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Software and Reproducibility: Data management was performed using Python and the OpenSAFELY software, with data extracted via SQL Server Management Studio and analysis carried out using Python.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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 strengths of this study are the scale and completeness of the underlying raw EHR data. The OpenSAFELY platform runs analyses across an unprecedented scale of data: the full dataset of all raw, single-event-level clinical events for all patients at 40% of all GP practices in England, including all tests, treatments, diagnostic events, and other salient clinical and demographic information for 23.8 million patients. By contrast the CPRD dataset contains records for a substantially smaller number of current patients spread across two databases; and the GPES dataset held by NHS Digital contains a much smaller amount of data on each individual patient. OpenSAFELY also provides data in near-real time, providing unprecedented opportunities for audit and feedback to rapidly identify and resolve concerns around health service activity: the delay from occurrence of a clinical event to it appearing in the OpenSAFELY platform varies from two to nine days. This is substantially faster than any other source of GP data, including those giving much less complete records. It is also faster than any source of large scale secondary care data: the SUS dataset, containing duration and main activity for each hospital event, is coded several weeks after completion (not commencement) of the clinical episode or spell. We also recognise some limitations. We used a data-driven approach, capitalising on the historical CTV3 hierarchy where possible, in combination with 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.

    About SciScore

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  4. Version published to 10.1101/2021.01.06.21249352v1 on medRxiv