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Clinical coding of long COVID in English primary care: a federated analysis of 58 million patient records in situ using OpenSAFELY
- The OpenSAFELY Collaborative; Alex J Walker; Brian MacKenna; Peter Inglesby; Christopher T Rentsch; Helen J Curtis; Caroline E Morton; Jessica Morley; Amir Mehrkar; Sebastian CJ Bacon; George Hickman; Christopher Bates; Richard Croker; David Evans; Tom Ward; Jonathan Cockburn; Simon Davy; Krishnan Bhaskaran; Anna Schultze; Elizabeth J Williamson; William J Hulme; Helen I McDonald; Laurie Tomlinson; Rohini Mathur; Rosalind M Eggo; Kevin Wing; Angel YS Wong; Harriet Forbes; John Tazare; John Parry; Frank Hester; Sam Harper; Shaun O'Hanlon; Alex Eavis; Richard Jarvis; Dima Avramov; Paul Griffiths; Aaron Fowles; Nasreen Parkes; Ian J Douglas; Stephen JW Evans; Liam Smeeth; Ben Goldacre.
Affiliation
  • - The OpenSAFELY Collaborative;
  • Alex J Walker; University of Oxford
  • Brian MacKenna; University of Oxford
  • Peter Inglesby; University of Oxford
  • Christopher T Rentsch; London School of Hygiene and Tropical Medicine
  • Helen J Curtis; University of Oxford
  • Caroline E Morton; University of Oxford
  • Jessica Morley; University of Oxford
  • Amir Mehrkar; University of Oxford
  • Sebastian CJ Bacon; University of Oxford
  • George Hickman; University of Oxford
  • Christopher Bates; TPP
  • Richard Croker; University of Oxford
  • David Evans; University of Oxford
  • Tom Ward; University of Oxford
  • Jonathan Cockburn; TPP
  • Simon Davy; University of Oxford
  • Krishnan Bhaskaran; London School of Hygiene and Tropical Medicine
  • Anna Schultze; London School of Hygiene and Tropical Medicine
  • Elizabeth J Williamson; London School of Hygiene and Tropical Medicine
  • William J Hulme; University of Oxford
  • Helen I McDonald; London School of Hygiene and Tropical Medicine
  • Laurie Tomlinson; London School of Hygiene and Tropical Medicine
  • Rohini Mathur; London School of Hygiene and Tropical Medicine
  • Rosalind M Eggo; London School of Hygiene and Tropical Medicine
  • Kevin Wing; London School of Hygiene and Tropical Medicine
  • Angel YS Wong; London School of Hygiene and Tropical Medicine
  • Harriet Forbes; London School of Hygiene and Tropical Medicine
  • John Tazare; London School of Hygiene and Tropical Medicine
  • John Parry; TPP
  • Frank Hester; TPP
  • Sam Harper; TPP
  • Shaun O'Hanlon; EMIS
  • Alex Eavis; EMIS
  • Richard Jarvis; EMIS
  • Dima Avramov; EMIS
  • Paul Griffiths; EMIS
  • Aaron Fowles; EMIS
  • Nasreen Parkes; EMIS
  • Ian J Douglas; London School of Hygiene and Tropical Medicine
  • Stephen JW Evans; London School of Hygiene and Tropical Medicine
  • Liam Smeeth; London School of Hygiene and Tropical Medicine
  • Ben Goldacre; University of Oxford
Preprint in English | medRxiv | ID: ppmedrxiv-21256755
ABSTRACT
BackgroundLong COVID is a term to describe new or persistent symptoms at least four weeks after onset of acute COVID-19. Clinical codes to describe this phenomenon were released in November 2020 in the UK, but it is not known how these codes have been used in practice. MethodsWorking on behalf of NHS England, we used OpenSAFELY data encompassing 96% of the English population. We measured the proportion of people with a recorded code for long COVID, overall and by demographic factors, electronic health record software system, and week. We also measured variation in recording amongst practices. ResultsLong COVID was recorded for 23,273 people. Coding was unevenly distributed amongst practices, with 26.7% of practices having not used the codes at all. Regional variation was high, ranging between 20.3 per 100,000 people for East of England (95% confidence interval 19.3-21.4) and 55.6 in London (95% CI 54.1-57.1). The rate was higher amongst women (52.1, 95% CI 51.3-52.9) compared to men (28.1, 95% CI 27.5-28.7), and higher amongst practices using EMIS software (53.7, 95% CI 52.9-54.4) compared to TPP software (20.9, 95% CI 20.3-21.4). ConclusionsLong COVID coding in primary care is low compared with early reports of long COVID prevalence. This may reflect under-coding, sub-optimal communication of clinical terms, under-diagnosis, a true low prevalence of long COVID diagnosed by clinicians, or a combination of factors. We recommend increased awareness of diagnostic codes, to facilitate research and planning of services; and surveys of clinicians experiences, to complement ongoing patient surveys.
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Full text: Available Collection: Preprints Database: medRxiv Type of study: Observational study / Prognostic study / Qualitative research Language: English Year: 2021 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Observational study / Prognostic study / Qualitative research Language: English Year: 2021 Document type: Preprint
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