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


Journal

The British journal of general practice : the journal of the Royal College of General Practitioners
ISSN: 1478-5242
Titre abrégé: Br J Gen Pract
Pays: England
ID NLM: 9005323

Informations de publication

Date de publication:
11 2021
Historique:
received: 07 05 2021
accepted: 18 06 2021
pubmed: 4 8 2021
medline: 4 11 2021
entrez: 3 8 2021
Statut: epublish

Résumé

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. To describe the use of long-COVID codes, and variation of use by general practice, demographic variables, and over time. Population-based cohort study in English primary care. 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. 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). 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.

Sections du résumé

BACKGROUND
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.

Identifiants

pubmed: 34340970
pii: BJGP.2021.0301
doi: 10.3399/BJGP.2021.0301
pmc: PMC8340730
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e806-e814

Subventions

Organisme : Medical Research Council
ID : MC_PC_20059
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_20051
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/S003975/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_20030
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_20058
Pays : United Kingdom

Investigateurs

Liam Smeeth (L)
Ben Goldacre (B)

Commentaires et corrections

Type : CommentIn

Informations de copyright

© The Authors.

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Auteurs

Alex J Walker (AJ)

The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford.

Brian MacKenna (B)

The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford.

Peter Inglesby (P)

The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford.

Laurie Tomlinson (L)

Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London.

Christopher T Rentsch (CT)

Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London.

Helen J Curtis (HJ)

The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford.

Caroline E Morton (CE)

The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford.

Jessica Morley (J)

The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford.

Amir Mehrkar (A)

The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford.

Seb Bacon (S)

The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford.

George Hickman (G)

The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford.

Chris Bates (C)

TPP, Leeds.

Richard Croker (R)

The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford.

David Evans (D)

The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford.

Tom Ward (T)

The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford.

Simon Davy (S)

The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford.

Krishnan Bhaskaran (K)

Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London.

Anna Schultze (A)

Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London.

Elizabeth J Williamson (EJ)

Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London.

William J Hulme (WJ)

The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford.

Helen I McDonald (HI)

Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London.

Rohini Mathur (R)

Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London.

Rosalind M Eggo (RM)

Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London.

Kevin Wing (K)

Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London.

Angel Ys Wong (AY)

Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London.

Harriet Forbes (H)

Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London.

John Tazare (J)

Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London.

John Parry (J)

TPP, Leeds.

Frank Hester (F)

TPP, Leeds.

Sam Harper (S)

TPP, Leeds.

Shaun O'Hanlon (S)

EMIS Health, Leeds.

Alex Eavis (A)

EMIS Health, Leeds.

Richard Jarvis (R)

EMIS Health, Leeds.

Dima Avramov (D)

EMIS Health, Leeds.

Paul Griffiths (P)

EMIS Health, Leeds.

Aaron Fowles (A)

EMIS Health, Leeds.

Nasreen Parkes (N)

EMIS Health, Leeds.

Ian J Douglas (IJ)

Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London.

Stephen Jw Evans (SJ)

Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London.

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