Patient characteristics associated with clinically coded long COVID: an OpenSAFELY study using electronic health records

Yinghui Wei^{1

2

3}, Elsie Mf Horne^{2

4}, Rochelle Knight^{2

4

5

6}, Genevieve Cezard⁷, Alex J Walker⁸, Louis Fisher⁸, Rachel Denholm^{2

4

9}, Kurt Taylor^{2

6}, Venexia Walker^{2

5

10}, Stephanie Riley¹¹, Dylan M Williams¹², Robert Willans¹³, Simon Davy⁸, Sebastian Bacon⁸, Ben Goldacre⁸, Amir Mehrkar⁸, Spiros Denaxas^{14

15

16

17}, Felix Greaves^{13

18}, Richard J Silverwood¹⁹, Aziz Sheikh²⁰, Nish Chaturvedi¹², Angela M Wood⁷, John Macleod^{2

6}, Claire Steves²¹, Jonathan Sterne^{2

4

9}; UK COVID-19 Longitudinal Health and Wellbeing National Core Study and CONVALESCENCE study

Affiliations

¹ Centre for Mathematical Sciences, School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, UK y.wei3@exeter.ac.uk.
² Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
³ Exeter Medical School, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK.
⁴ NIHR Bristol Biomedical Research Centre, Bristol, UK.
⁵ MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
⁶ The National Institute for Health and Care Research Applied Research Collaboration West at University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK.
⁷ Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
⁸ The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
⁹ Health Data Research UK South-West, Bristol, UK.
¹⁰ Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, United States.
¹¹ Centre for Mathematical Sciences, University of Plymouth, Plymouth, UK.
¹² MRC Unit for Lifelong Health and Ageing, University College London (UCL), London, UK.
¹³ National Institute for Health and Care Excellence, London, UK.
¹⁴ Institute of Health Informatics, UCL, London, UK.
¹⁵ British Heart Foundation Data Science Centre, Health Data Research, London, UK.
¹⁶ NIHR UCL Hospitals Biomedical Research Centre (UCLH BRC), London, UK.
¹⁷ UCL BHF Research Accelerator, UCL, London, UK.
¹⁸ Department of Primary Care and Public Health, Imperial College London, London, UK.
¹⁹ Centre for Longitudinal Studies, UCL Social Research Institute, UCL, London, UK.
²⁰ Usher Institute, The University of Edinburgh, Edinburgh, UK.
²¹ Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK.

PMID: 40500151
PMCID: PMC12820521
DOI: 10.3399/BJGPO.2024.0140

Patient characteristics associated with clinically coded long COVID: an OpenSAFELY study using electronic health records

Yinghui Wei et al. BJGP Open. 2025.

. 2025 Dec 19;9(4):BJGPO.2024.0140.

doi: 10.3399/BJGPO.2024.0140. Print 2025 Dec.

Authors

Affiliations

¹ Centre for Mathematical Sciences, School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, UK y.wei3@exeter.ac.uk.
² Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
³ Exeter Medical School, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK.
⁴ NIHR Bristol Biomedical Research Centre, Bristol, UK.
⁵ MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
⁶ The National Institute for Health and Care Research Applied Research Collaboration West at University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK.
⁷ Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
⁸ The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
⁹ Health Data Research UK South-West, Bristol, UK.
¹⁰ Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, United States.
¹¹ Centre for Mathematical Sciences, University of Plymouth, Plymouth, UK.
¹² MRC Unit for Lifelong Health and Ageing, University College London (UCL), London, UK.
¹³ National Institute for Health and Care Excellence, London, UK.
¹⁴ Institute of Health Informatics, UCL, London, UK.
¹⁵ British Heart Foundation Data Science Centre, Health Data Research, London, UK.
¹⁶ NIHR UCL Hospitals Biomedical Research Centre (UCLH BRC), London, UK.
¹⁷ UCL BHF Research Accelerator, UCL, London, UK.
¹⁸ Department of Primary Care and Public Health, Imperial College London, London, UK.
¹⁹ Centre for Longitudinal Studies, UCL Social Research Institute, UCL, London, UK.
²⁰ Usher Institute, The University of Edinburgh, Edinburgh, UK.
²¹ Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK.

PMID: 40500151
PMCID: PMC12820521
DOI: 10.3399/BJGPO.2024.0140

Abstract

Background: Clinically coded long COVID cases in electronic health records (EHRs) are incomplete, despite reports of rising cases of long COVID.

Aim: To determine patient characteristics associated with clinically coded long COVID.

Design & setting: With the approval of NHS England, we conducted a cohort study using EHRs within the OpenSAFELY-TPP platform in England, to study patient characteristics associated with clinically coded long COVID from 29 January 2020 to 31 March 2022.

Method: We summarised the distribution of characteristics for people with clinically coded long COVID. We estimated age-sex adjusted hazard ratios (aHRs) and fully aHRs for coded long COVID. Patient characteristics included demographic factors, and health behavioural and clinical factors.

Results: Among 17 986 419 adults, 36 886 (0.21%) were clinically coded with long COVID. Patient characteristics associated with coded long COVID included female sex, younger age (aged <60 years), obesity, living in less deprived areas, ever smoking, greater consultation frequency, and history of diagnosed asthma, mental health conditions, pre-pandemic post-viral fatigue, or psoriasis. These associations were attenuated following two doses of COVID-19 vaccines compared with before vaccination. Differences in the predictors of coded long COVID between the pre-vaccination and post-vaccination cohorts may reflect the different patient characteristics in these two cohorts rather than the vaccination status. Incidence of coded long COVID was higher in those with hospitalised COVID-19 than with those with non-hospitalised COVID-19.

Conclusion: We identified variation in coded long COVID by patient characteristic. Results should be interpreted with caution as long COVID was likely under-recorded in EHRs.

Keywords: COVID-19; Long COVID; SARS-CoV-2; post-acute COVID-19 syndrome.

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Conflict of interest statement

Over the past five years BG has received research funding from the Laura and John Arnold Foundation, the NHS National Institute for Health Research (NIHR), the NIHR School of Primary Care Research, the NIHR Oxford Biomedical Research Centre, the Mohn-Westlake Foundation, NIHR Applied Research Collaboration Oxford and Thames Valley, the Wellcome Trust, the Good Thinking Foundation, Health Data Research UK (HDRUK), the Health Foundation, and the World Health Organization; he also receives personal income from speaking and writing for lay audiences on the misuse of science

Figures

Figure 1.. Primary and post-COVID diagnosis cohorts: age-and-sex adjusted and fully adjusted hazard ratios of coded long COVID for demographic variables. BMI = body mass index. IMD = Index of Multiple Deprivation

Figure 2.. Pre-vaccination and post-vaccination cohorts: age-and-sex adjusted and fully adjusted hazard ratios of coded long COVID for demographic variables. BMI = body mass index. IMD = Index of Multiple Deprivation

Figure 3.. Primary and post-COVID diagnosis cohorts: age-and-sex adjusted and fully adjusted hazard ratios of coded long COVID for health behavioural and clinical variables. SLE = systemic lupus erythematosus

Figure 4.. Pre-vaccination and post-vaccination cohorts: age-and-sex adjusted and fully adjusted hazard ratios of coded long COVID for health behavioural and clinical variables. SLE = systemic lupus erythematosus

See this image and copyright information in PMC

References

1. Munblit D, O’Hara ME, Akrami A, et al. Long COVID: aiming for a consensus. Lancet Respir Med. 2022; 10 (7):632–634. doi: 10.1016/S2213-2600(22)00135-7. - DOI - PMC - PubMed
1. Al-Aly Z, Xie Y, Bowe B. High-dimensional characterization of post-acute sequelae of COVID-19. Nature New Biol. 2021; 594 (7862):259–264. doi: 10.1038/s41586-021-03553-9. - DOI - PubMed
1. NHS England Post-COVID syndrome (long COVID) https://www.england.nhs.uk/coronavirus/post-covid-syndrome-long-covid/ [5 Sep 2025]. https://www.england.nhs.uk/coronavirus/post-covid-syndrome-long-covid/ accessed.
1. Soriano JB, Murthy S, Marshall JC, et al. A clinical case definition of post-COVID-19 condition by a Delphi consensus. Lancet Infect Dis. 2022; 22 (4):e102–e107. doi: 10.1016/S1473-3099(21)00703-9. - DOI - PMC - PubMed
1. National Institute for Health and Care Excellence (NICE) COVID-19 rapid guideline: managing the long-term effects of COVID-19. https://www.nice.org.uk/guidance/ng188. [24 Nov 2025]. https://www.nice.org.uk/guidance/ng188 accessed. - PubMed

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Patient characteristics associated with clinically coded long COVID: an OpenSAFELY study using electronic health records

Affiliations

Patient characteristics associated with clinically coded long COVID: an OpenSAFELY study using electronic health records

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

LinkOut - more resources

Full Text Sources

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