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

Yinghui Wei; Elsie Mf Horne; Rochelle Knight; Genevieve Cezard; Alex J Walker; Louis Fisher; Rachel Denholm; Kurt Taylor; Venexia Walker; Stephanie Riley; Dylan M Williams; Robert Willans; Simon Davy; Sebastian Bacon; Ben Goldacre; Amir Mehrkar; Spiros Denaxas; Felix Greaves; Richard J Silverwood; Aziz Sheikh; Nish Chaturvedi; Angela M Wood; John Macleod; Claire Steves; Jonathan Sterne; UK COVID-19 Longitudinal Health and Wellbeing National Core Study and CONVALESCENCE study

doi:10.3399/BJGPO.2024.0140

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

Yinghui Wei^*, Elsie Mf Horne, Rochelle Knight, Genevieve Cezard, Alex J Walker, Louis Fisher, Rachel Denholm, Kurt Taylor, Venexia Walker, Stephanie Riley, Dylan M Williams, Robert Willans, Simon Davy, Sebastian Bacon, Ben Goldacre, Amir Mehrkar, Spiros Denaxas, Felix Greaves, Richard J Silverwood, Aziz SheikhNish Chaturvedi, Angela M Wood, John Macleod, Claire Steves, Jonathan Sterne, UK COVID-19 Longitudinal Health and Wellbeing National Core Study and CONVALESCENCE study

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

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.

Original language	English
Article number	2024.0140
Number of pages	18
Journal	BJGP Open
Volume	9
Issue number	4
DOIs	https://doi.org/10.3399/BJGPO.2024.0140
Publication status	Published - 19 Dec 2025

Bibliographical note

Publisher Copyright:
© 2025, The Authors.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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Wei, Y., Horne, E. M., Knight, R., Cezard, G., Walker, A. J., Fisher, L., Denholm, R., Taylor, K., Walker, V., Riley, S., Williams, D. M., Willans, R., Davy, S., Bacon, S., Goldacre, B., Mehrkar, A., Denaxas, S., Greaves, F., Silverwood, R. J., ... UK COVID-19 Longitudinal Health and Wellbeing National Core Study and CONVALESCENCE study (2025). Patient characteristics associated with clinically coded long COVID: an OpenSAFELY study using electronic health records. BJGP Open, 9(4), Article 2024.0140. https://doi.org/10.3399/BJGPO.2024.0140

@article{ae8eed2e165f4034a83a29d75a4bc5ae,

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

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.",

author = "Yinghui Wei and Horne, \{Elsie Mf\} and Rochelle Knight and Genevieve Cezard and Walker, \{Alex J\} and Louis Fisher and Rachel Denholm and Kurt Taylor and Venexia Walker and Stephanie Riley and Williams, \{Dylan M\} and Robert Willans and Simon Davy and Sebastian Bacon and Ben Goldacre and Amir Mehrkar and Spiros Denaxas and Felix Greaves and Silverwood, \{Richard J\} and Aziz Sheikh and Nish Chaturvedi and Wood, \{Angela M\} and John Macleod and Claire Steves and Jonathan Sterne and \{UK COVID-19 Longitudinal Health and Wellbeing National Core Study and CONVALESCENCE study\}",

note = "Publisher Copyright: {\textcopyright} 2025, The Authors.",

year = "2025",

month = dec,

day = "19",

doi = "10.3399/BJGPO.2024.0140",

language = "English",

volume = "9",

journal = "BJGP Open",

issn = "2398-3795",

publisher = "Royal College of General Practitioners",

number = "4",

}

Wei, Y, Horne, EM, Knight, R, Cezard, G, Walker, AJ, Fisher, L, Denholm, R, Taylor, K, Walker, V, Riley, S, Williams, DM, Willans, R, Davy, S, Bacon, S, Goldacre, B, Mehrkar, A, Denaxas, S, Greaves, F, Silverwood, RJ, Sheikh, A, Chaturvedi, N, Wood, AM, Macleod, J, Steves, C, Sterne, J & UK COVID-19 Longitudinal Health and Wellbeing National Core Study and CONVALESCENCE study 2025, 'Patient characteristics associated with clinically coded long COVID: an OpenSAFELY study using electronic health records', BJGP Open, vol. 9, no. 4, 2024.0140. https://doi.org/10.3399/BJGPO.2024.0140

TY - JOUR

T1 - Patient characteristics associated with clinically coded long COVID

T2 - an OpenSAFELY study using electronic health records

AU - Wei, Yinghui

AU - Horne, Elsie Mf

AU - Knight, Rochelle

AU - Cezard, Genevieve

AU - Walker, Alex J

AU - Fisher, Louis

AU - Denholm, Rachel

AU - Taylor, Kurt

AU - Walker, Venexia

AU - Riley, Stephanie

AU - Williams, Dylan M

AU - Willans, Robert

AU - Davy, Simon

AU - Bacon, Sebastian

AU - Goldacre, Ben

AU - Mehrkar, Amir

AU - Denaxas, Spiros

AU - Greaves, Felix

AU - Silverwood, Richard J

AU - Sheikh, Aziz

AU - Chaturvedi, Nish

AU - Wood, Angela M

AU - Macleod, John

AU - Steves, Claire

AU - Sterne, Jonathan

AU - UK COVID-19 Longitudinal Health and Wellbeing National Core Study and CONVALESCENCE study

PY - 2025/12/19

Y1 - 2025/12/19

N2 - 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.

AB - 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.

U2 - 10.3399/BJGPO.2024.0140

DO - 10.3399/BJGPO.2024.0140

M3 - Article (Academic Journal)

C2 - 40500151

SN - 2398-3795

VL - 9

JO - BJGP Open

JF - BJGP Open

IS - 4

M1 - 2024.0140

ER -

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

Abstract

Bibliographical note

UN SDGs

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