Prevalence and risk factors for long COVID among adults in Scotland using electronic health records: a national, retrospective, observational cohort study

Karen Jeffrey; Lana Woolford; Rishma Maini; Siddharth Basetti; Ashleigh Batchelor; David Weatherill; Chris White; Vicky Hammersley; Tristan Millington; Calum Macdonald; Jennifer K. Quint; Robin Kerr; Steven Kerr; Syed Ahmar Shah; Igor Rudan; Adeniyi Francis Fagbamigbe; Colin R. Simpson; Srinivasa Vittal Katikireddi; Chris Robertson; Lewis Ritchie; Aziz Sheikh; Luke Daines

doi:10.1016/j.eclinm.2024.102590

Prevalence and risk factors for long COVID among adults in Scotland using electronic health records: a national, retrospective, observational cohort study

Karen Jeffrey, Lana Woolford, Rishma Maini, Siddharth Basetti, Ashleigh Batchelor, David Weatherill, Chris White, Vicky Hammersley, Tristan Millington, Calum Macdonald, Jennifer K. Quint, Robin Kerr, Steven Kerr, Syed Ahmar Shah, Igor Rudan, Adeniyi Francis Fagbamigbe, Colin R. Simpson, Srinivasa Vittal Katikireddi, Chris Robertson, Lewis RitchieAziz Sheikh, Luke Daines^* (Corresponding Author)

^*Corresponding author for this work

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Abstract

Summary Background Long COVID is a debilitating multisystem condition. The objective of this study was to estimate the prevalence of long COVID in the adult population of Scotland, and to identify risk factors associated with its development. Methods In this national, retrospective, observational cohort study, we analysed electronic health records (EHRs) for all adults (≥18 years) registered with a general medical practice and resident in Scotland between March 1, 2020, and October 26, 2022 (98–99% of the population). We linked data from primary care, secondary care, laboratory testing and prescribing. Four outcome measures were used to identify long COVID: clinical codes, free text in primary care records, free text on sick notes, and a novel operational definition. The operational definition was developed using Poisson regression to identify clinical encounters indicative of long COVID from a sample of negative and positive COVID-19 cases matched on time-varying propensity to test positive for SARS-CoV-2. Possible risk factors for long COVID were identified by stratifying descriptive statistics by long COVID status. Findings Of 4,676,390 participants, 81,219 (1.7%) were identified as having long COVID. Clinical codes identified the fewest cases (n = 1,092, 0.02%), followed by free text (n = 8,368, 0.2%), sick notes (n = 14,469, 0.3%), and the operational definition (n = 64,193, 1.4%). There was limited overlap in cases identified by the measures; however, temporal trends and patient characteristics were consistent across measures. Compared with the general population, a higher proportion of people with long COVID were female (65.1% versus 50.4%), aged 38–67 (63.7% versus 48.9%), overweight or obese (45.7% versus 29.4%), had one or more comorbidities (52.7% versus 36.0%), were immunosuppressed (6.9% versus 3.2%), shielding (7.9% versus 3.4%), or hospitalised within 28 days of testing positive (8.8% versus 3.3%%), and had tested positive before Omicron became the dominant variant (44.9% versus 35.9%). The operational definition identified long COVID cases with combinations of clinical encounters (from four symptoms, six investigation types, and seven management strategies) recorded in EHRs within 4–26 weeks of a positive SARS-CoV-2 test. These combinations were significantly (p

Original language	English
Article number	102590
Number of pages	13
Journal	EClinicalMedicine
Volume	71
Early online date	11 Apr 2024
DOIs	https://doi.org/10.1016/j.eclinm.2024.102590
Publication status	Published - 1 May 2024

Bibliographical note

Acknowledgements
This work was supported by the Chief Scientist Office, grant number COV/LTE/20/15. EAVE II is supported by a grant (MC_PC_19075) from the Medical Research Council; and a grant (MC_PC_19004) from BREATHE–The Health Data Research Hub for Respiratory Health, funded through the UK Research and Innovation Industrial Strategy Challenge Fund. LD was supported by a post-doctoral clinical fellowship from the Asthma UK Centre for Applied Research. SVK acknowledges funding from a NRS Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2) and the Scottish Government Chief Scientist Office (SPHSU17). The authors would like to acknowledge the support of Dave Kelly and Lamorna Brown of Albasoft Ltd., and Sharon Kennedy, Mike Birnie, Safraj Shahul Hameed and Elliott Hall of Public Health Scotland for their involvement in obtaining approvals, provisioning, and linking data and the use of the secure analytical platform within the National Safe Haven.
Funding Chief Scientist Office (Scotland), Medical Research Council, and BREATHE.

Data Availability Statement

All code, metadata and documentation for this project is publicly available at https://github.com/EAVE-II/Long-COVID. Most of the data used in this study are highly sensitive and will not be made available publicly.

Keywords

Long COVID
Population surveillance
Primary health care
Clinical coding
Matched-pair analysis

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Data From Prevalence and risk factors for long COVID among adults in Scotland using electronic health records: a national, retrospective, observational cohort study
Jeffrey, K. (Creator), Woolford, L. (Creator), Maini, R. (Creator), Basetti, S. (Creator), Batchelor, A. (Creator), Weatherill, D. (Creator), White, C. (Creator), Hammersley, V. (Creator), Millington, T. (Creator), Macdonald, C. (Creator), Quint, J. K. (Creator), Kerr, R. (Creator), Kerr, S. (Creator), Shah, S. A. (Creator), Rudan, I. (Creator), Fagbamigbe, A. (Creator), Simpson, C. R. (Creator), Katikireddi, S. V. (Creator), Robertson, C. (Creator), Ritchie, L. (Creator) & Daines , L. (Creator), University of Aberdeen, May 2024
DOI: 10.1016/j.eclinm.2024.102590, https://ars.els-cdn.com/content/image/1-s2.0-S258953702400169X-mmc1.pdf and one more link, https://github.com/EAVE-II/Long-COVID (show fewer)
Dataset

Cite this

Jeffrey, K., Woolford, L., Maini, R., Basetti, S., Batchelor, A., Weatherill, D., White, C., Hammersley, V., Millington, T., Macdonald, C., Quint, J. K., Kerr, R., Kerr, S., Shah, S. A., Rudan, I., Fagbamigbe, A. F., Simpson, C. R., Katikireddi, S. V., Robertson, C., ... Daines, L. (2024). Prevalence and risk factors for long COVID among adults in Scotland using electronic health records: a national, retrospective, observational cohort study. EClinicalMedicine, 71, Article 102590. https://doi.org/10.1016/j.eclinm.2024.102590

Jeffrey, K, Woolford, L, Maini, R, Basetti, S, Batchelor, A, Weatherill, D, White, C, Hammersley, V, Millington, T, Macdonald, C, Quint, JK, Kerr, R, Kerr, S, Shah, SA, Rudan, I, Fagbamigbe, AF, Simpson, CR, Katikireddi, SV, Robertson, C, Ritchie, L, Sheikh, A & Daines, L 2024, 'Prevalence and risk factors for long COVID among adults in Scotland using electronic health records: a national, retrospective, observational cohort study', EClinicalMedicine, vol. 71, 102590. https://doi.org/10.1016/j.eclinm.2024.102590

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abstract = "Summary Background Long COVID is a debilitating multisystem condition. The objective of this study was to estimate the prevalence of long COVID in the adult population of Scotland, and to identify risk factors associated with its development. Methods In this national, retrospective, observational cohort study, we analysed electronic health records (EHRs) for all adults (≥18 years) registered with a general medical practice and resident in Scotland between March 1, 2020, and October 26, 2022 (98–99% of the population). We linked data from primary care, secondary care, laboratory testing and prescribing. Four outcome measures were used to identify long COVID: clinical codes, free text in primary care records, free text on sick notes, and a novel operational definition. The operational definition was developed using Poisson regression to identify clinical encounters indicative of long COVID from a sample of negative and positive COVID-19 cases matched on time-varying propensity to test positive for SARS-CoV-2. Possible risk factors for long COVID were identified by stratifying descriptive statistics by long COVID status. Findings Of 4,676,390 participants, 81,219 (1.7%) were identified as having long COVID. Clinical codes identified the fewest cases (n = 1,092, 0.02%), followed by free text (n = 8,368, 0.2%), sick notes (n = 14,469, 0.3%), and the operational definition (n = 64,193, 1.4%). There was limited overlap in cases identified by the measures; however, temporal trends and patient characteristics were consistent across measures. Compared with the general population, a higher proportion of people with long COVID were female (65.1% versus 50.4%), aged 38–67 (63.7% versus 48.9%), overweight or obese (45.7% versus 29.4%), had one or more comorbidities (52.7% versus 36.0%), were immunosuppressed (6.9% versus 3.2%), shielding (7.9% versus 3.4%), or hospitalised within 28 days of testing positive (8.8% versus 3.3%%), and had tested positive before Omicron became the dominant variant (44.9% versus 35.9%). The operational definition identified long COVID cases with combinations of clinical encounters (from four symptoms, six investigation types, and seven management strategies) recorded in EHRs within 4–26 weeks of a positive SARS-CoV-2 test. These combinations were significantly (p ",

keywords = "Long COVID, Population surveillance, Primary health care, Clinical coding, Matched-pair analysis",

author = "Karen Jeffrey and Lana Woolford and Rishma Maini and Siddharth Basetti and Ashleigh Batchelor and David Weatherill and Chris White and Vicky Hammersley and Tristan Millington and Calum Macdonald and Quint, {Jennifer K.} and Robin Kerr and Steven Kerr and Shah, {Syed Ahmar} and Igor Rudan and Fagbamigbe, {Adeniyi Francis} and Simpson, {Colin R.} and Katikireddi, {Srinivasa Vittal} and Chris Robertson and Lewis Ritchie and Aziz Sheikh and Luke Daines",

note = "Acknowledgements This work was supported by the Chief Scientist Office, grant number COV/LTE/20/15. EAVE II is supported by a grant (MC_PC_19075) from the Medical Research Council; and a grant (MC_PC_19004) from BREATHE–The Health Data Research Hub for Respiratory Health, funded through the UK Research and Innovation Industrial Strategy Challenge Fund. LD was supported by a post-doctoral clinical fellowship from the Asthma UK Centre for Applied Research. SVK acknowledges funding from a NRS Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2) and the Scottish Government Chief Scientist Office (SPHSU17). The authors would like to acknowledge the support of Dave Kelly and Lamorna Brown of Albasoft Ltd., and Sharon Kennedy, Mike Birnie, Safraj Shahul Hameed and Elliott Hall of Public Health Scotland for their involvement in obtaining approvals, provisioning, and linking data and the use of the secure analytical platform within the National Safe Haven. Funding Chief Scientist Office (Scotland), Medical Research Council, and BREATHE.",

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T1 - Prevalence and risk factors for long COVID among adults in Scotland using electronic health records

T2 - a national, retrospective, observational cohort study

AU - Jeffrey, Karen

AU - Woolford, Lana

AU - Maini, Rishma

AU - Basetti, Siddharth

AU - Batchelor, Ashleigh

AU - Weatherill, David

AU - White, Chris

AU - Hammersley, Vicky

AU - Millington, Tristan

AU - Macdonald, Calum

AU - Quint, Jennifer K.

AU - Kerr, Robin

AU - Kerr, Steven

AU - Shah, Syed Ahmar

AU - Rudan, Igor

AU - Fagbamigbe, Adeniyi Francis

AU - Simpson, Colin R.

AU - Katikireddi, Srinivasa Vittal

AU - Robertson, Chris

AU - Ritchie, Lewis

AU - Sheikh, Aziz

AU - Daines, Luke

N1 - Acknowledgements This work was supported by the Chief Scientist Office, grant number COV/LTE/20/15. EAVE II is supported by a grant (MC_PC_19075) from the Medical Research Council; and a grant (MC_PC_19004) from BREATHE–The Health Data Research Hub for Respiratory Health, funded through the UK Research and Innovation Industrial Strategy Challenge Fund. LD was supported by a post-doctoral clinical fellowship from the Asthma UK Centre for Applied Research. SVK acknowledges funding from a NRS Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2) and the Scottish Government Chief Scientist Office (SPHSU17). The authors would like to acknowledge the support of Dave Kelly and Lamorna Brown of Albasoft Ltd., and Sharon Kennedy, Mike Birnie, Safraj Shahul Hameed and Elliott Hall of Public Health Scotland for their involvement in obtaining approvals, provisioning, and linking data and the use of the secure analytical platform within the National Safe Haven. Funding Chief Scientist Office (Scotland), Medical Research Council, and BREATHE.

PY - 2024/5/1

Y1 - 2024/5/1

N2 - Summary Background Long COVID is a debilitating multisystem condition. The objective of this study was to estimate the prevalence of long COVID in the adult population of Scotland, and to identify risk factors associated with its development. Methods In this national, retrospective, observational cohort study, we analysed electronic health records (EHRs) for all adults (≥18 years) registered with a general medical practice and resident in Scotland between March 1, 2020, and October 26, 2022 (98–99% of the population). We linked data from primary care, secondary care, laboratory testing and prescribing. Four outcome measures were used to identify long COVID: clinical codes, free text in primary care records, free text on sick notes, and a novel operational definition. The operational definition was developed using Poisson regression to identify clinical encounters indicative of long COVID from a sample of negative and positive COVID-19 cases matched on time-varying propensity to test positive for SARS-CoV-2. Possible risk factors for long COVID were identified by stratifying descriptive statistics by long COVID status. Findings Of 4,676,390 participants, 81,219 (1.7%) were identified as having long COVID. Clinical codes identified the fewest cases (n = 1,092, 0.02%), followed by free text (n = 8,368, 0.2%), sick notes (n = 14,469, 0.3%), and the operational definition (n = 64,193, 1.4%). There was limited overlap in cases identified by the measures; however, temporal trends and patient characteristics were consistent across measures. Compared with the general population, a higher proportion of people with long COVID were female (65.1% versus 50.4%), aged 38–67 (63.7% versus 48.9%), overweight or obese (45.7% versus 29.4%), had one or more comorbidities (52.7% versus 36.0%), were immunosuppressed (6.9% versus 3.2%), shielding (7.9% versus 3.4%), or hospitalised within 28 days of testing positive (8.8% versus 3.3%%), and had tested positive before Omicron became the dominant variant (44.9% versus 35.9%). The operational definition identified long COVID cases with combinations of clinical encounters (from four symptoms, six investigation types, and seven management strategies) recorded in EHRs within 4–26 weeks of a positive SARS-CoV-2 test. These combinations were significantly (p

AB - Summary Background Long COVID is a debilitating multisystem condition. The objective of this study was to estimate the prevalence of long COVID in the adult population of Scotland, and to identify risk factors associated with its development. Methods In this national, retrospective, observational cohort study, we analysed electronic health records (EHRs) for all adults (≥18 years) registered with a general medical practice and resident in Scotland between March 1, 2020, and October 26, 2022 (98–99% of the population). We linked data from primary care, secondary care, laboratory testing and prescribing. Four outcome measures were used to identify long COVID: clinical codes, free text in primary care records, free text on sick notes, and a novel operational definition. The operational definition was developed using Poisson regression to identify clinical encounters indicative of long COVID from a sample of negative and positive COVID-19 cases matched on time-varying propensity to test positive for SARS-CoV-2. Possible risk factors for long COVID were identified by stratifying descriptive statistics by long COVID status. Findings Of 4,676,390 participants, 81,219 (1.7%) were identified as having long COVID. Clinical codes identified the fewest cases (n = 1,092, 0.02%), followed by free text (n = 8,368, 0.2%), sick notes (n = 14,469, 0.3%), and the operational definition (n = 64,193, 1.4%). There was limited overlap in cases identified by the measures; however, temporal trends and patient characteristics were consistent across measures. Compared with the general population, a higher proportion of people with long COVID were female (65.1% versus 50.4%), aged 38–67 (63.7% versus 48.9%), overweight or obese (45.7% versus 29.4%), had one or more comorbidities (52.7% versus 36.0%), were immunosuppressed (6.9% versus 3.2%), shielding (7.9% versus 3.4%), or hospitalised within 28 days of testing positive (8.8% versus 3.3%%), and had tested positive before Omicron became the dominant variant (44.9% versus 35.9%). The operational definition identified long COVID cases with combinations of clinical encounters (from four symptoms, six investigation types, and seven management strategies) recorded in EHRs within 4–26 weeks of a positive SARS-CoV-2 test. These combinations were significantly (p

KW - Long COVID

KW - Population surveillance

KW - Primary health care

KW - Clinical coding

KW - Matched-pair analysis

U2 - 10.1016/j.eclinm.2024.102590

DO - 10.1016/j.eclinm.2024.102590

M3 - Article

SN - 2589-5370

VL - 71

JO - EClinicalMedicine

JF - EClinicalMedicine

M1 - 102590

ER -

Prevalence and risk factors for long COVID among adults in Scotland using electronic health records: a national, retrospective, observational cohort study

Abstract

Bibliographical note

Data Availability Statement

Keywords

Access to Document

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Datasets

Data From Prevalence and risk factors for long COVID among adults in Scotland using electronic health records: a national, retrospective, observational cohort study

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