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

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

Journal

EClinicalMedicine
ISSN: 2589-5370
Titre abrégé: EClinicalMedicine
Pays: England
ID NLM: 101733727

Informations de publication

Date de publication:
May 2024
Historique:
received: 14 11 2023
revised: 07 03 2024
accepted: 21 03 2024
medline: 16 4 2024
pubmed: 16 4 2024
entrez: 16 4 2024
Statut: epublish

Résumé

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. 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. 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 < 0.0001) more prevalent in positive COVID-19 patients than in matched negative controls. In a case-crossover analysis, 16.4% of those identified by the operational definition had similar healthcare patterns recorded before testing positive. The prevalence of long COVID presenting in general practice was estimated to be 0.02-1.7%, depending on the measure used. Due to challenges in diagnosing long COVID and inconsistent recording of information in EHRs, the true prevalence of long COVID is likely to be higher. The operational definition provided a novel approach but relied on a restricted set of symptoms and may misclassify individuals with pre-existing health conditions. Further research is needed to refine and validate this approach. Chief Scientist Office (Scotland), Medical Research Council, and BREATHE.

Sections du résumé

Background UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
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 < 0.0001) more prevalent in positive COVID-19 patients than in matched negative controls. In a case-crossover analysis, 16.4% of those identified by the operational definition had similar healthcare patterns recorded before testing positive.
Interpretation UNASSIGNED
The prevalence of long COVID presenting in general practice was estimated to be 0.02-1.7%, depending on the measure used. Due to challenges in diagnosing long COVID and inconsistent recording of information in EHRs, the true prevalence of long COVID is likely to be higher. The operational definition provided a novel approach but relied on a restricted set of symptoms and may misclassify individuals with pre-existing health conditions. Further research is needed to refine and validate this approach.
Funding UNASSIGNED
Chief Scientist Office (Scotland), Medical Research Council, and BREATHE.

Identifiants

pubmed: 38623399
doi: 10.1016/j.eclinm.2024.102590
pii: S2589-5370(24)00169-X
pmc: PMC11016856
doi:

Types de publication

Journal Article

Langues

eng

Pagination

102590

Informations de copyright

© 2024 Published by Elsevier Ltd.

Déclaration de conflit d'intérêts

AS reports grants from HDRUK, NIHR, MRC, ICSF, and CSO during the conduct of the study; and being a Member of the Scottish Government's CMO COVID-19 Advisory Group and Standing Committee on Pandemics. CR reports support from PHS, CSO and MRC; and being a Member of SPI-M, Scottish Government Scientific Advisory Committee, MHRA Covid vaccine benefit and risk expert working group. CS reports grants from MBIE (New Zealand), Ministry of Health (New Zealand), and HRC (New Zealand). JKQ reports grants from MRC, HDR UK, GlaxoSmithKline, BI, Asthma + Lung UK, and AstraZeneca and consulting fees from GlaxoSmithKline, Evidera, AstraZeneca, Insmed. SVK reports grants from CSO and MRC. All other authors declare no competing interests.

Auteurs

Karen Jeffrey (K)

Usher Institute, University of Edinburgh, Edinburgh, UK.

Lana Woolford (L)

Usher Institute, University of Edinburgh, Edinburgh, UK.

Rishma Maini (R)

Public Health Scotland, Glasgow and Edinburgh, UK.

Siddharth Basetti (S)

NHS Highland, Inverness, UK.

Ashleigh Batchelor (A)

Patient and Public Contributors, Usher Institute, University of Edinburgh, Edinburgh, UK.

David Weatherill (D)

Patient and Public Contributors, Usher Institute, University of Edinburgh, Edinburgh, UK.

Chris White (C)

Patient and Public Contributors, Usher Institute, University of Edinburgh, Edinburgh, UK.

Vicky Hammersley (V)

Usher Institute, University of Edinburgh, Edinburgh, UK.

Tristan Millington (T)

Usher Institute, University of Edinburgh, Edinburgh, UK.

Calum Macdonald (C)

Usher Institute, University of Edinburgh, Edinburgh, UK.

Jennifer K Quint (JK)

National Heart and Lung Institute, Imperial College London, London, UK.

Robin Kerr (R)

NHS Borders, Melrose, UK.
NHS Dumfries & Galloway, Dumfries, UK.

Steven Kerr (S)

Usher Institute, University of Edinburgh, Edinburgh, UK.

Syed Ahmar Shah (SA)

Usher Institute, University of Edinburgh, Edinburgh, UK.

Igor Rudan (I)

Usher Institute, University of Edinburgh, Edinburgh, UK.

Adeniyi Francis Fagbamigbe (AF)

Institute of Applied Health Sciences, University of Aberdeen, UK.

Colin R Simpson (CR)

Usher Institute, University of Edinburgh, Edinburgh, UK.
School of Health, Wellington Faculty of Health, Victoria University of Wellington, Wellington, NZ.

Srinivasa Vittal Katikireddi (SV)

Public Health Scotland, Glasgow and Edinburgh, UK.
MRC/CSO Social & Public Health Sciences Unit, University of Glasgow, Glasgow, UK.

Chris Robertson (C)

Public Health Scotland, Glasgow and Edinburgh, UK.
Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK.

Lewis Ritchie (L)

Academic Primary Care, University of Aberdeen, Aberdeen, UK.
Institute of Applied Health Sciences, University of Aberdeen, UK.

Aziz Sheikh (A)

Usher Institute, University of Edinburgh, Edinburgh, UK.

Luke Daines (L)

Usher Institute, University of Edinburgh, Edinburgh, UK.

Classifications MeSH