Details

Autor(en) / Beteiligte

• Alasdair D. Henderson
• Ben FC. Butler-Cole
• John Tazare
• Laurie A. Tomlinson
• Michael Marks
• Mark Jit
• Andrew Briggs
• Liang-Yu Lin
• Oliver Carlile
• Chris Bates
• Sebastian CJ. Bacon
• William A. Dennison
• Ruth E. Costello
• Yinghui Wei
• Alex J. Walker
• William Hulme
• Ben Goldacre
• Amir Mehrkar
• Brian MacKenna
• Emily Herrett
• Rosalind M. Eggo
• Alex Walker
• Amelia Green
• Andrea Schaffer
• Andrew Brown
• Ben Butler-Cole
• Caroline Morton
• Caroline Walters
• Catherine Stables
• Christine Cunningham
• Christopher Wood
• Colm Andrews
• David Evans
• George Hickman
• Helen Curtis
• Henry Drysdale
• Iain Dillingham
• Jessica Morley
• Jon Massey
• Linda Nab
• Lisa Hopcroft
• Louis Fisher
• Lucy Bridges
• Milan Wiedemann
• Nicholas DeVito
• Orla Macdonald
• Peter Inglesby
• Rebecca Smith
• Richard Croker
• Rose Higgins
• Sebastian Bacon
• Simon Davy
• Steven Maude
• Thomas O'Dwyer
• Victoria Speed
• Liam Hart
• Pete Stokes
• Krishnan Bhaskaran
• Ruth Costello
• Thomas Cowling
• Ian Douglas
• Rosalind Eggo
• Stephen Evans
• Richard Grieve
• Sinead Langan
• Viyaasan Mahalingasivam
• Kathryn Mansfield
• Rohini Mathur
• Helen McDonald
• Edward Parker
• Christopher Rentsch
• Anna Schultze
• Liam Smeeth
• Laurie Tomlinson
• Jemma Walker
• Elizabeth Williamson
• Kevin Wing
• Angel Wong
• Christopher Bates
• John Parry
• Frank Hester
• Sam Harper
• Shaun O'Hanlon
• Alex Eavis
• Richard Jarvis
• Dima Avramov
• Paul Griffiths
• Aaron Fowles
• Nasreen Parkes
• Rafael Perera
• David Harrison
• Kamlesh Khunti
• Jonathan Sterne
• Jennifer Quint

Titel

Clinical coding of long COVID in primary care 2020–2023 in a cohort of 19 million adults: an OpenSAFELY analysisResearch in context

Ist Teil von

• EClinicalMedicine, 2024-06, Vol.72, p.102638

Ort / Verlag

Elsevier

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Background: Long COVID is the patient-coined term for the persistent symptoms of COVID-19 illness for weeks, months or years following the acute infection. There is a large burden of long COVID globally from self-reported data, but the epidemiology, causes and treatments remain poorly understood. Primary care is used to help identify and treat patients with long COVID and therefore Electronic Health Records (EHRs) of past COVID-19 patients could be used to help fill these knowledge gaps. We aimed to describe the incidence and differences in demographic and clinical characteristics in recorded long COVID in primary care records in England. Methods: With the approval of NHS England we used routine clinical data from over 19 million adults in England linked to SARS-COV-2 test result, hospitalisation and vaccination data to describe trends in the recording of 16 clinical codes related to long COVID between November 2020 and January 2023. Using OpenSAFELY, we calculated rates per 100,000 person-years and plotted how these changed over time. We compared crude and adjusted (for age, sex, 9 NHS regions of England, and the dominant variant circulating) rates of recorded long COVID in patient records between different key demographic and vaccination characteristics using negative binomial models. Findings: We identified a total of 55,465 people recorded to have long COVID over the study period, which included 20,025 diagnoses codes and 35,440 codes for further assessment. The incidence of new long COVID records increased steadily over 2021, and declined over 2022. The overall rate per 100,000 person-years was 177.5 cases in women (95% CI: 175.5–179) and 100.5 in men (99.5–102). The majority of those with a long COVID record did not have a recorded positive SARS-COV-2 test 12 or more weeks before the long COVID record. Interpretation: In this descriptive study, EHR recorded long COVID was very low between 2020 and 2023, and incident records of long COVID declined over 2022. Using EHR diagnostic or referral codes unfortunately has major limitations in identifying and ascertaining true cases and timing of long COVID. Funding: This research was supported by the National Institute for Health and Care Research (NIHR) (OpenPROMPT: COV-LT2-0073).