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Maria Gomez

Maria Gomez

Professor

Maria Gomez

Attributes and predictors of long COVID

Author

  • Carole H. Sudre
  • Benjamin Murray
  • Thomas Varsavsky
  • Mark S. Graham
  • Rose S. Penfold
  • Ruth C. Bowyer
  • Joan Capdevila Pujol
  • Kerstin Klaser
  • Michela Antonelli
  • Liane S. Canas
  • Erika Molteni
  • Marc Modat
  • M. Jorge Cardoso
  • Anna May
  • Sajaysurya Ganesh
  • Richard Davies
  • Long H. Nguyen
  • David A. Drew
  • Christina M. Astley
  • Amit D. Joshi
  • Jordi Merino
  • Neli Tsereteli
  • Tove Fall
  • Maria F. Gomez
  • Emma L. Duncan
  • Cristina Menni
  • Frances M.K. Williams
  • Paul W. Franks
  • Andrew T. Chan
  • Jonathan Wolf
  • Sebastien Ourselin
  • Tim Spector
  • Claire J. Steves

Summary, in English

Reports of long-lasting coronavirus disease 2019 (COVID-19) symptoms, the so-called ‘long COVID’, are rising but little is known about prevalence, risk factors or whether it is possible to predict a protracted course early in the disease. We analyzed data from 4,182 incident cases of COVID-19 in which individuals self-reported their symptoms prospectively in the COVID Symptom Study app1. A total of 558 (13.3%) participants reported symptoms lasting ≥28 days, 189 (4.5%) for ≥8 weeks and 95 (2.3%) for ≥12 weeks. Long COVID was characterized by symptoms of fatigue, headache, dyspnea and anosmia and was more likely with increasing age and body mass index and female sex. Experiencing more than five symptoms during the first week of illness was associated with long COVID (odds ratio = 3.53 (2.76–4.50)). A simple model to distinguish between short COVID and long COVID at 7 days (total sample size, n = 2,149) showed an area under the curve of the receiver operating characteristic curve of 76%, with replication in an independent sample of 2,472 individuals who were positive for severe acute respiratory syndrome coronavirus 2. This model could be used to identify individuals at risk of long COVID for trials of prevention or treatment and to plan education and rehabilitation services.

Department/s

  • Genetic and Molecular Epidemiology
  • EXODIAB: Excellence in Diabetes Research in Sweden
  • Diabetic Complications
  • EpiHealth: Epidemiology for Health

Publishing year

2021-04-01

Language

English

Pages

626-631

Publication/Series

Nature Medicine

Volume

27

Issue

4

Document type

Journal article

Publisher

Nature Publishing Group

Topic

  • Infectious Medicine

Status

Published

Research group

  • Genetic and Molecular Epidemiology
  • Diabetic Complications

ISBN/ISSN/Other

  • ISSN: 1078-8956