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Key predictors of attending hospital with COVID19: An association study from the COVID Symptom Tracker App in 2,618,948 individuals
Mary Ni Lochlainn; Karla A Lee; Carole H Sudre; Thomas Varsavsky; M. Jorge Cardoso; Cristina Menni; Ruth C. E. Bowyer; Long H. Nguyen; David Alden Drew; Sajaysurya Ganesh; Julien Lavigne du Cadet; Alessia Visconti; Maxim B Freydin; Marc Modat; Mark S Graham; Joan Capdevila Pujol; Benjamin Murray; Julia S El-Sayed Moustafa; Xinyuan Zhang; Richard Davies; Mario Falchi; Timothy D Spector; Andrew T Chan; Sebastien Ourselin; Claire J Steves.
Afiliação
  • Mary Ni Lochlainn; Department of Twin Research and Genetic Epidemiology, Kings College London, Westminster Bridge Road, SE17EH London, UK
  • Karla A Lee; Department of Twin Research and Genetic Epidemiology, Kings College London, Westminster Bridge Road, SE17EH London, UK
  • Carole H Sudre; School of Biomedical Engineering & Imaging Sciences, Kings College London, Westminster Bridge Road, SE17EH London, UK
  • Thomas Varsavsky; School of Biomedical Engineering & Imaging Sciences, Kings College London, Westminster Bridge Road, SE17EH London, UK
  • M. Jorge Cardoso; School of Biomedical Engineering & Imaging Sciences, Kings College London, Westminster Bridge Road, SE17EH London, UK
  • Cristina Menni; Department of Twin Research and Genetic Epidemiology, Kings College London, Westminster Bridge Road, SE17EH London, UK
  • Ruth C. E. Bowyer; Department of Twin Research and Genetic Epidemiology, Kings College London, Westminster Bridge Road, SE17EH London, UK
  • Long H. Nguyen; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, MA, USA
  • David Alden Drew; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, MA, USA
  • Sajaysurya Ganesh; Zoe Global Limited,164 Westminster Bridge Road, London SE1 7RW, UK
  • Julien Lavigne du Cadet; Zoe Global Limited,164 Westminster Bridge Road, London SE1 7RW, UK
  • Alessia Visconti; Department of Twin Research and Genetic Epidemiology, Kings College London, Westminster Bridge Road, SE17EH London, UK
  • Maxim B Freydin; Department of Twin Research and Genetic Epidemiology, Kings College London, Westminster Bridge Road, SE17EH London, UK
  • Marc Modat; School of Biomedical Engineering & Imaging Sciences, Kings College London, Westminster Bridge Road, SE17EH London, UK
  • Mark S Graham; School of Biomedical Engineering & Imaging Sciences, Kings College London, Westminster Bridge Road, SE17EH London, UK
  • Joan Capdevila Pujol; Zoe Global Limited,164 Westminster Bridge Road, London SE1 7RW, UK
  • Benjamin Murray; School of Biomedical Engineering & Imaging Sciences, Kings College London, Westminster Bridge Road, SE17EH London, UK
  • Julia S El-Sayed Moustafa; Department of Twin Research and Genetic Epidemiology, Kings College London, Westminster Bridge Road, SE17EH London, UK
  • Xinyuan Zhang; Department of Twin Research and Genetic Epidemiology, Kings College London, Westminster Bridge Road, SE17EH London, UK
  • Richard Davies; Zoe Global Limited,164 Westminster Bridge Road, London SE1 7RW, UK
  • Mario Falchi; Department of Twin Research and Genetic Epidemiology, Kings College London, Westminster Bridge Road, SE17EH London, UK
  • Timothy D Spector; Department of Twin Research and Genetic Epidemiology, Kings College London, Westminster Bridge Road, SE17EH London, UK
  • Andrew T Chan; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, MA, USA
  • Sebastien Ourselin; School of Biomedical Engineering & Imaging Sciences, Kings College London, Westminster Bridge Road, SE17EH London, UK
  • Claire J Steves; Department of Twin Research and Genetic Epidemiology, Kings College London, Westminster Bridge Road, SE17EH London, UK
Preprint em Inglês | medRxiv | ID: ppmedrxiv-20079251
ABSTRACT
ObjectivesWe aimed to identify key demographic risk factors for hospital attendance with COVID-19 infection. DesignCommunity survey SettingThe COVID Symptom Tracker mobile application co-developed by physicians and scientists at Kings College London, Massachusetts General Hospital, Boston and Zoe Global Limited was launched in the UK and US on 24th and 29th March 2020 respectively. It captured self-reported information related to COVID-19 symptoms and testing. Participants2,618,948 users of the COVID Symptom Tracker App. UK (95.7%) and US (4.3%) population. Data cut-off for this analysis was 21st April 2020. Main outcome measuresVisit to hospital and for those who attended hospital, the need for respiratory support in three subgroups (i) self-reported COVID-19 infection with classical symptoms (SR-COVID-19), (ii) selfreported positive COVID-19 test results (T-COVID-19), and (iii) imputed/predicted COVID-19 infection based on symptomatology (I-COVID-19). Multivariate logistic regressions for each outcome and each subgroup were adjusted for age and gender, with sensitivity analyses adjusted for comorbidities. Classical symptoms were defined as high fever and persistent cough for several days. ResultsOlder age and all comorbidities tested were found to be associated with increased odds of requiring hospital care for COVID-19. Obesity (BMI >30) predicted hospital care in all models, with odds ratios (OR) varying from 1.20 [1.11; 1.31] to 1.40 [1.23; 1.60] across population groups. Pre-existing lung disease and diabetes were consistently found to be associated with hospital visit with a maximum OR of 1.79 [1.64,1.95] and 1.72 [1.27; 2.31]) respectively. Findings were similar when assessing the need for respiratory support, for which age and male gender played an additional role. ConclusionsBeing older, obese, diabetic or suffering from pre-existing lung, heart or renal disease placed participants at increased risk of visiting hospital with COVID-19. It is of utmost importance for governments and the scientific and medical communities to work together to find evidence-based means of protecting those deemed most vulnerable from COVID-19. Trial registrationThe App Ethics have been approved by KCL ethics Committee REMAS ID 18210, review reference LRS-19/20-18210
Licença
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Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Experimental_studies / Estudo observacional / Estudo prognóstico / Rct Idioma: Inglês Ano de publicação: 2020 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Experimental_studies / Estudo observacional / Estudo prognóstico / Rct Idioma: Inglês Ano de publicação: 2020 Tipo de documento: Preprint
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