Back to Search Start Over

Key predictors of attending hospital with COVID19: An association study from the COVID Symptom Tracker App in 2,618,948 individuals

Authors :
Mark S. Graham
Sajaysurya Ganesh
Julien Lavigne du Cadet
Benjamin J. Murray
Jonathan Wolf
Richard Davies
Julia S. El-Sayed Moustafa
Long H. Nguyen
Ruth C. E. Bowyer
David A. Drew
Marc Modat
Tim D. Spector
Andrew T. Chan
Alessia Visconti
Mary Ni Lochlainn
Karla A. Lee
Cristina Menni
Maxim B. Freidin
Thomas Varsavsky
M. Jorge Cardoso
Joan Capdevila Pujol
Claire J. Steves
Sebastien Ourselin
Xinyuan Zhang
Carole H. Sudre
Mario Falchi
Publication Year :
2020
Publisher :
Cold Spring Harbor Laboratory, 2020.

Abstract

ObjectivesWe aimed to identify key demographic risk factors for hospital attendance with COVID-19 infection.DesignCommunity surveySettingThe COVID Symptom Tracker mobile application co-developed by physicians and scientists at King’s College London, Massachusetts General Hospital, Boston and Zoe Global Limited was launched in the UK and US on 24thand 29thMarch 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 21stApril 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

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....d7563a3622693b4f806113105969fdcb
Full Text :
https://doi.org/10.1101/2020.04.25.20079251