Key predictors of attending hospital with COVID19: An association study from the COVID Symptom Tracker App in 2,618,948 individuals
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Abstract
Objectives
We aimed to identify key demographic risk factors for hospital attendance with COVID-19 infection.
Design
Community survey
Setting
The 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 24 th and 29 th March 2020 respectively. It captured self-reported information related to COVID-19 symptoms and testing.
Participants
2,618,948 users of the COVID Symptom Tracker App. UK (95.7%) and US (4.3%) population. Data cut-off for this analysis was 21 st April 2020.
Main outcome measures
Visit 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.
Results
Older 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.
Conclusions
Being 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 registration
The App Ethics have been approved by KCL ethics Committee REMAS ID 18210, review reference LRS-19/20-18210
Article activity feed
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SciScore for 10.1101/2020.04.25.20079251: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has a number of limitations. First, all the data collected is self-reported, and questions on comorbidities were somewhat simplified to ease reporting at large scale on an app. Both symptoms and test results may be …
SciScore for 10.1101/2020.04.25.20079251: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has a number of limitations. First, all the data collected is self-reported, and questions on comorbidities were somewhat simplified to ease reporting at large scale on an app. Both symptoms and test results may be subject to reporting bias. Secondly, the sampling using an app will under-represent individuals without smartphone devices, including older participants, and is likely to under-represent those severely affected by the disease. Additionally, we are reporting visits, rather than admissions, to hospital; we do not know how many visits resulted in an inpatient stay. While we believe that our sampling provides useful information about the risk of most symptomatic infection, it will not provide insight into very severe disease as the most unwell patients may not record hospitalisation due to incapacitation or even death. Additionally, COVID-19 diagnoses, where confirmed by testing, were likely to be based on RT-PCR which is thought to be between 66-80% sensitive for a single test (46,47). Another important caveat of note is that the individuals on which the model was trained are highly selected because COVID-19 tests are not performed at random (48). The participants were tested because they either displayed severe symptoms, were in contact with COVID-19 positive individuals, were healthcare workers or had travelled to an area of particular risk. Additionally, the app captured whether participants had been diagnosed with COVID-19 but did not specifically ask wh...
Results from TrialIdentifier: We found the following clinical trial numbers in your paper:
Identifier Status Title NCT04331509 Recruiting COVID-19 Symptom Tracker Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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