New risk model estimates likelihood of death or hospitalisation from COVID-19
21 October 2020
A model that can calculate a person's risk of becoming infected and then seriously ill due to COVID-19 has been shown to accurately estimate risk during the first wave of the pandemic in England, in new research involving Queen Mary University of London.
Queen Mary researchers will also be supporting the implementation of the new risk model in east London General Practices
The model - 'QCovid' – was developed using anonymised data from more than 8 million adults in 1,205 general practices across England, and uses a number of factors such as a person's age, ethnicity and existing medical conditions to predict their risk of catching COVID-19 and then dying or being admitted to hospital. This has the potential to provide doctors and the public with more nuanced information about risk of serious illness due to COVID-19.
Queen Mary's Clinical Effectiveness Group (CEG) is supporting local GP teams in east London with disease management during the Covid pandemic. As part of this Dr John Robson, CEG clinical lead, has been part of the collaboration team who have produced QCovid.
Dr John Robson, Clinical Reader in Primary Care Research & Development & Turing Fellow, from Queen Mary's Institute of Population Health Sciences said: "East London has had some of the highest levels of Covid-19 related deaths and hospital admissions. Our Clinical Effectiveness Group will be supporting the implementation of QCovid in east London General Practices which cover a population of around 2 million. This will assist in shielding patients, managing their current diseases and when a vaccine is ready, will assist in prioritising vaccination programmes."
The study involving researchers from across the UK and funded by the National Institute for Health Research (NIHR) used anonymous data from primary care, hospitals, COVID-19 test results and death registries to determine which factors were associated with poor outcomes during the first wave of COVID-19.
The factors incorporated in the QCovid model include age, sex, ethnicity, level of deprivation, obesity, whether someone lived in residential care or was homeless, and a range of existing medical conditions, such as cardiovascular disease, diabetes, respiratory disease and cancer.
This model was then tested in two independent sets of anonymised data, from January to April 2020 and from May 2020 to June 2020, to find out whether it accurately predicted severe outcomes due to COVID-19 during the first wave of the pandemic in England.
The research results, published in The BMJ, showed that the model performed well in predicting outcomes. People in the dataset whose calculated risk put them in the top 20 per cent of predicted risk of death accounted for 94 per cent of deaths from COVID-19.
The research was funded by the NIHR following a commission by the Chief Medical Officer for England.
Deputy Chief Medical Officer for England Dr Jenny Harries said: "Continuing to improve our understanding of the virus and how it affects different members of the population is vital as prevalence continues to rise. This is why we commissioned and funded this research, and I'm pleased it is providing useful evidence to help us move towards a more nuanced understanding of COVID-19 risk."
The research team is led by the University of Oxford and includes researchers from the universities of Queen Mary University of London, Cambridge, Edinburgh, Swansea, Leicester, Nottingham and Liverpool with the London School of Hygiene & Tropical Medicine, Queen's University Belfast, University College London, the Department of Health and Social Care, NHS Digital and NHS England.
The paper can be accessed here: 'Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study' by Clift et al. The BMJ.
Updated by: Michal Filus