Research led by Professor Julia Hippisley-Cox in the University of Oxford’s Nuffield Department of Primary Care Health Sciences, with collaborators across the UK, found that there are several health and personal factors which, when combined, could mean someone is at a higher risk from COVID-19. These include characteristics like age, ethnicity and BMI, as well as certain medical conditions and treatments.
The team turned their research into a risk prediction model called QCovid , which has been independently validated by the Office for National Statistics. It is thought to be the only COVID-19 risk prediction model in the world to meet the highest standards of evidence.
Details of the development and validation of the tool were published in the BMJ, and the model has been fully published for transparency at www.qcovid.org.
NHS Digital have now used this model to develop a population risk assessment. The risk assessment predicts on a population basis whether registered patients with a combination of risk factors may be at more serious risk from COVID-19, enabling the government to prioritise them for vaccination, and provide appropriate advice and support. These individuals will be added to the Shielded Patient List on a precautionary basis and to enable rapid vaccination.
This assessment is made possible for the first time by utilising the QCovid model from the Oxford-led team and emerging evidence about the impact of Covid-19 on different groups and who could be most vulnerable, which means further steps can be taken to protect those most at risk.
Up to 1.5 million patients have been identified to date. Approximately 700,000 will have already been vaccinated as part of the over-70s cohort, and an additional 800,000 adults between 19 and 69 years will now be prioritised for a vaccination.
Professor Julia Hippisley-Cox, Professor of Clinical Epidemiology and General Practice in the University of Oxford’s Nuffield Department of Primary Care Health Sciences said, ’The QCovid model, which has been developed using anonymised data from more than 8 million adults, provides nuanced assessment of risk by taking into account a number of different factors that are cumulatively used to estimate risk including ethnicity. The research to develop and validate the model is published in the British Medical Journal along with the underlying model for transparency. This will be updated to take account of new information as the pandemic progresses. I’m delighted that less than a year after being funded by the NIHR, the model is now being used to help protect people at most risk from COVID-19.’
Fred Kemp, Deputy Head of Life Sciences at Oxford University Innovation, said, ’As a further example of how the University of Oxford is at the forefront of combatting the pandemic, OUI is proud to have supported the development and implementation of QCovid as a highly validated, evidence-based risk prediction tool that will enable prioritised delivery of vaccines to those most in need.’
Deputy Chief Medical Officer for England Dr Jenny Harries said, ’For the first time, we are able to go even further in protecting the most vulnerable in our communities. This new model is a tribute to our health and technology researchers. The model’s data-driven approach to medical risk assessment will help the NHS identify further individuals who may be at high risk from COVID-19 due to a combination of personal and health factors. This action ensures those most vulnerable to COVID-19 can benefit from both the protection that vaccines provide, and from enhanced advice, including shielding and support, if they choose it.’
QCovid was developed using the QResearch database of anonymised electronic health records, a collaboration between Professor Julia Hippisley-Cox’s team in Oxford and primary are computer systems provider EMIS Health. The model included data from primary care, hospitals, COVID-19 test results and death registries, and was informed by a significant amount of patient engagement. It is the
latest in a series of risk prediction models developed through the collaboration, which are widely used by healthcare practitioners to identify patients at risk of serious illness including cardiovascular disease, stroke, cancer and diabetes.
Commenting on the roll-out, Dr Shaun O’Hanlon, Chief Medical Officer at EMIS, said, ’EMIS is proud to have supported this important piece of research, which will enable the NHS to protect more vulnerable people, more quickly, from COVID-19. We thank all of the GP practices who have contributed anonymised patient data to the QResearch database in the 15 years-plus it has been in existence.’
The independent validation from the Office of National Statistics is considered the ’gold standard’ in quality assurance. The ONS has shown that the model performs in the ’excellent’ range, and accurately identifies patients at highest risk from COVID-19. This shows the model is robust and meets the highest standards of evidence.
The development of the QCovid model involved researchers from the universities of Oxford, Cambridge, Edinburgh, Swansea, Leicester, Nottingham and Liverpool with the London School of Hygiene & Tropical Medicine, Queen’s University Belfast, Queen Mary University of London and University College London. It was supported by the NIHR Oxford Biomedical Research Centre.
In related work from the University of Edinburgh, the QCovid model has been validated for use in the Scottish population.