QCovid® is a coronavirus risk prediction model, created by the University of Oxford, which we're using to support the NHS coronavirus response.
QCovid® is an evidence-based risk prediction model that estimates a person's combined risk of:
catching coronavirus and being admitted to hospital
catching coronavirus and dying
How QCovid® was developed
The Chief Medical Officer for England asked leading academics, clinicians and scientists to create a way of predicting who may be at high risk of serious illness if they catch coronavirus.
A team of researchers, led by the University of Oxford, studied the anonymised health records of more than 8 million people using GP records, hospital records and mortality data from late January 2020 to April 2020. This initial analysis, funded by the National Institute for Health Research, was done using data collected during the first wave of the coronavirus pandemic in the United Kingdom.
The results showed that things such as age, sex assigned at birth, height and weight (used to calculate body mass index (BMI)), ethnicity and some medical conditions increased risk relating to coronavirus – these are known as risk factors.
They then used this information to create QCovid®. This estimates someone’s combined risk of catching coronavirus and being admitted to hospital and their combined risk of catching coronavirus and dying.
QCovid® was designed to:
risk assess the general population
inform people about their risk level
support people with decisions about behaviours in consultation with a clinician
How QCovid® works
QCovid® works by taking information about risk factors and converting each of these into values. These values are then combined in an equation that estimates risk and generates figures for absolute risk and relative risk.
Absolute risk is the overall risk, based on what happened to other people with the same characteristics and risk factors who caught coronavirus and went to hospital or died as a result.
Relative risk is the level of risk compared to a person who is the same age and sex registered at birth, but without any other risk factors.
To reflect the fact that some risk factors have a bigger impact on risk than others, some values contribute more to the result than others (weighting). The weighting of some values can be affected by the presence or absence of other factors. For example, the risk associated with Type 2 diabetes increases with age.
List of QCovid® risk factors in approximate order of weighting
The risk factors below contribute to coronavirus risk and are used by the QCovid® model. The list is provided in an approximate order of their impact on coronavirus outcomes (greatest first). However, it is important to note that:
QCovid® is a complex model
some risk factors interact with others in different ways
the impact of some risk factors increases with their severity - for example, a higher level of obesity means a higher risk
some risk factors affect men more than women, and vice versa
the model is currently based on data collected during the 97-day period of the first wave of the pandemic in the UK (January – April 2020)
As with any model like this, QCovid® can only estimate risk and cannot take all factors into account. There are several things that are important to consider that are not included in QCovid®, such as:
an individual’s behaviour (for example hand washing, wearing face coverings and visiting friends or family)
local and national lockdown measures
We also do not yet know whether having had coronavirus previously or being vaccinated affects the accuracy of the risk assessment results, because the data used to develop QCovid® was from the first wave of the pandemic.
How QCovid® has been validated
QCovid® has been peer reviewed, which means independent academic experts have checked that the research is robust.
Although the data used to develop the current QCovid® model was collected in early 2020, it has since been tested with new data and continues to perform well and accurately predict outcomes.
The Office for National Statistics (ONS) has independently validated the performance of QCovid®. The ONS has shown that the model performs well and accurately identifies patients at high risk from coronavirus. The NHS can therefore be confident that the model is robust and meets the highest standards of evidence.
QCovid® is a ‘living’ risk prediction model. This means that, although it is not updated automatically in real-time, it can be updated periodically by the University of Oxford using the latest data and as we learn more about coronavirus.
This will help to ensure it is still accurate and relevant and establish what changes may need to be made to the COVID-19 Clinical Risk Assessment Tool.