Frequently Asked Questions

What is the QCovid® score?

The QCovid® score is a risk score given by an evidence-based model which uses a range of factors such as age, sex, ethnicity and existing medical conditions to predict risk of death or hospitalisation from COVID-19.

It provides nuanced information on an individual's risk of serious illness due to COVID-19 to inform understanding of risk.

It is a 'living' risk prediction model which will be updated regularly as our understanding of COVID-19 increases and more data and interventions become available.

Why has it been developed?

QCovid® was produced to provide doctors and the public with more nuanced information about risk of serious illness due to COVID-19, and to help patients and doctors reach a shared understanding of risk, within the context of individual circumstances, risk attitude and the sorts of preventative measures people can take in their daily lives.

Approximately 2.2 million people with pre-existing medical conditions thought to be the most serious, such as organ transplant recipients and people with severe respiratory conditions, were deemed 'clinically extremely vulnerable' during the first wave of the COVID-19 pandemic in England. These individuals were advised to shield themselves from the virus by staying at home.

Throughout the pandemic, new information has emerged about what factors may influence whether someone is likely to be seriously affected by COVID-19, creating an opportunity to develop this detailed QCovid® risk prediction model.

Who can use the QCovid® calculator?

At present, the QCovid4 model is not a registered medical device so it cannot be used by patients or clinicians for any diagnosis, prevention, monitoring, treatment or alleviation of disease. The model is made available for academic research and peer review purposes only. If you wish to discuss use of the tool for clinical or commercial purposes, please email [email protected] with 'QCovid' in the subject line.

Who developed it?

The research team is led by Professor Julia Hippisley-Cox at the University of Oxford and includes researchers from the universities of Cambridge, Edinburgh, Swansea, Leicester, Nottingham and Liverpool with the London School of Hygiene & Tropical Medicine, Queen's University Belfast, Queen Mary University of London, University College London, the Department of Health and Social Care, NHS Digital and NHS England.

How has it been developed?

QCovid® has been developed using the University of Oxford hosted QResearch database which has anonymised data from primary care, hospitals, COVID-19 test results and death registries. This was used to determine which factors were associated with poor outcomes during the first wave of COVID-19 and create a risk prediction model - QCovid® - that provides a weighted, cumulative calculation of absolute risk using the variables associated with poor COVID-19 outcomes. The factors incorporated in the model include age, 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 data, one from January to April 2020 and one 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, published in the BMJ, showed that the model performed well in predicting severe outcomes due to COVID-19 (death and hospitalisation).

New models have then been developed to take account of the second, third and fourth waves of the pandemic and the introduction of widespread testing and COVID-19 vaccinations. The QCOVID2 model is for unvaccinated people and the QCOVID3 model for vaccinated people. The QCOVID4 model was developed during the ‘Omicron’ waves and can be used by vaccinated and unvaccinated people. All the models have been tested and performed well.

Who funded this research?

The research was funded by the NIHR following a commission by the Chief Medical Officer for England. See press release here.

What does the absolute risk of a COVID-19 death mean and why is it important?

This is the risk of someone catching and then dying from COVID-19 over a certain time period such as a 90-day period. If someone has a risk of 2% then in a crowd of 100 people like them, on average 2 people would catch and die from COVID-19 within 90 days. Or put another way, they have a 'one in fifty chance of catching and dying from COVID-19 in the next 90 days. If someone has a risk of 0.02% then in a crowd of 10,000 people like them, on average 2 people would catch and die from COVID-19 within 90 days.

What is the relative risk measure?

The relative risk is the comparison between an individual's risk and the average risk of a person of the same age and sex, and with a white ethnicity and a body mass index of 25 but without any risk factors such as medical conditions. It is a ratio of the two risks, so if someone has a relative risk of 2 for COVID-19 death that means their risk is twice as high as someone of the same age and sex as them but without other risk factors. We compare against a person with White ethnicity in order to highlight the differences in risk between different ethnicities, rather than comparing individuals within their own ethnic group.

Ranking a person’s risk

A rank is often a helpful measure alongside absolute risk, as the absolute risk will change when infection levels change over time but the rank is a more stable measure for identifying people at highest risk. If we put the whole population in a line, ordered from low to high according to their risk of COVID-19 death, and then divide this line into 100 equally sized groups, the person's score places them into a particular group.

Their rank is the group into which they fall.

Please note that ranks are not percentages. They show where the person is in the line if we put everyone in order of risk, with 1 at the bottom being the 1% of people with the lowest risks and with 100, at the top, being the 1% of people most at risk.

Which people can have a QCovid® score calculated?

The calculator can be used for people aged between 18 and 100 years.

Can the QCovid models be used together to determine how effective vaccines are?

No – the models haven’t been designed for this purpose (a different type of study would be needed). Therefore it wouldn’t be valid to use them to assess vaccine effectiveness.

What is the Townsend score, what does it measure and why was it used?

COVID-19 risk is associated with socio-economic status such that people from deprived areas have higher risks. The use of equations that do not include a measure of socio-economic status may exacerbate health inequalities, i.e. the difference between rich and poor.

The Townsend score is a measure of material deprivation based on the area where a person lives and obtained using their postcode. It includes four variables obtained from census data: unemployment (lack of material resources and insecurity), overcrowding (material living conditions), lack of owner-occupied accommodation (a proxy indicator of wealth) and lack of car ownership (a proxy indicator of income).

This score is considered the best indicator of material deprivation currently available and has been widely used in medical research including a range of studies conducted on the QResearch database.

What is body mass index and how is it measured?

Body mass index is a number calculated from a person's height and weight. It is their weight in kilograms divided by their height in metres squared.

Conventionally a person is considered to be obese if they have a body mass index over 30 kg/m2.

Which immunosuppressants are included here?

These are medicines which fall within chapter 8.2 of the British National Formulary for example see here.

Which chemotherapy drugs are in each categories A, B and C?

See the table of Chemotherapy Groups.

For further information, please see Supplementary Box A of the BMJ paper. Note that Supplementary Box A can be found in the Data Supplement, which is accessible under Related Content on the BMJ website.