Their health data study has revealed that having an underlying health condition, such as heart disease or diabetes, increases a person’s risk of death fivefold over the next year. The team have developed a prototype online ‘risk calculator’ to show how age, sex and underlying health conditions can affect mortality rates.

The Challenge

It is not known what the medical, societal, and economic impact of the COVID-19 pandemic will have on population mortality, with previous mortality rates based on deaths among infected people, nearly all of whom have had underlying conditions. Models have not incorporated information on those with high-risk conditions or their pre-COVID-19 mortality.

With COVID-19 having rapidly become a global pandemic, and as confirmed cases continue to grow, there is an urgent need to understand who is most at risk, and how to limit its impact.

The Solution

In a population-based cohort study, published in the Lancet, the team linked primary and secondary care electronic health records from England, and reported the prevalence of underlying conditions defined by Public Health England using validated, openly available phenotypes for each condition.

The study found that for adults with no underlying health conditions, their pre-COVID-19 one-year mortality risk is 0.63%, compared to 3.51% in those with one underlying condition (5.57-fold increase); and 7.5% in those with two underlying conditions. The researchers modelled the additional effect of the pandemic on top of these background risks.

They estimated the excess number of deaths over one year under different COVID-19 incidence scenarios, based on varying levels of transmission suppression and differing mortality impacts based on different relative risks for the disease.

Overall, the team have estimated between 37,000 and 73,000 excess deaths over the next year as a result of the COVID-19 emergency, based on the population in England having a 10% infection rate and 20% having at least one high-risk underlying condition.

Impact and Outcomes

The team have developed a prototype online risk calculator – called OurRisk.CoV – showing how age, sex and underlying health conditions can affect mortality rates depending on varying rates of infection within the population, and varying overall impact of the COVID-19 pandemic.

The risk is based on both the direct effects of viral infection (e.g. how deadly it is) as well as indirect effects (e.g. health system strain). For example, a 66-year-old man with chronic obstructive pulmonary disease, a lung condition that cause breathing difficulties, has a 6.39% risk of dying over the next year, according to the calculator. With 25,641 people in England in this subgroup, it predicts 1639 baseline deaths over one year. If the infection rate is set to 10%, and the impact of the COVID-19 emergency to a doubling of the baseline risk of mortality, it estimates 164 excess COVID-related deaths over a year in that demographic category.

Dr Amitava Banerjee, UCL Institute of Health Informatics and Lead Author said:

“OurRisk.CoV calculator provides prior one-year mortality risks. Before the pandemic neither doctors, nor patients have been used to seeing such information, but in the current emergency there is an urgent need to develop better understanding based on reliable health data of who is at risk. What we offer is a prototype, a beta version that we and others can develop further.”

While researchers are working to develop vaccines, repurpose existing drugs, and identify novel antibody approaches, it is recognised that this will take time. The results signal the need for sustained stringent suppression measures, as well as sustained efforts to target those at highest risk because of underlying conditions with a range of preventive interventions.

Professor Harry Hemingway, UCL Institute of Health Informatics and Research Director of Health Data Research UK, said:

“Our research draws a line from current UK government policy to best available data and back to policy recommendations to avoid not just immediate deaths but also long-term excess deaths. We are putting into the public domain summary data on mortality risks, online tools and methods to catalyse this process.”

He added: The NHS has been crucial in identifying those people who are at ‘high risk’ and ‘extremely vulnerable’. This is a remarkable and important policy which is delivering interventions to people with a wide range of diseases, who are cared for by a wide range of clinical specialties. This policy is only possible because we have an NHS able to use system-wide data for patient benefit.”


University College London

University College London NHS Trust

The University of Cambridge

Health Data Research UK


Dr Amitava Banerjee, UCL Institute of Health Informatics