Anonymous data was collected from more than eight million adults in 1,205 general practitioners across England.
The algorithm was based on primary care, hospitals, COVID-19 test results and death records during the first wave of the pandemic.
The new model also took into account risk factors such as age, ethnicity, degree of deprivation, obesity and existing medical conditions.
Together, this data was used to create the “QCovid” tool, which enabled a weighted, cumulative calculation of risk.
The QCovid algorithm was tested in two independent data sets from January to April 2020 and from May 2020 to June 2020.
A team of researchers ran the tool to determine whether or not it could accurately predict serious outcomes of developing COVID-19 – and it did.
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“The QCovid risk model offers a much more nuanced risk assessment,” she added.
Dr. Hippisley-Cox stated that the QCovid tool does this by “taking into account a number of different factors that are used cumulatively to estimate risk”.
She continued, “This model will help educate the clinical council so people can take reasonable precautions to protect themselves from COVID-19.”
The work was funded by the National Institute for Health Research (NIHR) and commissioned by the Chief Medical Officer (CMO) for England.
The Deputy CMO for England, Dr. Jenny Harries, said, “It is vital to continue to improve our understanding of the virus and its impact on different populations as its prevalence continues to rise.
“That is why we commissioned and funded this research, and I am pleased that it is useful.”
The NHS has classified people as either “clinically extremely susceptible” or “clinically susceptible” to the virus.
Those in the first group – also known as “high risk” – may receive targeted cancer treatment.
People in the second group (ie, “moderate risk”) include people with diabetes.
Click here for a full list of all health conditions considered in both risk categories.
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