New tech will help high-risk British Indians access Covid vaccines

New tech will help high-risk British Indians access Covid vaccines
Courtesy: Andriy Onufriyenko | Moment via Getty Image

Many British Indians are set to benefit from a new risk-prediction model to be used by the National Health Service (NHS), which identifies a wider group of people who may be at high risk from Covid-19 due to underlying conditions or comorbidities associated with people of South Asian origin.

The technique is devised by Oxford University scientists and will be deployed by NHS England to help clinicians identify their most vulnerable patients, based on medical records. Over 800,000 adults are expected to be prioritised to receive a vaccine as part of the current vaccination cohorts, combining several health and personal factors, such as age, ethnicity and body mass index (BMI), as well as certain medical conditions and treatments.

As the UK’s vaccination programme expands to over-65s this week, the new model will provide an additional level of protection for people who may be in the younger age range but still at a greater risk from the deadly virus.

QCovid model

The University of Oxford turned its research into a risk prediction model called QCovid, which has been independently validated by the Office for National Statistics (ONS) and is pegged as the only Covid-19 risk prediction model in the world to meet the "highest standards of evidence and assurance".

Dr Jenny Harries, Deputy Chief Medical Officer for England, said: “For the first time, we are able to go even further in protecting the most vulnerable in our communities.

“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.”

Given the evidence that Covid-19 impacts certain age groups and ethnic minorities such as South Asians with comorbidities – such as such as blood pressure, obesity and diabetes – at a higher rate, research was commissioned by England’s Chief Medical Officer, Chris Whitty, and funded by the National Institute of Health Research, to zero in on these risk factors. The new technology analyses a combination of risk factors based on medical records, to assess whether somebody may be more vulnerable than was previously understood, helping clinicians provide vaccination more quickly to them.

Anonymised data

Lead researcher Professor Julia Hippisley-Cox, Professor of Clinical Epidemiology and General Practice at 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.”

Under the modelling, up to 1.7 million patients have been identified and their general practitioners (GPs) are also being notified. Those within this group who are over 70 will have already been invited for vaccination and 820,000 adults between 19 and 69 years will now be prioritised for a vaccination.

The patients identified through the risk assessment will be sent a letter from NHS England in the coming days, explaining that their risk factors may help identify them as high clinical risk and that they are included within the support and advice for the clinically extremely vulnerable. They will be invited to receive a Covid-19 vaccine as soon as possible if they haven’t already had the jab, and will be given advice on precautionary measures, including shielding if necessary.

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