SCIENTISTS in Scotland are extending surveillance programmes set up during Covid in a bid to create a new risk calculator able to predict the patients most likely to be hospitalised with respiratory infections.
The project is one of more than a dozen data science initiatives which are being funded across the UK to find ways of alleviating winter pressures on the NHS.
These include rapid-research studies geared at reducing ambulance wait times, understanding how best to prioritise same-day emergency care, and using artificial intelligence to forecast peaks in Respiratory Syncytial Virus (RSV) - a common bug which can put pressure on paediatric intensive care units.
Other projects will investigate the impact of cold and damp homes on people’s health with the aim of informing policies to protect the most vulnerable and avoid knock-on impacts on the NHS.
All the projects will use existing data and are expected to be completed within three months in order to inform public health responses from next winter.
In Edinburgh, researchers have been funded to adapt Scotland's EAVE II database to identify individuals at the highest risk of becoming seriously ill if they develop a respiratory infection in winter.
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EAVE was originally set up during the swine flu pandemic in 2009 to monitor the effectiveness of vaccines by tracking 227,000 people from 40 GP practices.
The database was re-activated in response to the Covid pandemic and massively expanded in order to encompass all 5.4million people registered with a GP in Scotland.
This enabled scientists to track Covid in close to real-time in order to gauge vaccine safety and effectiveness against infection, hospitalisation, and death, as well as the characteristics of those falling seriously ill.
Professor Sir Aziz Sheikh, EAVE II study lead and the director of Edinburgh Usher Institute, said extending the surveillance to other viruses - such as flu or RSV - had the potential to cut hospital admissions.
He said: "What we now want to do is extend the work beyond Covid to look at other respiratory infections and morbidities to try to identify and characterise those who end up being hospitalised with respiratory conditions.
"Firstly, we want to understand the risk factors that increase the risk of those serious outcomes - hospital admissions.
"Secondly, we want to develop a risk prediction algorithm that will help decision-makers in terms of identifying which of those individuals are going to end up in hospital.
"The key is to see how we can intervene to reduce those risks, and thereby try to reduce pressures on the NHS."
Prof Aziz said while risk algorithms already exist and are used to prioritise access to vaccines and antivirals for individual diseases, such as Covid, this would create an overall risk scoring system for the first time covering all infectious respiratory diseases.
"We don't have anything like that at the moment - we only have standalone ones," he added.
"What we are also aware of is that there have been important developments on RSV in terms of vaccines - these are going to be available in due course and I think we need to be preparing the ground for when these become available.
"There are foundations there, but these need to be built on if we're going to get the NHS into a better position."
READ MORE: Scotland's flu deaths highest in more than 20 years
It comes days after the number of weekly flu deaths in Scotland hit their highest level in more than 20 years, following an extraordinary surge in the virus during December. At its peak, there were around 1,450 people in hospital testing positive for flu.
In the week ending January 15, a total of 2,020 deaths were registered in Scotland - of which 469 (23 per cent) were directly causes by respiratory illnesses including flu and Covid.
The surge in flu also pushed excess deaths to 450 - the highest number recorded in Scotland since the height of the pandemic in April 2020.
The project is one of 16 launched by Health Data Research (HDR) UK, the national institute for health data science, with funding of £800,000provided by the National Institute for Health and Care Research - which is in turn sponsored by the Department of Health.
Prof Aziz said the experience of the pandemic had underlined the importance of using data science to respond to problems quickly.
He said: "As an academic community, we began to understand what the need is and the pace with which answers are needed.
"Sometimes it's not about the shiny paper in a journal - the early insights can be equally, if not more, important."
Other projects will use datasets to identify people with rare diseases which make them more clinically vulnerable to Covid, but who have until now been overlooked in the public health response - for example by being missed for potentially lifesaving Covid boosters.
There are around 3.5 million people in the UK with rare diseases, but at the moment there is no clear guidance on which rare diseases require extra Covid-19 protection.
Another study, led by researchers at King's College London, will examine how the cost of living crisis impacts on children's health and the NHS by using artificial intelligence to "digitally replicate" these youngsters' household environments before simulating the impact of various interventions, such as winter fuel payments.
Dr Martin Chapman, who is leading the project, said the results could "guide future policy" on how to reduce avoidable pressures on NHS services.
He said: “There isn’t enough emphasis placed on the impact of the health of children and young people on the NHS during winter.
"Living in cold, damp and mouldy homes leads to chest conditions in children and mental health problems in adolescents, and rising energy costs mean more people than ever are living with heat poverty."
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Professor Cathie Sudlow, chief scientist at HDR UK, said:"We have not, until relatively recently, really embraced the power of data to inform and improve efficiency within the health system because we haven't been making it available at the scale and linked together in the sort of way that staff need it.
"That's becoming increasingly possible now. There are issues - like staffing - that data alone cannot solve, but there are certain issues where using data can inform better ways of delivering care."
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