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UKHSA launches new pilot to prevent care home infections

Data from over 500 care homes across England is to be gathered and analysed as part of a new pilot scheme designed to help prevent infections.
The UK Health Security Agency (UKHSA) has announced a new pilot scheme to monitor infections in care homes in England.
The pilot builds on the success of the Vivaldi study during the pandemic, with a wider remit to study other infections.
The Vivaldi social care project, commissioned by UKHSA in collaboration with University College London (UCL) and other partners including The Outstanding Society, Care England, and NHS England, is one of several national surveillance studies commissioned by UKHSA to gather evidence on the burden of infections across healthcare and community settings.
This new pilot will work with over 500 care homes in England to monitor infections such as Covid-19, flu, norovirus, and urinary tract infections, and analyse the resultant anonymised data in order to help reduce infections in care homes for older adults.
The Vivaldi study
The Vivaldi study, a national surveillance study, was commissioned by UKHSA during the pandemic to investigate Covid-19 infections in care homes. Researchers looked at the impact of Covid-19 in care homes, what could be done to prevent the spread of infection, and the effectiveness of the Covid-19 vaccination.
At the beginning of the pandemic, a lack of data and surveillance infrastructure left care homes for older adults vulnerable to infections and outbreaks.
The Vivaldi survey was set up in May 2020 to measure the burden of Covid-19 infections in care home staff and residents and understand why some homes were experiencing outbreaks. This included swab testing through the national care home testing programme and surveys completed by care home managers.
The main study started in June 2020 and built on the findings of the Vivaldi survey by linking routinely collected data from staff and residents in over 300 care homes in England.
This data included vaccination records, hospital visits, and death records. Blood samples were also taken to study infection rates, immunity, and other factors in long-term care facilities.
According to UKHSA, the research from the Vivaldi study helped to inform decisions such as limiting staff movement between care homes to reduce infection risk and highlighting the need for sick pay for care home workers.
Professor Steven Riley, director general of Data, Analytics and Surveillance at UKHSA, commented: “UKHSA’s collaboration with UCL on the Vivaldi study helped us understand the impact Covid-19 had in care homes and fed directly into important policy making decisions, helping to protect those living and working in adult social care settings during the pandemic.
“We are delighted to be able to continue this work through the Vivaldi social care pilot which will improve our understanding of infections in care homes and similar environments beyond Covid-19 and will provide valuable data that will contribute to our mission to prepare for, prevent and respond to health threats, protect livelihoods and, most importantly, save lives.”
Zoe Fry OBE, director of The Outstanding Society, said: “This programme will work to enhance the wellbeing of individuals residing or working in care homes for older people throughout England as well as supporting the wellbeing of visitors to the homes.
“This initiative seeks to investigate strategies for minimising infections and enhancing the overall quality of life within these care settings. The collection of data plays a pivotal role in driving positive transformation within the sector, reflecting our commitment to research led by social care, for social care.”
Shaping policy and priorities
The Vivaldi social care platform has been co-designed with people who live and work in care homes and will connect lists of care home residents with routine data sets already held by the NHS, including hospital admissions, mortality data, immunisations, laboratory test results, and prescriptions, all using a common pseudo-identifier based on NHS numbers.
The data generated by the Vivaldi social care pilot will play a pivotal role in shaping important policy areas and priorities as the study progresses.
Professor Martin Green OBE, chief executive of Care England, added: “The Vivaldi social care project will enable residents, families, care workers, providers, and wider stakeholders to work together and develop our understanding of how to reduce the impact of infections and outbreaks in care homes.
“Research in our sector is vital to help influence government policy and deliver improved outcomes for people who live in, work in, and visit social care. Vivaldi social care brings the sector together to improve learning and lays a strong foundation for other studies to shape social care through research going forward.”
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AI can predict Alzheimer’s with almost 93% accuracy, researchers say

Alzheimer’s AI can predict the disease with nearly 93 per cent accuracy using more than 800 brain scans, researchers say.
The system identified anatomical changes in the brain linked to the onset of the most common form of dementia, a condition that gradually damages memory and thinking.
The findings build on years of research suggesting AI could help spot early Alzheimer’s risk, predict disease and identify patients whose condition has not yet been diagnosed.
Benjamin Nephew, an assistant research professor at the Worcester Polytechnic Institute in Massachusetts, said: “Early diagnosis of Alzheimer’s disease can be difficult because symptoms can be mistaken for normal ageing.
“We found that machine-learning technologies, however, can analyse large amounts of data from scans to identify subtle changes and accurately predict Alzheimer’s disease and related cognitive states.”
The study used MRI scans, a type of detailed brain imaging, from 344 people aged 69 to 84.
The dataset included 281 scans showing normal mental function, 332 with mild cognitive impairment, an early stage of memory and thinking decline, and 202 with Alzheimer’s.
The scans covered 95 of the brain’s nearly 200 distinct regions and used an AI algorithm to predict patients’ health.
Being able to use AI to help diagnose Alzheimer’s earlier could give patients and doctors crucial time to prepare and potentially slow the progression of the disease.
The analysis showed that one of the top predictive factors was brain volume loss, or shrinkage, in the hippocampus, which helps form memories, the amygdala, which processes fear, and the entorhinal cortex, which helps provide a sense of time.
This pattern held across age and sex, with both men and women aged 69 to 76 showing volume loss in the right part of the hippocampus, suggesting it may be an important area for early diagnosis, the researchers noted.
However, the research also found that the way brain regions shrink differs by sex.
In females, volume loss occurred in the brain’s left middle temporal cortex, which is involved in language and visual perception. In males, it was mainly seen in the right entorhinal cortex
The researchers believe this could be linked to changes in sex hormones, including the loss of oestrogen in women and testosterone in men.
These conclusions could help improve methods of diagnosis and treatment going forward, Nephew said.
More than 7.2m Americans are living with Alzheimer’s, according to the Alzheimer’s Association.
More research is being done to reveal other impacting factors.
Nephew said: “The critical challenge in this research is to build a generalisable machine-learning model that captures the difference between healthy brains and brains from people with mild cognitive impairment or Alzheimer’s disease.”
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