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The University of Southampton

New £2.2 million study to use AI to prevent multiple long-term health conditions

Published: 30 June 2022
Young person smoking

Researchers from the Faculty of Medicine are to lead a new multidisciplinary, multicentre centre project to investigate risk factors and key timepoints in life to prevent particular combinations of long-term conditions like diabetes, heart disease, depression or dementia.

Increasing numbers of people are living with these long-term health conditions, which are known as multiple long-term condition multimorbidity. Many aspects across the lifecourse influence the risk of developing these conditions including biological factors, behaviours and broader life experiences, such as education and work. However, people from more disadvantaged backgrounds and/or certain ethnicities are more likely to develop multimorbidity and to develop it earlier.

To understand more about the things influencing the way people develop early (before the age of 65) multimorbidity over their lifetime and the subsequent burden, the study will use artificial intelligence (AI) methods to connect information and knowledge from three birth cohort studies of people all born in the same year and followed throughout their lives with two large electronic health record datasets.

They will also be investigating the order in which people develop conditions and how they group together to become ‘burdensome’.

The £2.2 million study, funded by the NIHR, will bring together researchers from public health, primary care, maths and computer science from five universities, as well as the city council and hospital trusts. The research team will work with public and patient contributors to ensure the research is timely and relevant.

The study is being led by Drs Simon Fraser and Nisreen Alwan and forms part of the NIHR Artificial Intelligence in Multiple Long-Term Condition Multimorbidity (AIM) programme .

Dr Fraser, Associate Professor of Public Health, said: “Multiple long-term condition multimorbidity is more likely to develop at a younger age among people from more socioeconomically deprived backgrounds and certain ethnicities. Using AI techniques will allow us to study the whole lifecourse and identify key targets and timepoints for public health preventive action. I am delighted to be working with colleagues in maths, statistics, computer science, and policy across a number of institutions along with members of the public to address this pressing public health issue.”

Dr Alwan, Associate Professor in Public Health, added: “This is a great opportunity to develop a co-produced approach with the public and colleagues from various disciplines that aims to investigate the early determinants of multimorbidity and examine how lifecourse health inequalities are shaped and thus how to tackle them.”

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