Researchers from Population Data Science at Swansea University Medical School are working with a team led by the University of Southampton and also involving King’s College London, the University of Glasgow, and the University of Aberdeen.
This new multidisciplinary, multicentre centre project Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity (MELD-B) to investigate risk factors and key time points 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 life-course 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 (EHR) data sources.
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 from the University of Southampton 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 time points 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.”
Dr Rhiannon Owen, Associate Professor of Statistics at Population Data Science, said, “As global populations live longer, multiple long-term conditions and multimorbidity is a major health concern worldwide, with vast implications on health service providers such as the NHS. Targeted approaches for preventing or delaying the development of multiple long-term conditions are crucial to future healthcare planning and improving patient outcomes. This is an exciting opportunity to work as part of a multidisciplinary team across the UK to ensure that patient and public benefit are at the forefront of analytical development in healthcare research.”
Ashley Akbari, Senior Research Manager and Data Scientist at SAIL Databank, said, “Team Science collaborations such as MELD-B are vital in research in making the best use of the wide range of data currently available within trusted research environments across the UK, and the Population Data Science group at Swansea University has a strong record of multi-organisational and multidisciplinary collaborations such as this. We are really excited to be part of this project which will bring together a wide range of collaborators and stakeholders from academia, policy, health and public members to ensure the research we deliver provides impact and value to people and services.
