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Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity

Health & Wellbeing Artificial Intelligence

Project Vision

A growing number of peopless are living with several long-term health conditions like diabetes, heart disease, depression or dementia. We call this multiple long-term condition multimorbidity (MLTC-M). Many things throughout a person’s life influence the chances of developing health conditions. This includes their biology (e.g. age, ethnicity), things that happen to them (e.g. infections, accidents), behaviours (e.g. smoking, diet) and broader experiences (e.g. the environment people grew up in, their education, work, income).

People from more disadvantaged backgrounds and/or certain ethnicities are more likely to develop MLTC-M and to develop it earlier. The impact (or ‘burden’) of MLTC-M, and the order that people develop conditions, also vary. Our research will help understand when MLCT-M becomes ‘burdensome’ and the best opportunities for intervention.

Project Objectives

To use an Artificial Intelligence (AI) enhanced analysis of birth cohort data and electronic health records to identify lifecourse time points and targets for the prevention of early-onset, burdensome MLTC-M.

  • Undertake a qualitative evidence synthesis and a consensus study (Delphi) to develop deeper understanding of what ‘burdensomeness’ and ‘complexity’ mean to people living with early-onset (by age 65) MLTC-M, carers and healthcare professionals

  • Develop a safe data environments and readiness for AI analyses across large, representative routine healthcare datasets and birth cohorts.

  • In those safe data environments, using the WP1 burdensomeness/complexity indicators and applying AI methods, identify novel early-onset, burdensome MLTC-M clusters. Also in this work package, we will match individuals in birth cohorts into routine data MLTC-M clusters and then identify determinants of burdensome clusters and model trajectories of long-term conditions (LTCs) and burden accrual.

  • By characterising clusters of early-life (pre-birth to 18 years) risk factors for early-onset, burdensome MLTC-M and sentinel conditions (the first LTC to occur in the lifecourse), we will define population groups in early life at risk of future MLTC-M, identify critical time points and targets for prevention, and model counterfactual prevention scenarios of interventions acting on combined risk factors at key timepoints.

  • Engage key stakeholders to prioritise timepoints and targets to prevent/delay specified sentinel conditions and early-onset, burdensome MLTC-M. Partnering with our PPI Advisory Board, and through further stakeholder engagement, we will co-produce public health implementation recommendations

IT Innovation's Role

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IT Innovation leads the work package on trustworthy data access and harmonisation (data access, processing and ontology) across multiple routine and longditudinal datasets. IT Innovation also contributes to the AI architectures and pipelines for modelling burdensome conditions

MELDB builds on IT Innovation's deep expertise in data governance for research and broader data science expertise both in terms of data modelling and advanced analytics

Project Fact Sheet

The MELD-B project is a 36 month project funded by the National Institute of Health Research.

Coordinator: University of Southampton
More information:

European emblem This project has received funding from the National Institute of Health Research under grant agreement NIHR203988.