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The University of Southampton
Public Policy|Southampton

Research project: Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity (MELD-B)

Currently Active: 

A growing number of people 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.



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.


To achieve this aim, our study is composed of five work packages:

  1. 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
  2. Develop a safe data environments and readiness for AI analyses across large, representative routine healthcare datasets and birth cohorts.
  3. 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.
  4. 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.
  5. 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


We will work with our stakeholders to use the findings from our research to influence policy and practice, and to co-produce public health advice, on preventing burdensome MLTC-M.


MELD-B Collaborations
MELD-B Collaborations

Funder: NIHR

Duration: Start date 1st June 2022, End date 28th Feb 2025


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Journal Article

A paper published on earlier work from MELD which provides context to this current project

Staff MemberPrimary Position
Nisreen A AlwanProfessor in Public Health (University of Southampton)
Ashley AkbariSenior Research Manager (Swansea University)
Mark AshworthReader in Primary Care (King’s College London)
Ann BerringtonProfessor of Demography and Social Statistics (University of Southampton)
Michael BonifaceProfessorial Fellow of Information Systems (University of Southampton)
Jessica EnrightJessica Enright Senior Lecturer in Computing Science (University of Glasgow)
Nick FrancisNick Francis Professor of Primary Care Research (University of Southampton)
Simon DS FraserSimon DS Fraser Associate Professor of Public Health (University of Southampton)
Martin GullifordProfessor of Public Health (King’s College London)
Emilia HollandSpecialty Registrar in Public Health/Visiting Academic (University of Southampton)
Rebecca HoyleProfessor of Applied Mathematics (University of Southampton)
Barney JonesPatient and Public Involvement and Engagement Officer
Sara MacdonaldProfessor of General Practice and Primary Care (University of Glasgow)
Frances MairNorie Miller Professor of General Practice (University of Glasgow)
Rhiannon OwenAssociate Professor, Health Data Science (Swansea University)
Shantini ParanjothyProfessor and Clinical Chair in Public Health (University of Aberdeen)
Heather ParsonsPatient and Public Involvement and Engagement Lead
Ruben Sanchez-GarciaAssociate Professor of Pure and Applied Mathematics (University of Southampton)
Sebastian StannardPhD student in Demography and Social Statistics
Becky WilkinsonConsultant in Public Health (Southampton City Council)
Zlatko ZlatevSenior Enterprise Fellow, Electronics & Computer Science (University of Southampton)
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