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

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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.

MELD-b Research Project: an Intro

Watch our short animation for an introduction to MELD-b.

Click here to watch
MELD-B Logo
MELD-B Logo

Aim

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.

Plan

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

Impact

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

Contact: s.fraser@soton.ac.uk

Response to the Health and Social Care Committee’s Inquiry on prevention in health and social careThe Meld-B project Podcast Episode 1The Meld-B project Podcast Episode 2
Simon Fraser's Blog
MELD-B Lynn Blog

Meet Lynn Blog

Lynn is on the Patient Advisory Group for the Meld-B study and expressed how important it is for researchers to work with people who have not just one or two conditions but lots, to get a full insight.

MELD-B Jim Blog

Meet Jim Blog

Jim is hoping that the findings from the current phase (Meld-B) will show that an understanding can be gained into the influences of early morbidity to enable further funding to scale up the study.

MELD-B Jack Blog

Meet Jack Blog

The role Jack fulfils is as a member of the advisory board. He sees that job as overseeing the study as a whole and to give feedback and scrutiny.

Multidisciplinary ecosystem to study lifecourse determinants and prevention of early-onset burdensome multimorbidity (MELD-B) – protocol for a research collaboration

Journal Article 3

A paper on 'Multidisciplinary ecosystem to study lifecourse determinants and prevention of early-onset burdensome multimorbidity (MELD-B) – protocol for a research collaboration'

A conceptual framework for characterising lifecourse determinants of multiple long-term condition multimorbidity

Journal Article 2

A paper detailing research from MELD-b to develop groupings of early life factors that may be important for shaping the risk of developing multiple long-term conditions

BMJ logo

Journal Article 1

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)
Kelly Cheung Patient and Public Involvement and Engagement Officer
Alex DreganSenior Lecturer in Psychiatric Epidemiology
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)
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)
Ruben Sanchez-GarciaAssociate Professor of Pure and Applied Mathematics (University of Southampton)
Mozhdeh ShiraniradMachine learning Research Fellow
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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