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

Simon Ds Fraser; Sebastian Stannard; Emilia Holland; Michael Boniface; Rebecca B Hoyle; Rebecca Wilkinson; Ashley Akbari; Mark Ashworth; Ann Berrington; Roberta Chiovoloni; Jessica Enright; Nick A Francis; Gareth Giles; Martin Gulliford; Sara Macdonald; Frances S Mair; Rhiannon K Owen; Shantini Paranjothy; Heather Parsons; Ruben J Sanchez-Garcia; Mozhdeh Shiranirad; Zlatko Zlatev; Nisreen Alwan

doi:10.1177/26335565231204544

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

J Multimorb Comorb. 2023 Sep 25:13:26335565231204544. doi: 10.1177/26335565231204544. eCollection 2023 Jan-Dec.

Authors

Simon Ds Fraser^{1

2}, Sebastian Stannard¹, Emilia Holland¹, Michael Boniface³, Rebecca B Hoyle⁴, Rebecca Wilkinson⁵, Ashley Akbari⁶, Mark Ashworth⁷, Ann Berrington⁸, Roberta Chiovoloni⁶, Jessica Enright⁹, Nick A Francis¹, Gareth Giles¹⁰, Martin Gulliford⁷, Sara Macdonald¹¹, Frances S Mair¹¹, Rhiannon K Owen⁶, Shantini Paranjothy^{12

13}, Heather Parsons¹⁴, Ruben J Sanchez-Garcia^{4

15}, Mozhdeh Shiranirad⁴, Zlatko Zlatev³, Nisreen Alwan^{1

14

16

2}

Affiliations

¹ School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, Southampton General Hospital, Southampton, UK.
² NIHR Applied Research Collaboration Wessex, Southampton, UK.
³ School of Electronics and Computer Science, University of Southampton, Southampton, UK.
⁴ School of Mathematical Sciences, University of Southampton, Southampton, UK.
⁵ Southampton City Council, Southampton, UK.
⁶ Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK.
⁷ School of Life Course and Population Sciences, King's College London, London, UK.
⁸ Department of Social Statistics and Demography, University of Southampton, Southampton, UK.
⁹ Computing Science, University of Glasgow, Glasgow, UK.
¹⁰ Public Policy Southampton, University of Southampton, Southampton, UK.
¹¹ School of Health and Wellbeing, General Practice and Primary Care, University of Glasgow, Glasgow, UK.
¹² School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK.
¹³ NHS Grampian Health Board, Aberdeen, UK.
¹⁴ Patient and Public Involvement and Engagement, University Hospital Southampton NHS Foundation Trust, Southampton, UK.
¹⁵ The Alan Turing Institute, London, UK.
¹⁶ NIHR Southampton Biomedical Research Centre, Southampton, UK.

Abstract

Background: Most people living with multiple long-term condition multimorbidity (MLTC-M) are under 65 (defined as 'early onset'). Earlier and greater accrual of long-term conditions (LTCs) may be influenced by the timing and nature of exposure to key risk factors, wider determinants or other LTCs at different life stages. We have established a research collaboration titled 'MELD-B' to understand how wider determinants, sentinel conditions (the first LTC in the lifecourse) and LTC accrual sequence affect risk of early-onset, burdensome MLTC-M, and to inform prevention interventions.

Aim: Our aim is to identify critical periods in the lifecourse for prevention of early-onset, burdensome MLTC-M, identified through the analysis of birth cohorts and electronic health records, including artificial intelligence (AI)-enhanced analyses.

Design: We will develop deeper understanding of 'burdensomeness' and 'complexity' through a qualitative evidence synthesis and a consensus study. Using safe data environments for analyses across large, representative routine healthcare datasets and birth cohorts, we will apply AI methods to identify early-onset, burdensome MLTC-M clusters and sentinel conditions, develop semi-supervised learning to match individuals across datasets, identify determinants of burdensome clusters, and model trajectories of LTC and burden accrual. We will characterise early-life (under 18 years) risk factors for early-onset, burdensome MLTC-M and sentinel conditions. Finally, using AI and causal inference modelling, we will model potential 'preventable moments', defined as time periods in the life course where there is an opportunity for intervention on risk factors and early determinants to prevent the development of MLTC-M. Patient and public involvement is integrated throughout.

Keywords: Life course; artificial intelligence; birth cohorts; burdensome; complex; health; long-term conditions; multimorbidity; prevention; routine healthcare datasets.