Journal article 191 views 50 downloads
Multidisciplinary ecosystem to study lifecourse determinants and prevention of early-onset burdensome multimorbidity (MELD-B) – protocol for a research collaboration
,
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 Owen ,
Shantini Paranjothy,
Heather Parsons,
Ruben J Sanchez-Garcia,
Mozhdeh Shiranirad,
Zlatko Zlatev,
Nisreen Alwan
Journal of Multimorbidity and Comorbidity, Volume: 13
Swansea University Authors:
Ashley Akbari , Roberta Chiovoloni, Rhiannon Owen
-
PDF | Version of Record
© The Author(s) 2023. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0).
Download (1.22MB)
DOI (Published version): 10.1177/26335565231204544
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...
| Published in: | Journal of Multimorbidity and Comorbidity |
|---|---|
| ISSN: | 2633-5565 2633-5565 |
| Published: |
SAGE Publications
2023
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa64623 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| 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, multimorbidity, long-term conditions, health, burdensome, complex, artificial intelligence, birth cohorts, routine healthcare datasets, prevention |
| College: |
Faculty of Medicine, Health and Life Sciences |
| Funders: |
This work was supported by the National Institute for Health Research (NIHR) under its Programme Artificial Intelligence for Multiple and Long-Term Conditions (NIHR203988). |