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

Simon DS Fraser Orcid Logo, Sebastian Stannard Orcid Logo, Emilia Holland, Michael Boniface, Rebecca B Hoyle, Rebecca Wilkinson, Ashley Akbari Orcid Logo, Mark Ashworth, Ann Berrington, Roberta Chiovoloni, Jessica Enright, Nick A Francis, Gareth Giles, Martin Gulliford, Sara Macdonald, Frances S Mair Orcid Logo, Rhiannon Owen Orcid Logo, 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 Orcid Logo, Roberta Chiovoloni, Rhiannon Owen Orcid Logo

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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...

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Published in: Journal of Multimorbidity and Comorbidity
ISSN: 2633-5565 2633-5565
Published: SAGE Publications 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa64623
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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 &#x2018;MELD-B&#x2019; 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 &#x2018;burdensomeness&#x2019; and &#x2018;complexity&#x2019; 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 &#x2018;preventable moments&#x2019;, 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.</abstract><type>Journal Article</type><journal>Journal of Multimorbidity and Comorbidity</journal><volume>13</volume><journalNumber/><paginationStart/><paginationEnd/><publisher>SAGE Publications</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>2633-5565</issnPrint><issnElectronic>2633-5565</issnElectronic><keywords>Life course, multimorbidity, long-term conditions, health, burdensome, complex, artificial intelligence, birth cohorts, routine healthcare datasets, prevention</keywords><publishedDay>31</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-12-31</publishedDate><doi>10.1177/26335565231204544</doi><url>http://dx.doi.org/10.1177/26335565231204544</url><notes/><college>COLLEGE NANME</college><department>Medical School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MEDS</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><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).</funders><projectreference/><lastEdited>2024-10-29T13:08:33.0771580</lastEdited><Created>2023-09-26T18:39:58.4164080</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Health Data Science</level></path><authors><author><firstname>Simon DS</firstname><surname>Fraser</surname><orcid>0000-0002-4172-4406</orcid><order>1</order></author><author><firstname>Sebastian</firstname><surname>Stannard</surname><orcid>0000-0002-6139-1020</orcid><order>2</order></author><author><firstname>Emilia</firstname><surname>Holland</surname><order>3</order></author><author><firstname>Michael</firstname><surname>Boniface</surname><order>4</order></author><author><firstname>Rebecca B</firstname><surname>Hoyle</surname><order>5</order></author><author><firstname>Rebecca</firstname><surname>Wilkinson</surname><order>6</order></author><author><firstname>Ashley</firstname><surname>Akbari</surname><orcid>0000-0003-0814-0801</orcid><order>7</order></author><author><firstname>Mark</firstname><surname>Ashworth</surname><order>8</order></author><author><firstname>Ann</firstname><surname>Berrington</surname><order>9</order></author><author><firstname>Roberta</firstname><surname>Chiovoloni</surname><order>10</order></author><author><firstname>Jessica</firstname><surname>Enright</surname><order>11</order></author><author><firstname>Nick A</firstname><surname>Francis</surname><order>12</order></author><author><firstname>Gareth</firstname><surname>Giles</surname><order>13</order></author><author><firstname>Martin</firstname><surname>Gulliford</surname><order>14</order></author><author><firstname>Sara</firstname><surname>Macdonald</surname><order>15</order></author><author><firstname>Frances S</firstname><surname>Mair</surname><orcid>0000-0001-9780-1135</orcid><order>16</order></author><author><firstname>Rhiannon</firstname><surname>Owen</surname><orcid>0000-0001-5977-376X</orcid><order>17</order></author><author><firstname>Shantini</firstname><surname>Paranjothy</surname><order>18</order></author><author><firstname>Heather</firstname><surname>Parsons</surname><order>19</order></author><author><firstname>Ruben J</firstname><surname>Sanchez-Garcia</surname><order>20</order></author><author><firstname>Mozhdeh</firstname><surname>Shiranirad</surname><order>21</order></author><author><firstname>Zlatko</firstname><surname>Zlatev</surname><order>22</order></author><author><firstname>Nisreen</firstname><surname>Alwan</surname><order>23</order></author></authors><documents><document><filename>64623__28837__d3c5441c8e3443b6a59752a82cfa628d.pdf</filename><originalFilename>64623.VOR.pdf</originalFilename><uploaded>2023-10-19T16:00:06.3171380</uploaded><type>Output</type><contentLength>1280720</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; The Author(s) 2023. 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spelling 2024-10-29T13:08:33.0771580 v2 64623 2023-09-26 Multidisciplinary ecosystem to study lifecourse determinants and prevention of early-onset burdensome multimorbidity (MELD-B) – protocol for a research collaboration aa1b025ec0243f708bb5eb0a93d6fb52 0000-0003-0814-0801 Ashley Akbari Ashley Akbari true false 08502855f683911aeb83edd02904be23 Roberta Chiovoloni Roberta Chiovoloni true false 0d30aa00eef6528f763a1e1589f703ec 0000-0001-5977-376X Rhiannon Owen Rhiannon Owen true false 2023-09-26 MEDS 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. Journal Article Journal of Multimorbidity and Comorbidity 13 SAGE Publications 2633-5565 2633-5565 Life course, multimorbidity, long-term conditions, health, burdensome, complex, artificial intelligence, birth cohorts, routine healthcare datasets, prevention 31 12 2023 2023-12-31 10.1177/26335565231204544 http://dx.doi.org/10.1177/26335565231204544 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University Another institution paid the OA fee This work was supported by the National Institute for Health Research (NIHR) under its Programme Artificial Intelligence for Multiple and Long-Term Conditions (NIHR203988). 2024-10-29T13:08:33.0771580 2023-09-26T18:39:58.4164080 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Simon DS Fraser 0000-0002-4172-4406 1 Sebastian Stannard 0000-0002-6139-1020 2 Emilia Holland 3 Michael Boniface 4 Rebecca B Hoyle 5 Rebecca Wilkinson 6 Ashley Akbari 0000-0003-0814-0801 7 Mark Ashworth 8 Ann Berrington 9 Roberta Chiovoloni 10 Jessica Enright 11 Nick A Francis 12 Gareth Giles 13 Martin Gulliford 14 Sara Macdonald 15 Frances S Mair 0000-0001-9780-1135 16 Rhiannon Owen 0000-0001-5977-376X 17 Shantini Paranjothy 18 Heather Parsons 19 Ruben J Sanchez-Garcia 20 Mozhdeh Shiranirad 21 Zlatko Zlatev 22 Nisreen Alwan 23 64623__28837__d3c5441c8e3443b6a59752a82cfa628d.pdf 64623.VOR.pdf 2023-10-19T16:00:06.3171380 Output 1280720 application/pdf Version of Record true © The Author(s) 2023. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title Multidisciplinary ecosystem to study lifecourse determinants and prevention of early-onset burdensome multimorbidity (MELD-B) – protocol for a research collaboration
spellingShingle Multidisciplinary ecosystem to study lifecourse determinants and prevention of early-onset burdensome multimorbidity (MELD-B) – protocol for a research collaboration
Ashley Akbari
Roberta Chiovoloni
Rhiannon Owen
title_short Multidisciplinary ecosystem to study lifecourse determinants and prevention of early-onset burdensome multimorbidity (MELD-B) – protocol for a research collaboration
title_full Multidisciplinary ecosystem to study lifecourse determinants and prevention of early-onset burdensome multimorbidity (MELD-B) – protocol for a research collaboration
title_fullStr Multidisciplinary ecosystem to study lifecourse determinants and prevention of early-onset burdensome multimorbidity (MELD-B) – protocol for a research collaboration
title_full_unstemmed Multidisciplinary ecosystem to study lifecourse determinants and prevention of early-onset burdensome multimorbidity (MELD-B) – protocol for a research collaboration
title_sort Multidisciplinary ecosystem to study lifecourse determinants and prevention of early-onset burdensome multimorbidity (MELD-B) – protocol for a research collaboration
author_id_str_mv aa1b025ec0243f708bb5eb0a93d6fb52
08502855f683911aeb83edd02904be23
0d30aa00eef6528f763a1e1589f703ec
author_id_fullname_str_mv aa1b025ec0243f708bb5eb0a93d6fb52_***_Ashley Akbari
08502855f683911aeb83edd02904be23_***_Roberta Chiovoloni
0d30aa00eef6528f763a1e1589f703ec_***_Rhiannon Owen
author Ashley Akbari
Roberta Chiovoloni
Rhiannon Owen
author2 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 Owen
Shantini Paranjothy
Heather Parsons
Ruben J Sanchez-Garcia
Mozhdeh Shiranirad
Zlatko Zlatev
Nisreen Alwan
format Journal article
container_title Journal of Multimorbidity and Comorbidity
container_volume 13
publishDate 2023
institution Swansea University
issn 2633-5565
2633-5565
doi_str_mv 10.1177/26335565231204544
publisher SAGE Publications
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
url http://dx.doi.org/10.1177/26335565231204544
document_store_str 1
active_str 0
description 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.
published_date 2023-12-31T14:29:20Z
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