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Clustering long-term health conditions among 67728 people with multimorbidity using electronic health records in Scotland

Adeniyi Francis Fagbamigbe Orcid Logo, Utkarsh Agrawal, Amaya Azcoaga-Lorenzo Orcid Logo, Briana MacKerron, Eda Bilici Özyiğit, Daniel C. Alexander, Ashley Akbari Orcid Logo, Rhiannon Owen Orcid Logo, Jane Lyons, Ronan Lyons Orcid Logo, Spiros Denaxas, Paul Kirk, Ana Corina Miller, Gill Harper, Carol Dezateux Orcid Logo, Anthony Brookes, Sylvia Richardson, Krishnarajah Nirantharakumar, Bruce Guthrie Orcid Logo, Lloyd Hughes, Umesh T. Kadam Orcid Logo, Kamlesh Khunti, Keith R. Abrams, Colin McCowan

PLOS ONE, Volume: 18, Issue: 11, Start page: e0294666

Swansea University Authors: Ashley Akbari Orcid Logo, Rhiannon Owen Orcid Logo, Jane Lyons, Ronan Lyons Orcid Logo

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DOI (Published version): 10.1371/journal.pone.0294666

Abstract

There is still limited understanding of how chronic conditions co-occur in patients with multimorbidity and what are the consequences for patients and the health care system. Most reported clusters of conditions have not considered the demographic characteristics of these patients during the cluster...

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ISSN: 1932-6203
Published: Public Library of Science (PLoS) 2023
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Most reported clusters of conditions have not considered the demographic characteristics of these patients during the clustering process. The study used data for all registered patients that were resident in Fife or Tayside, Scotland and aged 25 years or more on 1st January 2000 and who were followed up until 31st December 2018. We used linked demographic information, and secondary care electronic health records from 1st January 2000. Individuals with at least two of the 31 Elixhauser Comorbidity Index conditions were identified as having multimorbidity. Market basket analysis was used to cluster the conditions for the whole population and then repeatedly stratified by age, sex and deprivation. 318,235 individuals were included in the analysis, with 67,728 (21·3%) having multimorbidity. We identified five distinct clusters of conditions in the population with multimorbidity: alcohol misuse, cancer, obesity, renal failure, and heart failure. Clusters of long-term conditions differed by age, sex and socioeconomic deprivation, with some clusters not present for specific strata and others including additional conditions. These findings highlight the importance of considering demographic factors during both clustering analysis and intervention planning for individuals with multiple long-term conditions. 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spelling v2 65255 2023-12-09 Clustering long-term health conditions among 67728 people with multimorbidity using electronic health records in Scotland aa1b025ec0243f708bb5eb0a93d6fb52 0000-0003-0814-0801 Ashley Akbari Ashley Akbari true false 0d30aa00eef6528f763a1e1589f703ec 0000-0001-5977-376X Rhiannon Owen Rhiannon Owen true false 1b74fa5125a88451c52c45bcf20e0b47 Jane Lyons Jane Lyons true false 83efcf2a9dfcf8b55586999d3d152ac6 0000-0001-5225-000X Ronan Lyons Ronan Lyons true false 2023-12-09 HDAT There is still limited understanding of how chronic conditions co-occur in patients with multimorbidity and what are the consequences for patients and the health care system. Most reported clusters of conditions have not considered the demographic characteristics of these patients during the clustering process. The study used data for all registered patients that were resident in Fife or Tayside, Scotland and aged 25 years or more on 1st January 2000 and who were followed up until 31st December 2018. We used linked demographic information, and secondary care electronic health records from 1st January 2000. Individuals with at least two of the 31 Elixhauser Comorbidity Index conditions were identified as having multimorbidity. Market basket analysis was used to cluster the conditions for the whole population and then repeatedly stratified by age, sex and deprivation. 318,235 individuals were included in the analysis, with 67,728 (21·3%) having multimorbidity. We identified five distinct clusters of conditions in the population with multimorbidity: alcohol misuse, cancer, obesity, renal failure, and heart failure. Clusters of long-term conditions differed by age, sex and socioeconomic deprivation, with some clusters not present for specific strata and others including additional conditions. These findings highlight the importance of considering demographic factors during both clustering analysis and intervention planning for individuals with multiple long-term conditions. By taking these factors into account, the healthcare system may be better equipped to develop tailored interventions that address the needs of complex patients. Journal Article PLOS ONE 18 11 e0294666 Public Library of Science (PLoS) 1932-6203 29 11 2023 2023-11-29 10.1371/journal.pone.0294666 COLLEGE NANME Health Data Science COLLEGE CODE HDAT Swansea University This work was supported by Health Data Research UK (HDR UK) Measuring and Understanding Multimorbidity using Routine Data in the UK (HDR-9006; CFC0110). Health Data Research UK (HDR-9006) is funded by: UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, the National Institute for Health Research (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome Trust 2024-04-15T18:30:54.7477441 2023-12-09T15:45:35.8107267 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Adeniyi Francis Fagbamigbe 0000-0001-9184-8258 1 Utkarsh Agrawal 2 Amaya Azcoaga-Lorenzo 0000-0003-3307-878x 3 Briana MacKerron 4 Eda Bilici Özyiğit 5 Daniel C. Alexander 6 Ashley Akbari 0000-0003-0814-0801 7 Rhiannon Owen 0000-0001-5977-376X 8 Jane Lyons 9 Ronan Lyons 0000-0001-5225-000X 10 Spiros Denaxas 11 Paul Kirk 12 Ana Corina Miller 13 Gill Harper 14 Carol Dezateux 0000-0001-9787-6276 15 Anthony Brookes 16 Sylvia Richardson 17 Krishnarajah Nirantharakumar 18 Bruce Guthrie 0000-0003-4191-4880 19 Lloyd Hughes 20 Umesh T. Kadam 0000-0002-4433-2977 21 Kamlesh Khunti 22 Keith R. Abrams 23 Colin McCowan 24 65255__29381__f777732467964dacb3ac96f4f0669799.pdf 65255.pdf 2024-01-04T15:38:47.4642525 Output 538049 application/pdf Version of Record true This is an open access article distributed under the terms of the Creative Commons Attribution License. true eng https://creativecommons.org/licenses/by/4.0/
title Clustering long-term health conditions among 67728 people with multimorbidity using electronic health records in Scotland
spellingShingle Clustering long-term health conditions among 67728 people with multimorbidity using electronic health records in Scotland
Ashley Akbari
Rhiannon Owen
Jane Lyons
Ronan Lyons
title_short Clustering long-term health conditions among 67728 people with multimorbidity using electronic health records in Scotland
title_full Clustering long-term health conditions among 67728 people with multimorbidity using electronic health records in Scotland
title_fullStr Clustering long-term health conditions among 67728 people with multimorbidity using electronic health records in Scotland
title_full_unstemmed Clustering long-term health conditions among 67728 people with multimorbidity using electronic health records in Scotland
title_sort Clustering long-term health conditions among 67728 people with multimorbidity using electronic health records in Scotland
author_id_str_mv aa1b025ec0243f708bb5eb0a93d6fb52
0d30aa00eef6528f763a1e1589f703ec
1b74fa5125a88451c52c45bcf20e0b47
83efcf2a9dfcf8b55586999d3d152ac6
author_id_fullname_str_mv aa1b025ec0243f708bb5eb0a93d6fb52_***_Ashley Akbari
0d30aa00eef6528f763a1e1589f703ec_***_Rhiannon Owen
1b74fa5125a88451c52c45bcf20e0b47_***_Jane Lyons
83efcf2a9dfcf8b55586999d3d152ac6_***_Ronan Lyons
author Ashley Akbari
Rhiannon Owen
Jane Lyons
Ronan Lyons
author2 Adeniyi Francis Fagbamigbe
Utkarsh Agrawal
Amaya Azcoaga-Lorenzo
Briana MacKerron
Eda Bilici Özyiğit
Daniel C. Alexander
Ashley Akbari
Rhiannon Owen
Jane Lyons
Ronan Lyons
Spiros Denaxas
Paul Kirk
Ana Corina Miller
Gill Harper
Carol Dezateux
Anthony Brookes
Sylvia Richardson
Krishnarajah Nirantharakumar
Bruce Guthrie
Lloyd Hughes
Umesh T. Kadam
Kamlesh Khunti
Keith R. Abrams
Colin McCowan
format Journal article
container_title PLOS ONE
container_volume 18
container_issue 11
container_start_page e0294666
publishDate 2023
institution Swansea University
issn 1932-6203
doi_str_mv 10.1371/journal.pone.0294666
publisher Public Library of Science (PLoS)
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
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description There is still limited understanding of how chronic conditions co-occur in patients with multimorbidity and what are the consequences for patients and the health care system. Most reported clusters of conditions have not considered the demographic characteristics of these patients during the clustering process. The study used data for all registered patients that were resident in Fife or Tayside, Scotland and aged 25 years or more on 1st January 2000 and who were followed up until 31st December 2018. We used linked demographic information, and secondary care electronic health records from 1st January 2000. Individuals with at least two of the 31 Elixhauser Comorbidity Index conditions were identified as having multimorbidity. Market basket analysis was used to cluster the conditions for the whole population and then repeatedly stratified by age, sex and deprivation. 318,235 individuals were included in the analysis, with 67,728 (21·3%) having multimorbidity. We identified five distinct clusters of conditions in the population with multimorbidity: alcohol misuse, cancer, obesity, renal failure, and heart failure. Clusters of long-term conditions differed by age, sex and socioeconomic deprivation, with some clusters not present for specific strata and others including additional conditions. These findings highlight the importance of considering demographic factors during both clustering analysis and intervention planning for individuals with multiple long-term conditions. By taking these factors into account, the healthcare system may be better equipped to develop tailored interventions that address the needs of complex patients.
published_date 2023-11-29T18:30:50Z
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