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Clustering long-term health conditions among 67728 people with multimorbidity using electronic health records in Scotland
,
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
PLOS ONE, Volume: 18, Issue: 11, Start page: e0294666
Swansea University Authors:
Ashley Akbari , Rhiannon Owen , Jane Lyons, Ronan Lyons
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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...
| Published in: | PLOS ONE |
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| ISSN: | 1932-6203 |
| Published: |
Public Library of Science (PLoS)
2023
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa65255 |
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| 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 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. |
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| College: |
Faculty of Medicine, Health and Life Sciences |
| Funders: |
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 |
| Issue: |
11 |
| Start Page: |
e0294666 |