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

  • Adeniyi Francis Fagbamigbe
  • , Utkarsh Agrawal
  • , Amaya Azcoaga-Lorenzo
  • , Briana MacKerron
  • , Eda Bilici Özyiğit
  • , Daniel C. Alexander
  • , Ashley Akbari
  • , Rhiannon K. Owen
  • , Jane Lyons
  • , Ronan A. 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
    • University of Cambridge
    • University of Birmingham
    • University of Warwick
    • University of St Andrews
    • University of Ibadan
    • University of Aberdeen
    • University of Oxford
    • Instituto de Investigación Sanitaria
    • University College London
    • Swansea University
    • British Heart Foundation
    • Queen's University Belfast
    • Queen Mary University of London
    • University of Leicester
    • University of Edinburgh

    Research output: Contribution to journalArticlepeer-review

    63 Downloads (Pure)

    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. [Abstract copyright: Copyright: © 2023 Fagbamigbe et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.]
    Original languageEnglish
    Article numbere0294666
    Number of pages14
    JournalPLoS ONE
    Volume18
    Issue number11
    Early online date29 Nov 2023
    DOIs
    Publication statusE-pub ahead of print - 29 Nov 2023

    Bibliographical note

    Copyright: © 2023 Fagbamigbe et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

    Keywords

    • Chronic Disease
    • Cluster Analysis
    • Delivery of Health Care
    • Electronic Health Records
    • Humans
    • Multimorbidity
    • Scotland - epidemiology

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