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Clustering long-term health conditions among 67728 people with multimorbidity using electronic health records in Scotland.
Fagbamigbe, Adeniyi Francis; Agrawal, Utkarsh; Azcoaga-Lorenzo, Amaya; MacKerron, Briana; Özyigit, Eda Bilici; Alexander, Daniel C; Akbari, Ashley; Owen, Rhiannon K; Lyons, Jane; Lyons, Ronan A; Denaxas, Spiros; Kirk, Paul; Miller, Ana Corina; Harper, Gill; Dezateux, Carol; Brookes, Anthony; Richardson, Sylvia; Nirantharakumar, Krishnarajah; Guthrie, Bruce; Hughes, Lloyd; Kadam, Umesh T; Khunti, Kamlesh; Abrams, Keith R; McCowan, Colin.
Afiliação
  • Fagbamigbe AF; School of Medicine, University of St Andrews, St Andrews, United Kingdom.
  • Agrawal U; Department of Epidemiology and Medical Statistics, University of Ibadan, Ibadan, Nigeria.
  • Azcoaga-Lorenzo A; Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, United Kingdom.
  • MacKerron B; Research Methods and Evaluation Unit, Institute for Health & Wellbeing, Coventry University, Coventry, United Kingdom.
  • Özyigit EB; Nuffield Department of Primary Care Health Science, University of Oxford, Oxford, United Kingdom.
  • Alexander DC; School of Medicine, University of St Andrews, St Andrews, United Kingdom.
  • Akbari A; Hospital Rey Juan Carlos, Instituto de Investigación Sanitaria Fundación Jimenez Diaz, Madrid, Spain.
  • Owen RK; School of Medicine, University of St Andrews, St Andrews, United Kingdom.
  • Lyons J; Centre for Medical Image Computing, Department of Computer Science, UCL, London, United Kingdom.
  • Lyons RA; Centre for Medical Image Computing, Department of Computer Science, UCL, London, United Kingdom.
  • Denaxas S; Population Data Science, Swansea University Medical School, Swansea University, Swansea, United Kingdom.
  • Kirk P; Population Data Science, Swansea University Medical School, Swansea University, Swansea, United Kingdom.
  • Miller AC; Population Data Science, Swansea University Medical School, Swansea University, Swansea, United Kingdom.
  • Harper G; Population Data Science, Swansea University Medical School, Swansea University, Swansea, United Kingdom.
  • Dezateux C; Institute of Health Informatics, UCL, London, United Kingdom.
  • Brookes A; British Heart Foundation Data Science Centre, London, United Kingdom.
  • Richardson S; MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom.
  • Nirantharakumar K; Centre for Public Health, Institute of Clinical Science, Queen's University Belfast, Belfast, United Kingdom.
  • Guthrie B; Clinical Effectiveness Group, Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom.
  • Hughes L; Clinical Effectiveness Group, Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom.
  • Kadam UT; Department of Genetics & Genome Biology, University of Leicester, Leicester, United Kingdom.
  • Khunti K; MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom.
  • Abrams KR; Public Health, University of Birmingham, Birmingham, United Kingdom.
  • McCowan C; Advanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom.
PLoS One ; 18(11): e0294666, 2023.
Article em En | MEDLINE | ID: mdl-38019832
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.
Assuntos

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Registros Eletrônicos de Saúde / Multimorbidade Limite: Humans País/Região como assunto: Europa Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Reino Unido

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Registros Eletrônicos de Saúde / Multimorbidade Limite: Humans País/Região como assunto: Europa Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Reino Unido