Work

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

Year: 2023

Type: article

Abstract: Background: A deeper understanding of the co-occurrence of long-term conditions could help prepare health systems to manage the complex needs of people with multiple long-term conditions and deliver more effective care. Most reported clusters of conditions have not considered the demographic characteristics of these patients during the clustering process. Methods: The study used data for all registered patients resident in Fife or Tayside, Scotland and aged 44 years or more on 31st December 2018. We used linked demographic information, and secondary care electronic health records from 1st January 2000. People with at least two of the 31 Elixhauser Comorbidity Index conditions were identified as population with multimorbidity. Market basket analysis was used to cluster the conditions and stratified by age, sex and deprivation. Results: 318,235 individuals were included in the analysis, of whom 67,728 (21·3%) had multimorbidity. We identified five distinct clusters of conditions in the population with multimorbidity, however, clusters differed by sex, age and deprivation. Conditions such as psychosis, drug abuse, depression, other neurological disorders, liver disease and alcohol abuse clustered together among those aged 44-49 years, compared with clustering of conditions such as metastatic cancer, rheumatoid arthritis/collagen disorders, hypothyroidism, deficiency anaemia, and fluid & electrolyte disorders among those aged over 80 years. Conclusion: Multimorbidity was present in one of every 5 people. Clusters of long-term conditions differed by age, sex and socioeconomic deprivation. When clustering and planning intervention for multiple long-term conditions, it is important to consider the age as well as sex and socioeconomic deprivation of individuals. Funding: 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. Declaration of Interest: The authors declare no competing interests. Ethical Approval: HIC provided a linked dataset within a Safe Haven environment for this study. The dataset was obtained under HIC Standard Operating Procedures (SOP). NHS Tayside Research Ethics Committee have approved these SOPs (18/ES/0126). The School of Medicine Ethics Committee, acting on behalf of the University of St Andrews Teaching and Research Ethics Committee approved the project (UTREC MD15619 approved 30th June 2021). As the study data are de-identified, consent from individual patients was not required.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0294666