Journal
BMC FAMILY PRACTICE
Volume 19, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s12875-018-0790-x
Keywords
Multimorbidity; Cluster analysis; Multiple correspondence analysis; K-means clustering; Primary health care; Electronic health records; Diseases
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Funding
- Instituto de Salud Carlos III of the Ministry of Economy and Competitiveness (Spain) through the Network for Prevention and Health Promotion in Primary Health Care (redIAPP) [RD12/0005]
- National Institute for Health Research Clinician Scientist Award [NIHR/CS/010/024]
- ISCiii [PI12/00427]
- European Union ERDF funds
- National Institutes of Health Research (NIHR) [NIHR/CS/010/024] Funding Source: National Institutes of Health Research (NIHR)
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Background: The purpose of this study was to ascertain multimorbidity patterns using a non-hierarchical duster analysis in adult primary patients with multimorbidity attended in primary care centers in Catalonia. Methods: Cross-sectional study using electronic health records from 523,656 patients, aged 45-64 years in 274 primary health care teams in 2010 in Catalonia, Spain. Data were provided by the Information System for the Development of Research in Primary Care (SIDIAP), a population database. Diagnoses were extracted using 241 blocks of diseases (International Classification of Diseases, version 10). Multimorbidity patterns were identified using two steps: 1) multiple correspondence analysis and 2) k-means clustering. Analysis was stratified by sex. Results: The 408,994 patients who met multimorbidity criteria were included in the analysis (mean age,54.2 years [Standard deviation, SD: 5.8], 53.3% women). Six multimorbidity patterns were obtained for each sex; the three most prevalent included 68% of the women and 66% of the men, respectively. The top cluster included coincident diseases in both men and women: Metabolic disorders, Hypertensive diseases, Mental and behavioural disorders due to psychoactive substance use, Other dorsopathies, and Other soft tissue disorders. Conclusion: Non-hierarchical cluster analysis identified multimorbidity patterns consistent with clinical practice, identifying phenotypic subgroups of patients.
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