Discovery and classification of complex multimorbidity patterns: unravelling chronicity networks and their social profiles

• Published in: Scientific reports Vol. 12; no. 1; p. 20004
• Main Authors: Alvarez-Galvez, Javier, Vegas-Lozano, Esteban
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 21-11-2022; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308; 692/499; 692/699; Algorithms; Chronic Disease; Chronic illnesses; Classification; Clinical medicine; Comorbidity; Disabled Persons; Disease prevention; Epidemiology; Health services utilization; Health Surveys; Humanities and Social Sciences; Humans; Mortality; multidisciplinary; Multimorbidity; Older people; Primary care; Qualitative research; Quality of Life; Science; Science (multidisciplinary); Social organization
• DOI: 10.1038/s41598-022-23617-8
• EISSN: 2045-2322

Multimorbidity can be defined as the presence of two or more chronic diseases in an individual. This condition is associated with reduced quality of life, increased disability, greater functional impairment, increased health care utilisation, greater fragmentation of care and complexity of treatment, and increased mortality. Thus, understanding its epidemiology and inherent complexity is essential to improve the quality of life of patients and to reduce the costs associated with multi-pathology. In this paper, using data from the European Health Survey, we explore the application of Mixed Graphical Models and its combination with social network analysis techniques for the discovery and classification of complex multimorbidity patterns. The results obtained show the usefulness and versatility of this approach for the study of multimorbidity based on the use of graphs, which offer the researcher a holistic view of the relational structure of data with variables of different types and high dimensionality.