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Exploring patterns of multimorbidity in South Korea using exploratory factor analysis and non negative matrix
Yeonjae Kim1, Samina Park2, Yun Mi Choi3
1Department of Preventive Medicine, College of Medicine, Chung-Ang University, Seoul, Korea.
Scientific Reports
|March 23, 2025
Summary
This study analyzed disease patterns in over a million South Koreans, identifying new clusters of chronic diseases. These findings offer data-driven insights into multimorbidity, aiding public health strategies.
Area of Science:
- Public Health
- Epidemiology
- Data Science
Background:
- Multimorbidity, the co-occurrence of multiple chronic diseases, poses a significant public health challenge.
- Effective healthcare strategies require a deeper understanding of disease patterns and temporal clustering.
Purpose of the Study:
- To explore disease patterns and temporal clustering in a large South Korean population.
- To identify novel clusters of non-communicable diseases (NCDs) and confirm known comorbidity patterns.
Main Methods:
- Utilized data from South Korea's National Health Insurance Service (2002-2019) for approximately 1 million individuals.
- Analyzed 126 NCDs with >1% prevalence, applying a wash-out period for incidence determination.
- Employed Exploratory Factor Analysis (EFA) and Non-negative Matrix Factorization (NMF) to identify disease clusters across age and sex groups.
Main Results:
- EFA revealed 4-7 distinct disease patterns per demographic group (men/women, 50s/60s).
- NMF identified 10-16 distinct disease clusters per demographic group.
- The study confirmed known comorbidity patterns and uncovered previously unrecognized disease clusters.
Conclusions:
- Data-driven insights into multimorbidity mechanisms were provided.
- Findings support the development of evidence-based healthcare strategies for managing complex chronic conditions.
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