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[Statistical analysis methods for identifying multimorbidity patterns]
1School of Public Health, Health Science Center, Ningbo University, Ningbo 315211, China.
Summary
Identifying multimorbidity patterns is key for better healthcare. This study reviews methods to find these patterns, aiding prognosis and resource use.
Area of Science:
- Public Health
- Biostatistics
- Epidemiology
Context:
- Multimorbidity, the co-occurrence of multiple chronic diseases, is a significant global health challenge.
- Effective identification of multimorbidity patterns is crucial for optimizing healthcare resource allocation and improving patient outcomes.
Purpose:
- To summarize and compare three main methodological approaches for identifying multimorbidity patterns: association analysis, classification, and dimensionality reduction.
- To demonstrate the application of these methods using UK Biobank data for pattern identification.
- To provide guidance on selecting appropriate methods for multimorbidity research.
Summary:
- The study reviews association rule mining, network analysis, cluster analysis, latent class analysis, latent transition analysis, principal component analysis, factor analysis, and multiple correspondence analysis.
- These methods were applied to UK Biobank data to identify distinct multimorbidity patterns.
- A comparative analysis of the results from these methods is presented.
Impact:
- This research offers valuable insights for healthcare providers and policymakers in managing complex patient populations.
- The findings can inform the development of targeted interventions and personalized treatment strategies for individuals with multiple conditions.
- This work serves as a reference for researchers investigating multimorbidity patterns, promoting methodological clarity and informed decision-making.
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