USING NETWORK ANALYSIS METHODS TO STUDY MULTIMORBIDITY PATTERNS
Bibliographic record
Abstract
Abstract Multimorbidity is a risk factor for patient-important outcomes including quality of life and functional decline. Multimorbidity research has focused mainly on disease counts, with less attention to patterns among chronic conditions. Network analysis has been increasingly used to examine multimorbidity clusters, but there are no guidelines for its conduct. In 12 recent studies using network analysis, we found heterogeneity in association measures (10 different measures) and clustering algorithms (5 different methods) used to identify multimorbidity clusters. Using self-reported data on 24 diseases in community-living adults aged 45-85 from the Canadian Longitudinal Study on Aging, we conducted network analyses using the 10 association measures and 5 clustering algorithms to better understand how these choices impact the number and types of clusters identified. We compared the similarity among clusters using the adjusted Rand index (ARI); an ARI of 0 is equivalent to the diseases being randomly assigned to clusters and 1 indicates perfect agreement. Two clinicians independently identified potential disease clusters which we compared to network analyses results. We found results differed greatly across combinations of association measures and cluster algorithms. The number of clusters identified ranged from 1 to 12 and their similarity was generally very low. Compared to clinician-derived clusters, the ARIs ranged from 0 to 0.23 indicating little similarity. These analyses demonstrate the need for a systematic evaluation of the performance of network analysis methods on binary clustered data like diseases. Moreover, diseases may not cluster, and a personalized approach to the care of older adults may be needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".