A systematic review and meta-analysis of disease clusters in multimorbidity
Bibliographic record
Abstract
There is a growing body of research on disease clusters in multimorbidity. Here we systematically review clustering methodologies and perform a meta-analysis of disease cluster stability across the literature, searching Medline and EMBASE from inception to June 5th, 2024, for studies of disease clusters in multimorbidity (including network approaches). Here we include 79 articles. 30% of studies had high risk of bias. Hierarchical cluster analysis was the most used clustering methodology (25% of analyses), followed by latent class analysis (20%) and K-center clustering (15%). Network-based approaches were used in 19% of studies. We perform a meta-analysis of 1226 disease clusters across 73 studies. Strong relationships emerged between neurological, autoimmune, musculoskeletal, and cardiovascular diseases. We identify six meta-analytic disease clusters with moderate stability (Jaccard index ≥0.51), these largely featured cardiometabolic conditions. No disease clusters had high stability (Jaccard ≥0.75) and very few accounted for disease temporality. Multimorbidity disease clustering research is heterogeneous regarding disease definitions, the number of diseases included, and clustering methodologies. Despite this heterogeneity, moderately consistent disease clusters emerge. We provide suggestions to improve the performance and reporting of multimorbidity clustering research. There is a growing body of research on disease clusters in multimorbidity, necessitating a systematic review and meta-analysis of methods and findings. Here, the authors show the range of methods applied, and identify six disease clusters with moderate stability across the multimorbidity literature.
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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 | 0.038 | 0.100 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.044 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".