Temporal multimorbidity patterns and cluster identification: a longitudinal analysis of administrative data
Bibliographic record
Abstract
BACKGROUND: Multimorbidity is analytically and clinically complex, involving multiple interactions between diseases each with unique implications for health. Identifying disease co-occurrence patterns at the population level could aid in disease prevention, management, and care delivery. METHODS: Here, we analyzed multimorbidity patterns using linked administrative data from a longitudinal cohort of 1,347,820 individuals with multimorbidity over 20 years in British Columbia, Canada. A directed network-based approach was used to assess disease patterns in multimorbidity by frequency (prevalence) and non-random association (lift). We applied a community detection algorithm to identify multimorbidity disease clusters. RESULTS: Mood and anxiety disorders and hypertension were the most common disease predecessors in prevalence networks, with differences between age groups. Lift networks revealed non-random disease associations. Some indicate potential etiological disease relationships (e.g., breast cancer preceding heart disease in young women), shared risk profiles (e.g., chronic obstructive pulmonary disease and lung cancer), or overlapping disease constructs (e.g., Parkinsonism and dementia). Disease clusters often centered around a single disease as a common predecessor or consequence, representing potential multimorbidity profiles, which may be relevant for patient subgrouping or management. CONCLUSIONS: Insights from these analyses can complement traditional chronic disease surveillance methods, flagging disease patterns for further interrogation into their impacts on function, mortality, and health service utilization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".