Unpacking excessive polypharmacy patterns among individuals living with chronic pain in Quebec: a longitudinal study
Bibliographic record
Abstract
Introduction: Excessive polypharmacy, which can be defined as the concurrent use of ≥10 medications, is prevalent among individuals with chronic pain. However, it remains unclear how it may vary between individuals or over time. Objectives: This study aimed to describe and identify factors associated with trajectories of excessive polypharmacy. Methods: A retrospective longitudinal study was conducted using the TorSaDE Cohort, which links Canadian Community Health Surveys (2007-2016) and Quebec health administrative databases. Among 9,156 adults living with chronic pain and covered by public prescribed drug insurance, the presence of excessive polypharmacy (yes/no) was assessed monthly for one-year post-survey completion (12 time points). Group-based trajectory modelling was applied to identify groups with similar patterns over time (trajectories). Multivariable multinomial regression was used to identify factors associated with trajectory membership. Results: Four trajectories were obtained: (1) "No excessive polypharmacy" (74.8%); (2) "Sometimes in excessive polypharmacy" (8.6%); (3) "Often in excessive polypharmacy" (6.1%); 4) "Always in excessive polypharmacy" (10.5%). Factors associated with the "always in excessive polypharmacy" trajectory membership were: being older, being born in Canada, having a lower income, having a higher comorbidity index score, more severe pain intensity, and more daily activities prevented by pain, reporting arthritis or back pain and poorer perceived general health, and having a family physician. Using opioids or benzodiazepines, having a lower alcohol consumption, doing less physical activity, a higher number of prescribers and visits to a family physician also predicted being always in excessive polypharmacy. Discussion: This study identifies distinct trajectories of excessive polypharmacy in adults with chronic pain, emphasizing key sociodemographic and clinical factors and the need for tailored interventions for effective medication management.
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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 | 0.016 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".