High‐Cost Users of Prescription Drugs: A Population‐Based Analysis from British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVE: To examine variation in pharmaceutical spending and patient characteristics across prescription drug user groups. DATA SOURCES: British Columbia's population-based linked administrative health and sociodemographic databases (N = 3,460,763). STUDY DESIGN: We classified individuals into empirically derived prescription drug user groups based on pharmaceutical spending patterns outside hospitals from 2007 to 2011. We examined variation in patient characteristics, mortality, and health services usage and applied hierarchical clustering to determine patterns of concurrent drug use identifying high-cost patients. PRINCIPAL FINDINGS: Approximately 1 in 20 British Columbians had persistently high prescription costs for 5 consecutive years, accounting for 42 percent of 2011 province-wide pharmaceutical spending. Less than 1 percent of the population experienced discrete episodes of high prescription costs; an additional 2.8 percent transitioned to or from high-cost episodes of unknown duration. Persistent high-cost users were more likely to concurrently use multiple chronic medications; episodic and transitory users spent more on specialized medicines, including outpatient cancer drugs. Cluster analyses revealed heterogeneity in concurrent medicine use within high-cost groups. CONCLUSIONS: Whether low, moderate, or high, costs of prescription drugs for most individuals are persistent over time. Policies controlling high-cost use should focus on reducing polypharmacy and encouraging price competition in drug classes used by ordinary and high-cost users alike.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".