Factors associated with drug shortages in Canada: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: To monitor the magnitude of the drug shortage problem in Canada, since 2017, Health Canada has required manufacturers to report drug shortages. This study aimed to identify the factors associated with drug shortages in Canada. METHODS: We conducted a retrospective cohort study of all prescription drugs available on the market between Mar. 14, 2017, and Sept. 12, 2018, in Canada. All drugs of the same active ingredient, dosage form, route of administration and strength were grouped into a "market." Our main outcome was shortages at the market level, determined using the Drug Shortages Canada database. We used logistic regression to identify associated factors such as market structure, route or dosage form, and Anatomic Therapeutic Chemical (ATC) classification. RESULTS: Among the 3470 markets included in our analysis, 13.3% were reported to be in shortage. Markets with a single generic manufacturer were more likely to be in shortage than other markets. Markets with oral nonsolid route or dosage form were more likely to be in shortage than those that were oral solid with regular release (odds ratio [OR] 1.66, 95% confidence interval [CI] 1.11 to 2.49). Markets for sensory organs were more likely to be in shortage than most other ATC classes. Markets with a higher proportion of drugs covered by public insurance programs were more likely to be in shortage (OR 1.03, 95% CI 1.00 to 1.05 per 10% increase). INTERPRETATION: Markets with a single generic manufacturer were most likely to be in shortage. To ensure the security of drug supply, governments should be vigilant in monitoring markets with a single generic manufacturer, with complex manufacturing processes, with higher demand from public programs or those that are in certain ATC classes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".