Early observations of Tier-3 drug shortages on purchasing trends across Canada: A cross-sectional analysis of 3 case-example drugs
Bibliographic record
Abstract
BACKGROUND: To curb the growing impact of drug shortages, Health Canada developed the Tiered Notification and Communication Framework which assigns potential shortages a corresponding tiered status. Tier-3 is assigned to shortages with the greatest potential impact on the healthcare system. This study aims to describe drug purchasing trends in response to Tier-3 shortages using three case-examples. METHODS: We conducted a time-series analysis of monthly purchasing data for three out of 17 Tier-3 drug shortages (hydralazine, sarilumab, and medroxyprogesterone acetate) with publicly available reports in July 2021 and available IQVIA MIDAS data from January 2016 to December 2021. We assessed percent changes in purchasing at 1-, 3-, and 6-months after the onset of each Tier-3 drug shortage and interventional ARIMA modelling was used to assess the statistical significance. RESULTS: Medroxyprogesterone acetate experienced a significant shift (p = 0.0370) in purchasing following its shortage, and the 1-, 3-, and 6-month percent changes were +14.9%, +6.8% and -3.1%, respectively. Hydralazine and sarilumab did not show a significant shift. The 1-, 3-, and 6-month percent changes for hydralazine were +15.5%, +10.2%, and +9.6% respectively and +25.2%, +45.1% and +39.2 for sarilumab. CONCLUSIONS: These results indicate that drugs assigned a Tier-3 status may not show declines in purchasing in the months following status assignment, which may be due to policy responses following the assignment. However, more insight is needed into the mechanisms through which these policy measures impact shortages and whether they are functioning as intended.
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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 | 0.000 | 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) | 0.000 | 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".