COVID-19 and the prevalence of drug shortages in Canada: a cross-sectional time-series analysis from April 2017 to April 2022
Bibliographic record
Abstract
<h3>Background:</h3> In March 2020, the Government of Canada introduced measures to reduce intensifying shortages of prescription drugs during the beginning of the COVID-19 pandemic. We sought to assess the extent to which a decline in drug shortages was observed in the months after this policy change. <h3>Methods:</h3> Our data source was the Drug Shortages Canada Database, which reports shortages by drug product, including shortage start and duration. Using a cross-sectional design, we tracked shortage rates of drug products using a 30-day moving average from Apr. 15, 2017, to Apr. 1, 2022. We used autoregressive integrated moving average modelling with a ramp function to determine the significance of trend changes after policy implementation. <h3>Results:</h3> We found that of the 13 329 drug products at risk for shortage, 44.7% (<i>n</i> = 5953) had at least 1 shortage event in the past 5 years. Average daily shortage prevalence rates rose from 901 in April 2017 to a peak of 2345 by April 2020. Significant declines (<i>p</i> = 0.02) ensued shortly thereafter, dropping to a rate of 1611 shortages by the end of the first year after policy implementation. However, we did not observe a significant reduction in shortage rates in the second year (<i>p</i> = 0.2), with rates plateauing below 1500 and then rising back above 1600 by the end of March 2022. <h3>Interpretation:</h3> Drug shortages are common in Canada, including during the initial months of the COVID-19 pandemic. We observed substantial improvements after the implementation of the new measures, but gains appear to have plateaued. Continued vigilance is needed to sustain improvements.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".