Extent of a valsartan drug shortage and its effect on antihypertensive drug use in the Canadian population: a national cross-sectional study
Bibliographic record
Abstract
<h3>Background:</h3> Drug shortages represent a growing global problem, with potentially serious consequences to patients and the health care system. Our study investigates the impacts of a major recall and shortage of valsartan, an angiotensin receptor blocker (ARB), in July 2018 in Canada. <h3>Methods:</h3> We conducted a time-series analysis of antihypertensive drugs dispensed in Canada between 2015 and 2019 using commercially available retail prescription data. Using autoregressive integrated moving average (ARIMA) modelling, we evaluated the change in valsartan use after the recall. We also measured the overall use of ARBs, angiotensin-converting-enzyme (ACE) inhibitors and other antihypertensive drug classes for the same period. <h3>Results:</h3> After the recall in July 2018, valsartan use decreased 57.8%, from 362 231 prescriptions dispensed in June 2018 to 152 892 in September 2018 (difference = 209 339, <i>p</i> < 0.0001). Overall use of the ARB drug class decreased 2.0%, from 1 577 509 prescriptions dispensed in June 2018 to 1 545 591 in September 2018 (difference = 31 918, <i>p</i> = 0.0003), but use of non-valsartan ARBs increased 14.6%, from 1 215 278 to 1 392 699 prescriptions dispensed (difference = 177 421, <i>p</i> < 0.0001) in the same time frame. Although use of ACE inhibitors initially declined, this reduction was not sustained. The valsartan recall was not associated with a significant impact on use of other antihypertensive drug classes. <h3>Interpretation:</h3> Our findings illustrate the impact of a major drug shortage, with the immediate and substantial reduction of valsartan dispensed and cascading effects on other ARBs, though future research is warranted to understand the consequences of such extensive shortages on clinical outcomes and health system costs. Improved policy strategies are needed to address the underlying causes of drug shortages and to mitigate their effects.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".