The association between drug shortages and prices across 74 countries: uncovering global access inequities
Bibliographic record
Abstract
BACKGROUND: Drug shortages are a global concern that poses risks to clinical care and health systems. Lower prices and market pressures are often cited as drivers of drug shortages; thus, a commonly proposed policy lever is to increase drug prices. However, global evidence to evaluate the association between shortages and prices is lacking. This research aims to fill the gap by examining the global markets of drug shortages. METHOD: We included 25 global shortage markets by reviewing publications on drug shortages from 2013 to 2023. We used quarterly pharmaceutical sales data across 74 countries/regions from the IQVIA MIDAS quarterly sales volume data database from Q3 2011 to Q3 2022. Our outcome of interest was shortage intensity, defined as the drug shortage duration between its onset and the end of meaningful shortages. To assess the impact of price on the occurrence and intensity of shortages, we used zero-inflated negative binomial regression, with price as the independent factor and included gross domestic product (GDP) per capita and the number of manufacturers as covariates. P values less than a Bonferroni-corrected significance level (0.00625) were considered statistically significant. RESULTS: Out of 1096 subjects (each representing a drug market in a specific country), 65% (712 subjects) experienced meaningful shortages with a median shortage intensity of 10 quarters. We found price was not a significant predictor of either shortage odds (p=0.044) or intensity (p=0.066). Factors significantly associated with increased odds of a non-shortage occurrence included a unit increase in GDP per capita (adjusted OR (aOR): 1.707, 95% CI 1.428 to 2.040) and in the number of manufacturers (aOR: 4.038, 95% CI 3.045 to 5.354), corresponding to 70.7% and 303.8% higher odds, respectively. A unit increase in GDP per capita was significantly associated with a 10.4% decrease (adjusted rate ratios: 0.896, 95% CI 0.834 to 0.962) in the duration of shortage intensity. CONCLUSION: Our study reveals global inequities in the impact of drug shortages, with countries with lower GDP per capita disproportionately affected. Persistent shortages of essential medications have been observed worldwide over the past decade, but are not evenly distributed across countries. Collectively, our findings suggest the need to consider creative and alternative policy strategies beyond pricing to address both critical drug shortages and global inequities in shortage experiences.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".