US drug shortages compared to the World Health Organization’s Model List of Essential Medicines for Children: A cross-sectional study
Bibliographic record
Abstract
PURPOSE: To describe US drug shortages affecting medications on the 2019 World Health Organization (WHO) Model List of Essential Medicines for Children (EMLc). METHODS: Drug shortage data from January 2014 to December 2019 were obtained from the University of Utah Drug Information Service. Shortage data for drugs on the EMLc were analyzed for the type of drug, American Hospital Formulary Service category, reason for the shortage, duration of the shortage, marketing status (generic vs brand name), and whether the agent was a single- or multisource drug. RESULTS: From 2014 to 2019, a total of 209 drug shortages impacted medications on the EMLc, of which 77 (36.8%) remained unresolved by 2019. Of all active shortages, 13 (6.2%) began before 2014. Resolved shortages had a median duration of 5.9 months (interquartile range [IQR], 3.6-13.2 months) while active shortages had a median duration of 18.3 months (IQR, 10.9-33.5 months; P ≤ 0.0001). The therapeutic categories most impacted by drug shortages were anti-infective agents (27.3%), central nervous system agents (12.9%), and antineoplastic agents (11.0%). The reason for the shortage was not reported in 46.4% of cases. When a reason was provided, the most common reason was manufacturing problems (29.2%) followed by supply/demand mismatch (15.8%). CONCLUSION: US drug shortages affected many medications on the WHO EMLc. Future studies should examine the global shortage climate and implications for patient care.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".