Impact of Supply Chain Disruptions and Drug Shortages on Drug Utilization: A Scoping Review
Bibliographic record
Abstract
PURPOSE: Drug shortages are a growing challenge in health systems across the world. A better understanding of the impacts of shortages on patient drug access and use will guide policies aimed at mitigating shortages. This scoping review aims to summarize observational literature assessing the impact of drug shortages on drug utilization trends. METHODS: We searched Ovid MEDLINE and Ovid EMBASE for studies published between 1946 and September 17, 2024. An extensive grey literature search was conducted through targeted website searches, grey literature databases, and the Google search engine. Observational studies examining the impacts of drug shortages on drug use were included. Study screening and extraction were conducted by two independent reviewers. RESULTS: We identified 55 published articles and five gray literature reports. Most studies were conducted in North America (n = 42, 70%). Population-level data were most used (n = 34, 57%), and most studies used drug prescription data to assess changes in use (n = 30, 55%). Most studies reported changes in drug use as a percent change, and the magnitude in decreases ranged from 1% to 99%. Many different data sources, methods, and measures were used to study the impact of drug shortages on drug utilization, and a broad range of decreases in drug utilization following the shortages were reported. CONCLUSIONS: It is important to synthesize findings across studies to understand how different drugs and settings are affected by shortages. The findings here will inform future studies aimed at filling this knowledge gap, ultimately yielding real-world evidence that can guide policy decisions to address drug supply challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| 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) | 0.001 | 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".