Trends in Opioid and Gabapentinoid Utilization: A Time‐Series Analysis Across 72 Countries From 2012 to 2023
Bibliographic record
Abstract
PURPOSE: We compare trends in gabapentinoid and opioid utilization overall and by economic development category. We also sought to predict future trends and assess correlations in gabapentinoid and opioid utilization. METHODS: We conducted a repeated cross-sectional analysis of retail prescriptions for 72 countries from Q1 2012 to Q3 2023. We measured standardized units/1000 population for gabapentinoid and opioid sales, stratified by development category, and used time-series models to predict trends for the following 3 years. Granger causality tests examined predictive relationships between gabapentinoid and opioid sales. RESULTS: Global gabapentinoid annual sales rose by 114.5% from 2012 to 2022, with a higher increase in developing (180.9%) than developed economies (110.0%). In contrast, annual opioid sales declined globally by 25.4%, with a 27.9% decrease in developed and a 16.8% increase in developing economies. Assuming current trends persist over the following 3 years, gabapentinoid quarterly sales are forecasted to rise by 7.7% in developed and 18.6% in developing economies, while opioid quarterly sales are expected to decrease by 9.5% and increase by 15.1%, respectively. Granger causality tests indicated that gabapentinoids may predict opioid sales globally for the following year, but opioids did not predict gabapentinoid sales. CONCLUSION: We evaluated the global trends in gabapentinoid and opioid sales, suggesting important differences in pain management practices across developed and developing economies. Our findings highlight the need to ensure the safe use of gabapentinoids and opioids while balancing proper pain management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".