International trends in prescription opioid sales among developed and developing economies, and the impact of the <scp>COVID</scp>‐19 pandemic: A cross‐sectional analysis of 66 countries
Bibliographic record
Abstract
PURPOSE: We sought to compare trends in opioid purchasing between developed and developing economies to understand patterns of opioid consumption, and how they were impacted by the COVID-19 pandemic. METHODS: We conducted a retrospective cross-sectional study of retail pharmacy opioid sales from 66 jurisdictions between July 2014 and August 2020. We measured monthly population-adjusted rate of opioid units purchased, stratified by development group and country, and used interventional time series analysis to assess the impact of the COVID-19 pandemic on rates of opioid purchasing among developed and developing economies separately. RESULTS: Rates of opioid purchasing were generally higher among developed economies, although trends differed considerably by development group. Rates of opioid purchasing declined 23.8% (95% confidence interval [CI] -34.7% to 3.6%) in the 5 years prior to the pandemic in developed economies, but rose 15.2% (95% CI 4.6%-35.6%) among developing economies. In March 2020 there was a short-term increase in the rate of opioid purchases in both developing (10.9 units/1000 population increase; p < 0.0001) and developed (145.5 units/1000 population; p < 0.0001) economies, which was followed immediately by reduced opioid purchasing of a similar scale in April-May 2020 (-14.8 and -171.8 units/1000 population in developing and developed economies, respectively; p < 0.0001). CONCLUSION: The COVID-19 pandemic led to disruptions in opioid purchasing around the world; although the specific impacts varied both between and among developed and developing economies. With global variation in opioid use, there is a need to monitor these trajectories to ensure the safety of opioid use, and adequate access to pain management globally.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".