International Trends in Opioid Prescribing by Age and Sex from 2001 to 2019: An Observational Study Using Population-Based Databases from 18 Countries and One Special Administrative Region
Bibliographic record
Abstract
OBJECTIVE: To characterize multinational trends and patterns of opioid analgesic prescribing by sex and age. DESIGN, SETTING, AND PARTICIPANTS: We studied opioid analgesic prescribing from 2001 to 2019 with common protocol using population-based databases from eighteen countries and one special administrative region. MAIN OUTCOME MEASURES: We measured opioid prescribing by geographical region, sex and age, estimating annual prevalent, incident, and nonincident opioid prescribing per 100 population with a 95% confidence interval (CI) and meta-analyzed the multinational and regional opioid prescribing with a random-effects model. Time trends were reported through average annual absolute changes, estimated using linear mixed models. We further explored the effect of sex and age on prevalent opioid prescribing in the multivariable analysis. RESULTS: Over 248 million individuals were included. Pooled multinational opioid prescribing prevalence was 9.0% amongst included countries/regions. Opioid prescribing prevalence in 2015 ranged from 2.7% in Japan to 19.7% in Iceland. Average annual absolute changes in opioid prescribing prevalence per year ranged from - 1.53% (95% CI - 2.06, - 1.00; United States Medicaid) to + 1.24% (95% CI 1.02, 1.46; South Korea). Pooled multinational incident opioid prescribing (4.9%; 95% CI 4.1, 5.9) was higher than pooled multinational nonincident opioid prescribing (3.7%; 95% CI 2.9, 4.8). The female sex and older age were associated with higher opioid prescribing. Main limitations of this study include the absence of data from study duration or individuals not covered by the data sources and the lack of information on medication adherence and indication. CONCLUSIONS: Opioid prescribing remains unbalanced across geographical regions; however, results suggest a tendency to convergence across countries/regions. Differences in opioid prescribing by sex and age were identified.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".