Trends of Dispensed Opioids in Catalonia, Spain, 2007–19: A Population-Based Cohort Study of Over 5 Million Individuals
Bibliographic record
Abstract
Objective: To characterize the trend of opioid use (number of users, dispensations and oral morphine milligram equivalents) in Catalonia (Spain). Design, setting, and participants: This population-based cohort study included all individuals aged 18 years or older, registered in the Information System for Research in Primary Care (SIDIAP), which covers >75% of the population in Catalonia, Spain, from 1 January 2007, to 31 December 2019. Main exposure and outcomes: The exposures were all commercialized opioids and their combinations (ATC-codes): codeine, tramadol, oxycodone, tapentadol, fentanyl, morphine, and other opioids (dihydrocodeine, hydromorphone, dextropropoxyphene, buprenorphine, pethidine, pentazocine). The main outcomes were the annual figures per 1,000 individuals of 1) opioid users, 2) dispensations, and 3) oral morphine milligram equivalents (MME). Results were stratified separately by opioid types, age (5-year age groups), sex (male or female), living area (rural or urban), and socioeconomic status (from least, U1, to most deprived, U5). The overall trends were quantified using the percentage change (PC) between 2007 and 2019. Results: Among 4,656,197 and 4,798,114 residents from 2007 to 2019, the number of opioid users, dispensations and morphine milligram equivalents per 1,000 individuals increased 12% (percentage change: 95% confidence interval (CI) 11.9–12.3%), 105% (95% confidence interval 83%–126%) and 339% (95% CI 289%–390%) respectively. Tramadol represented the majority of opioid use in 2019 (61, 59, and 54% of opioid users, dispensations, and total MME, respectively). Individuals aged 80 years or over reported the sharpest increase regarding opioid users (PC: 162%), dispensations (PC: 424%), and MME (PC: 830%). Strong opioids were increasingly prescribed for non-cancer pains over the years. Conclusion: Despite the modest increase of opioid users, opioid dispensations and MME increased substantially, particularly in the older population. In addition, strong opioids were incrementally indicated for non-cancer pains over the years. These findings suggest a transition of opioid prescriptions from intermittent to chronic and weak to strong and call for more rigorous opioid stewardship.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".