Person-level changes in oxycodone use after the introduction of a tamper-resistant formulation in Australia
Bibliographic record
Abstract
BACKGROUND: Australia introduced tamper-resistant controlled-release (CR) oxycodone in April 2014. We quantified the impact of the reformulation on dispensing, switching and poisonings. METHODS: We performed interrupted time-series analyses using population-representative national dispensing data from 2012 to 2016. We measured dispensing of oxycodone CR (≥ 10 mg), discontinuation of use of strong opioids and switching to other strong opioids after the reformulation compared with a historical control period. Similarly, we compared calls about intentional opioid poisoning using data from a regional poisons information centre. RESULTS: After the reformulation, dispensing decreased for 10–30 mg (total level shift −11.1%, 95% confidence interval [CI], −17.2% to −4.6%) and 40–80 mg oxycodone CR (total level shift −31.5%, 95% CI −37.5% to −24.9%) in participants less than 65 years of age but was unchanged in people 65 years of age or older. Compared with the previous year, discontinuation of use of strong opioids did not increase (adjusted hazard ratio [HR] 0.95, 95% CI 0.91 to 1.00), but switching to oxycodone/naloxone did increase (adjusted HR 1.54, 95% CI 1.32 to 1.79). Switching to morphine varied by age (p < 0.001), and the greatest increase was in participants less than 45 years of age (adjusted HR 4.33, 95% CI 2.13 to 8.80). Participants switching after the reformulation were more likely to be dispensed a tablet strength of 40 mg or more (adjusted odds ratio [OR] 1.40, 95% CI 1.09 to 1.79). Calls for intentional poisoning that involved oxycodone taken orally increased immediately after the reformulation (incidence rate ratio (IRR) 1.31, 95% CI 1.05–1.64), but there was no change for injected oxycodone. INTERPRETATION: The reformulation had a greater impact on opioid access patterns of people less than 65 years of age who were using higher strengths of oxycodone CR. This group has been identified as having an increased risk of problematic opioid use and warrants closer monitoring in clinical practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".