Twenty‐five years of prescription opioid use in Australia: a whole‐of‐population analysis using pharmaceutical claims
Bibliographic record
Abstract
AIM: The aim of this paper is to investigate 25-year trends in community use of prescribed opioid analgesics in Australia, and to map these trends against major changes to opioid registration and subsidy. METHODS: We obtained dispensing data from 1990 to 2014 from two sources: dispensing claims processed under Australia's national drug subsidy programme, the Pharmaceutical Benefits Scheme, including under co-payment records from 2012; and estimates of non-subsidized medicine use from a survey of Australian pharmacies (until 2011). Utilization was expressed in defined daily doses (DDD)/1000 population/day. RESULTS: Opioid dispensing increased almost four-fold between 1990 and 2014, from 4.6 to 17.4 DDD/1000 pop/day. In 1990, weak, short-acting or orally administered opioids accounted for over 90% of utilization. Use of long-acting opioids increased over 17-fold between 1990 and 2000, due primarily to the subsidy of long-acting morphine and increased use of methadone for pain management. Between 2000 and 2011, oxycodone, fentanyl, buprenorphine, tramadol and hydromorphone use increased markedly. Use of strong opioids, long-acting and transdermal preparations also increased, largely following the subsidy of various opioids for noncancer pain. In 2011, the most dispensed opioids were codeine (41.1% of total opioid use), oxycodone (19.7%) and tramadol (16.1%); long-acting formulations comprised approximately half, and strong opioids 40%, of opioid dispensing. CONCLUSIONS: Opioid utilization in Australia is increasing, although these figures remain below levels reported in the US and Canada. The increased use of opioids was largely driven by the subsidy of long-acting formulations and opioids for the treatment of noncancer pain.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".