A comparison of opioids and benzodiazepines dispensing in Australia
Bibliographic record
Abstract
BACKGROUND: Inappropriate utilization of prescription opioids and benzodiazepines is a public health problem. This study examined and compared user-types and trends in dispensing of these medicines, and identified associated factors related to the duration of dispensing in Australia. METHODS: A random 10% sample of unit-record data of opioids and benzodiazepines dispensed nationally during 2013-2016 was analyzed. Users were categorized into four types: single-quarter (i.e., three months), medium-episodic (dispensed 2-6 quarters), long-episodic (dispensed 7-11 quarters), chronic (dispensed 12-16 quarters). Dispensing quantity was computed in defined daily dose (DDD). Generalized multilevel ordinal models were developed to examine the factors associated with the duration of dispensing. RESULTS: There were similarities in terms of trends of dispensing of opioids and benzodiazepines in Australia. Overall, more people were dispensed opioids than benzodiazepines. Around 52% of opioids users and 46% of benzodiazepines users were dispensed these medicines for a single quarter. However, chronic users were dispensed 60% of opioids and 50% of benzodiazepines in DDD/1000 people/day, respectively. On average, 16.6 DDD/1000 people/day of opioids and 14.2 DDD/1000 people/day of benzodiazepines were dispensed in Australia during the study period. Tasmania was dispensed the highest quantity (in DDD/1000 people/day) of these medicines, followed by South Australia and Queensland. Women compared to men, and clients of age-group 20-44, 45-64 and 65+ compared to age-group 0-19, were significantly more likely to have dispensed opioids/benzodiazepine for a relatively long duration. Clients with a history of dispensing of one of these two medicines were significantly more likely to have dispensed the other for a relatively long period. CONCLUSIONS: There were similarities in patterns of dispensing of opioids and benzodiazepines in terms of user characteristics and structural variables. Consistent use of real-time drug monitoring program and tailored intervention are recommended.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".