Community pharmacy naloxone supply, before and after rescheduling as an over‐the‐counter drug: sales and prescriptions data, 2014–2018
Bibliographic record
Abstract
OBJECTIVES: To characterise the community pharmacy supply of naloxone by supply type - individual prescription, prescriber bag, and non-dispensed (supplied over the counter or expired) - during 2014-2018; to examine whether the 2016 rescheduling of naloxone as an over-the-counter drug influenced non-dispensed naloxone supply volume. DESIGN, SETTING: Analysis of monthly naloxone prescriptions (Pharmaceutical Benefits Scheme) and sales data (IQVIA), 2014-2018, for Australia and by state and territory; time series analysis of non-dispensed naloxone supply to assess effect of rescheduling on naloxone supply. MAJOR OUTCOMES: Total naloxone supply to community pharmacies; prescribed and non-dispensed naloxone supply. RESULTS: During 2014-2018, 372 351 400 μg units of naloxone were sold to community pharmacies: non-dispensed naloxone accounted for 205 866.5 units (55.3%), prescriber bags for 155 841 units (41.8%), and individual prescriptions for 10 643.5 units (2.9%). Population-adjusted national naloxone sales to community pharmacies increased between 2014 and 2018 (per year: incidence rate ratio [IRR], 1.15; 95% CI, 1.09-2.22). This increase was primarily attributable to increased volumes of prescriber bag naloxone (IRR, 1.63; 95% CI, 1.50-1.78) and, to a lesser extent, increased individual prescription supply (IRR, 2.04; 95% CI, 1.85-2.26). Non-dispensed naloxone supply volume was unchanged at the national level (IRR, 0.93; 95% CI, 0.85-1.01); changes in non-dispensed supply immediately following rescheduling and subsequently were not statistically significant in time series analyses for most jurisdictions. CONCLUSIONS: Total naloxone supply to community pharmacies in Australia increased between 2014 and 2018, but rescheduling that enabled over-the-counter access did not significantly influence the volume of non-dispensed naloxone.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".