Financial costs associated with monopolies on biologic medicines in Australia
Bibliographic record
Abstract
Objectives The aim of the study was to estimate the potential savings to the Pharmaceutical Benefits Scheme (PBS) and the Repatriation Pharmaceutical Benefits Scheme (RPBS) in 2015-16 if biosimilar versions of selected biologic medicines (biologics) had been available and listed on the PBS. Methods The research involved retrospective analysis of Australian Medicare expenditure data and PBS price data from 2015-16 for biologics, for which biosimilar competition may be available in future, listed on the PBS. Results Australian Government expenditure on biologics on the PBS and RPBS was estimated at A$2.29 billion dollars in 2015-16. If biosimilar versions of these medicines had been listed on the PBS in 2015-16, at least A$367million dollars would have been saved in PBS and RPBS subsidies. Modelling based on price decreases following listing of biosimilars on the PBS suggests that annual PBS outlays on biologics could be reduced by as much as 24% through the timely introduction of biosimilars. Conclusions Biologic medicines represent a large proportion of government expenditure on pharmaceuticals. Reducing the length of monopoly protections on these medicines could generate savings of hundreds of millions of dollars per year. What is known about the topic? Biologics take up an increasing share of pharmaceutical expenditure, but no previous published studies have examined Australian Government expenditure on biologics or the potential savings from reducing the duration of monopoly protection. What does this paper add? This paper provides new evidence about Australian Government expenditure on biologics and potential savings for selected medicines that are still subject to monopoly protection and thus are not yet subject to biosimilar competition. In 2015-16 Australian Government expenditure on biologics through the PBS and RPBS was estimated at A$2.29 billion dollars. If biosimilar versions of these medicines had been listed on the PBS at that time, at least A$367million dollars would have been saved. What are the implications for practitioners? Reducing the duration of monopoly protection on biologic medicines could save hundreds of millions of dollars annually that could be redirected to other areas of the healthcare system.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".