Do policies that allow access to unregistered antimicrobials address the unmet need? Australia as a case study of a high-income country with universal healthcare
Bibliographic record
Abstract
Background: Ensuring timely and equitable access to effective and optimal antimicrobials is crucial for optimal patient care, to minimize the use of less appropriate treatment options and reduce the risk of antimicrobial resistance (AMR). Objectives: To determine the average time for new antibacterials to gain registration for use in Australia after obtaining marketing approval internationally, and to quantify the use of 'new' and older unregistered antimicrobials in Australian clinical practice between 2018 and 2023. Methods: Two data sources were utilized to estimate the usage of antimicrobials not registered for use in Australia. Annual hospital inpatient usage data were sourced from the National Antimicrobial Utilisation Surveillance Program (NAUSP) and data on Special Access Scheme (SAS) applications for unregistered antimicrobial was sourced from the Australian Government Department of Health and Aged Care. Results: Between 2018 and 2023 there were 36 131 applications to access unapproved antimicrobials in Australia. In 26.6% of cases, access to an unapproved antimicrobial was for the treatment of a critically ill patient. Levofloxacin, pyrazinamide, tetracycline and pristinamycin were the most frequently accessed unregistered antimicrobials. Applications for 'new' antibacterials increased from 55 in 2018 to 249 in 2023. Inpatient use of nine new antibacterials was reported in Australian hospitals in 2023, two registered and seven unregistered. Conclusions: Unapproved antimicrobials are frequently accessed by clinicians for patients unable to be treated with registered antimicrobials in Australia. Policy reform and economic incentives are required to support the registration of antimicrobials needed for otherwise untreatable infections and to ensure the sustainability of supply.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".