The affordability of prescription medicines in Australia: are copayments and safety net thresholds too high?
Bibliographic record
Abstract
Objective. To create and report survey-based indicators of the affordability of prescription medicines for patients in Australia. Method. A cross-sectional study of 1502 randomly selected participants in the Hunter Region of NSW, were interviewed by telephone. Main outcome measure. The self-reported financial burden of obtaining prescription medicines. Results. Data collection was completed with a response rate of 59.0%. Participants who had received and filled at least one prescription medicine in the previous 3 months, and eligible for analysis (n=952), were asked to self-report the level of financial burden from obtaining these medicines. Extreme and heavy financial burdens were reported by 2.1% and 6.8% of participants, respectively. A moderate level of burden was experienced by a further 19.5%. Low burden was recorded for participants who said that their prescription medicines presented either a slight burden (29.0%) or were no burden at all (42.6%). Conclusion. A substantial minority of participants who had obtained prescription medicines in the 3 months prior to survey experienced a level of financial burden from the cost of these medicines that was reported as being moderate to extreme. What is known about the topic? The Australian National Medicines Policy aims to, amongst other things, facilitate access to medicines at a cost that is affordable to individuals and the community. Copayments combined with the safety net and brand price premium are the main determinants of the amount that patients pay for PBS listed prescription medicines. Previous surveys have reported on selected aspects of medicine affordability in Australia and have shown some groups in the population experience difficulty with the cost of their medicines. What does this paper add? This paper develops and reports on a set of indicators that can be used to periodically measure the level of self-reported financial burden experienced by Australians when obtaining prescription medicines. The analysis assesses affordability issues for both general patients and patients who are able to access prescription medicines using a concession card. What are the implications? Our research suggests that, as they stand, the copayment and safety net thresholds are not protecting nearly one-third of Australian patients from financial burden. Ongoing monitoring and evaluation is required to ensure the copayment and safety net thresholds do not jeopardise the National Medicines Policy’s principle of equitable and affordable access to medicines.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".