Overall medicine burden for people on lipid-lowering therapy: Cross-sectional analysis of national pharmacy dispensing data
Bibliographic record
Abstract
MEDICINES are increasingly used to control risk factors; lipid-lowering medicines are recommended for cardiovascular disease (CVD) and risk. 1 Since 2016-17, statins have been the most commonly prescribed medicines in Australia 2 and, in 2016, they were prescribed to over 40% of older people. 1 Many other chronic conditions are commonly comorbid with CVD, including diabetes, chronic lung disease, mental health disorder and autoimmune conditions, 3 so patients taking statins are often prescribed multiple additional medicines.Polypharmacy is described as taking five or more concurrent medicines; 4 the polypharmacy rate among US patients with CVD aged 65 years increased from 24% in 2000 to 39% in 2014. 5As medicine number and complexity increase, so do the risks of adverse interactions and events, 4 and reduced patient adherence. 6In Australia, most longer-term medicines have been dispensed in monthly quantities, also necessitating frequent pharmacy visits, although Pharmaceutical Benefits Scheme (PBS) changes in September 2023 have begun to address this with 60-day supply for some items.People incur financial cost for medicines.International research shows that this is associated with reduced adherence, adverse outcomes and increased expenditure for the healthcare system. [7][8]][9] The Australian PBS mitigates cost with a fixed patient co-payment for each prescription dispensed.In 2019 this was capped at $6.60 for low-income and disadvantaged 'concessional' beneficiaries, and at $40.30 for general beneficiaries.The PBS-published Dispensed Price for Maximum Quantity (DPMQ) indicates, for each medicine, the maximum chargeable by pharmacies, including all fees and mark-ups. 10Co-payments from registered family members contribute towards a PBS safety net threshold; in 2019 this was $390 for concessional and $1550.70 for general beneficiaries.After reaching this threshold, co-payments were removed for concessional beneficiaries and reduced to $6.60 for general beneficiaries. 11 The aim of this study was to investigate the largely unknown medicine burden and costs for both general and concessional beneficiaries dispensed with lipid-lowering medicines in Australia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".