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Record W1554909976

Australia’s bad drug deal: high pharmaceutical prices

2013· article· en· W1554909976 on OpenAlexaboutno aff
Stephen Duckett

Bibliographic record

VenueSSRN Electronic Journal · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationDrug pricesGovernment (linguistics)PoliticsPharmaceutical Benefits SchemeCommonwealthBusinessMedical prescriptionPublic economicsEconomicsPolitical scienceLawMedicine
DOInot available

Abstract

fetched live from OpenAlex

Australians are paying too much for prescription drugs. The cost of this overpayment is at least $1.3 billion a year, or $3.5 million a day. This equates to 14 percent of the entire Pharmaceutical Benefits Scheme (PBS) budget. In a time of escalating health costs and other strains on the Commonwealth Budget, spending on pharmaceuticals could be reduced relatively easily, if there is the political will to do so.Several good examples show the way. In New Zealand, drug prices have plunged dramatically, freeing up money to spend on new drugs and other kinds of care. New Zealand’s secret is simple. The Government has taken the politics out of price-setting and appointed independent experts to make decisions. It has also capped the budget for drugs, which ensures clear priorities and tough negotiations with pharmaceutical companies. For Australia’s PBS, by contrast, decisions on drug pricing are opaque and unconstrained by a budget. Key decisions are made by a committee inside the Department of Health and Aging, that includes among its six members two representatives of drug companies. They have little interest in keeping prices low. In New Zealand, politicians decide how much is spent on drugs in total, then independent experts negotiate prices. In Australia, expert judgements come first, but can be overridden by political decisions. No one assesses how much we should spend overall. As a result, our wholesale prices for identical drugs are now more than six times New Zealand’s. In some cases, they are more than 20 times higher. One drug alone, atorvastatin, has cost the Australian Government and individual patients more than $700 million a year. In its 40 mg. form, the PBS paid more than $51 for a box of 30 tablets. New Zealand pays AU $5.80 for a box of 90 tablets. Adopting New Zealand prices for atorvastatin would have saved the PBS more than $1.4 million a day in 2011-12. Patients who paid full co-payments would have saved $22 on each box of tablets. This report proposes three changes to get pharmaceutical prices under control. The first is to establish a truly independent expert board. Like New Zealand’s Pharmaceutical Management Agency, it would manage pharmaceutical pricing within a defined budget. The second and vital change is to pay far less for generic drugs, which can be bought for low prices because they are off-patent. In Australia, drug companies must cut prices by 16 percent when a patent expires. Many countries require much bigger cuts. Canada has mandatory cuts of 82 percent for some drugs. Australia should require a cut of at least 50 percent, then benchmark prices against the world’s best. This might seem unrealistic. But Australia’s public hospitals already pay low prices. Like New Zealand, one state’s prices are only a sixth of those on the PBS. Down the line, a third reform should encourage people to use cheaper, but similar pharmaceuticals, which could save at least $550 million a year more. The pricing agreement between the government and drug companies expires in the middle of next year. Now is the time to make changes that will end Australia’s bad drug deal.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.054
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0100.006
Open science0.0010.004
Research integrity0.0090.010
Insufficient payload (model declined to judge)0.0370.008

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.

Opus teacher head0.049
GPT teacher head0.307
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2013
Admission routes1
Has abstractyes

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