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Record W4389477446 · doi:10.1007/s40273-023-01325-z

A Framework for the Fair Pricing of Medicines

2023· article· en· W4389477446 on OpenAlexafffund
Mike Paulden

Bibliographic record

VenuePharmacoEconomics · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsEconomic surplusHealth economicsIncentivePublic economicsHealth careWillingness to payMarginal costEconomicsPublic healthPopulationFair valueHealth administrationPopulation healthActuarial scienceBusinessWelfareMicroeconomicsMedicineFinanceEconomic growth

Abstract

fetched live from OpenAlex

As high-cost medicines put increasing pressure on public health care budgets, the need to identify 'fair' prices for medicines has never been greater. This paper proposes a framework, built upon fundamental economic principles, that allows for the consideration of 'fair' prices for medicines. The framework incorporates key considerations from conventional supply-side and demand-side approaches for specifying a cost-effectiveness 'threshold', including the health opportunity cost borne by other patients ([Formula: see text]) and society's willingness to pay for marginal improvements in population health ([Formula: see text]). The costs incurred by manufacturers in developing and supplying new medicines are also considered, as are the incentives for manufacturers to strategically price up to any common price per unit of benefit (cost-effectiveness 'threshold') specified by the payer. The framework finds that, at any 'fair' price, a medicine's dynamically calculated incremental cost-effectiveness ratio (ICER) lies below [Formula: see text]. When pricing medicines collectively, the framework finds that a common price below [Formula: see text] is required to maximize population health (consumer surplus) or to maximize total welfare (consumer and producer surplus). This framework has important policy implications for payers who wish to improve population health outcomes from constrained health care budgets. In particular, existing approaches to 'value-based pricing' should be reconsidered to ensure that patients receive a 'fair' share of the resulting economic surplus.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.472
GPT teacher head0.525
Teacher spread0.053 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations22
Published2023
Admission routes2
Has abstractyes

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