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Record W2161016869 · doi:10.1377/hlthaff.2014.0658

Compulsory Licensing Often Did Not Produce Lower Prices For Antiretrovirals Compared To International Procurement

2015· article· en· W2161016869 on OpenAlexafffund
Reed F. Beall, Randall Kuhn, Amir Attaran

Bibliographic record

VenueHealth Affairs · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsInstitute of Population and Public Health
FundersCanadian Institutes of Health Research
KeywordsProcurementLicenseBusinessDeveloping countryQuality (philosophy)Work (physics)Public economicsInternational economicsEconomicsEconomic growthMarketingPolitical scienceLaw

Abstract

fetched live from OpenAlex

Compulsory licensing has been widely suggested as a legal mechanism for bypassing patents to introduce lower-cost generic antiretrovirals for HIV/AIDS in developing countries. Previous studies found that compulsory licensing can reduce procurement prices for drugs, but it is unknown how the resulting prices compare to procurements through the Global Fund to Fight AIDS, Tuberculosis, and Malaria; UNICEF; and other international channels. For this study we systematically constructed a case-study database of compulsory licensing activity for antiretrovirals and compared compulsory license prices to those in the World Health Organization's (WHO's) Global Price Reporting Mechanism and the Global Fund's Price and Quality Reporting Tool. Thirty compulsory license cases were analyzed with 673 comparable procurements from WHO and Global Fund data. Compulsory license prices exceeded the median international procurement prices in nineteen of the thirty case studies, often with a price gap of more than 25 percent. Compulsory licensing often delivered suboptimal value when compared to the alternative of international procurement, especially when used by low-income countries to manufacture medicines locally. There is an ongoing need for multilateral and charitable actors to work collectively with governments and medicine suppliers on policy options.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.668
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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.

Opus teacher head0.199
GPT teacher head0.368
Teacher spread0.170 · 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.

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

Citations37
Published2015
Admission routes2
Has abstractyes

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