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Lowering Generic Drug Prices

2003· article· en· W2316837318 on OpenAlexaffabout
Aslam H. Anis, Daphne Guh, John Woolcott

Bibliographic record

VenueMedical Care · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsCentre for Health Evaluation and Outcome SciencesProvidence Health Care
Fundersnot available
KeywordsFormularyProcurementGeneric drugDrug pricesBusinessGovernment (linguistics)EconomicsDemographic economicsAgricultural economicsMonetary economicsDrugMedicineMarketingPharmacology

Abstract

fetched live from OpenAlex

BACKGROUND: In Ontario, Canada, the 70/90 regulations were instituted in May 1993 to establish provincial government procurement prices for generic drugs. Accordingly, the first generic entrant's price could not exceed 70% of the incumbent's branded price. Subsequent entrants' prices could not exceed 90% of the first entrant's price. OBJECTIVE: These regulations' impact on generic market competitiveness are evaluated. DESIGN AND METHODS: Data on 518 drugs spanning nine therapeutic classifications were collected for the period of 04/01/1987 to 12/31/1998 from Ontario Drug Benefit formulary and IMS Canada. The period 04/01/1987 to 04/30/1993 was defined as the before period (BP) and 05/01/1993 to 12/31/1998 was the after period (AP). We compared the price ratio (P = P(G)/P(B) ) between BP and AP and performed regression analysis to assess the determinants of P. RESULTS: in both the BP and AP decreased as the number of generic firms increased within these periods. However, this decrease in was significantly less in the AP (median: 0.75 --> 0.68 --> 0.67) than in BP (0.71 --> 0.61 --> 0.53) as the number of generics increased from 1 to 2 to 3, respectively. The regression analysis showed that the price ratio in the AP was higher than that in the BP by 0.05, 0.09, and 0.13 for first, second, and third generic entrant respectively. CONCLUSIONS: Our findings show that the 70/90 regulations not only failed to achieve their goal of lowering the procurement price but instead the opposite occurred. The mandated procurement price became a focal point and resulted in a clustering of prices around the maximum allowable levels with little price dispersion.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0050.001

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 head0.042
GPT teacher head0.277
Teacher spread0.235 · 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; both teacher heads agree on what is shown here.

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

Citations39
Published2003
Admission routes2
Has abstractyes

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