MétaCan
Menu
Back to cohort
Record W2104086837 · doi:10.1002/hec.1103

A re‐examination of the impact of reference pricing on anti‐hypertensive drug plan expenditures in British Columbia

2006· article· en· W2104086837 on OpenAlexaffabout
Paul Grootendorst, David P. Stewart

Bibliographic record

VenueHealth Economics · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsMcMaster UniversityUniversity of TorontoCanadian Institutes of Health Research
Fundersnot available
KeywordsReimbursementCounterfactual thinkingContext (archaeology)Drug pricingDrugActuarial sciencePlan (archaeology)Drug pricesMedicinePublic economicsBusinessEconomicsPharmacologyHealth careEconomic growthGeographyPsychology

Abstract

fetched live from OpenAlex

Reference pricing (RP) limits drug plan reimbursement of interchangeable medicines to a reference price, which is typically equal to the price of the lowest-cost interchangeable drug; any cost above that is borne by the patient. Much of the evidence of the effects of RP comes from 'before and after' studies of the RP scheme adopted by Pharmacare, the publicly funded drug plan for seniors and others in British Columbia, Canada. We critically assess the identifying assumption inherent in the before and after design - namely, that pre-RP trends accurately predict counterfactual outcomes - in the context of estimating the impact of RP on Pharmacare's expenditure on anti-hypertensive drugs for its senior beneficiaries. We use similar data from a public plan that has not introduced RP to estimate the effects on drug expenditures of patent expiration, secular changes in prescribing patterns and various other factors common to all Canadian public drug plans that could potentially confound the before and after estimates of the effect of RP on drug plan expenditures. We find that controlling for such factors reduces estimates of drug plan savings attributable to RP of the Calcium Channel Blockers by about half.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.066
GPT teacher head0.300
Teacher spread0.234 · 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 designObservational
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

Citations25
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueHealth EconomicsSame topicPharmaceutical Economics and PolicyFrench-language works237,207