North American cost analysis of brand name versus generic drugs for the treatment of glaucoma
Bibliographic record
Abstract
Monali S Malvankar-Mehta,1,2 Lucy Feng,3 Cindy ML Hutnik1,4 1Department of Ophthalmology, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario N6A 4V2, Canada; 2Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario N6A 5C1, Canada; 3School of Pharmacy, University of Waterloo, Kitchener, Ontario N2G 2B2, Canada; 4Department of Pathology, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON N6A 5C1, CanadaCorrespondence: Monali S Malvankar-MehtaDepartment of Ophthalmology, St. Joseph’s Hospital, 268 Grosvenor Street, London, Ontario N6A 4V2, CanadaTel +1 519 685 8500 Ext 61288Fax +1 519 661 3766Email Monali.Malvankar@schulich.uwo.caBackground: According to the World Health Organization, glaucoma is a leading cause of irreversible blindness worldwide. By 2020, 80 million people will be affected by glaucoma in the world, which represents a significant financial burden to society. Glaucoma medications alone make up 38–52% of the total direct cost. The purpose of this research is to conduct a cost-minimization analysis to evaluate brand-name medications versus generic medications for treating glaucoma patients.Methods: The per-bottle cost (in Canadian dollars) of brand-name drugs for glaucoma was obtained from the wholesaler, McKesson Canada, and, for generic drugs, from the Ontario Drug Benefit (ODB) Formulary. Further, a wastage adjustment fee, a pharmacy mark-up, and an ODB dispensing fee ($CAD) was added to the cost of both brand and generic. Previously published frequencies of medication prescription were utilized to calculate the average annual cost for each class of brand and generic. For each medication class and for mono-, bi-, and tri-drug therapy, the cost differential between brands and generics over a six-year period was computed and analyzed from third-party payer perspective.Results: In descending order, the average annual government-funded health care system costs were: combination drugs such as Cosopt® ($748.23) were the most expensive, followed by prostaglandin analogs ($246.36), carbonic anhydrase inhibitors (CAIs) ($45.04), α-agonist ($30.34), β-blockers ($29.29), and cholinergic agonists ($16.51). Brand-name mono-drugs are 34% more expensive compared to generics. Brand-generic percentage cost differential for various medication classes over a six-year period was the highest for prostaglandin analogous (44%), followed by β-blockers (35%), α-agonist (31%), cholinergic agonists (22%), combination drugs (10%), and CAIs (1%).Conclusion: Brand-name drugs are relatively more expensive than their generic counterparts, with variable cost differentials depending on drug class.Keywords: glaucoma, open-angle, cost analysis, generic drugs, branded drugs
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".