Societal monetary benefits of pharmaceutical innovation: the case of ramipril in Canada
Bibliographic record
Abstract
To estimate the monetary benefits of ramipril and its distribution over time among four beneficiaries in Canada: the drug developing manufacturer, generic manufacturers, the healthcare sector and employment sectors. Monetary benefits to developing and generic manufacturers were defined as the profits from the drug. A dynamic Markov model was used to estimate monetary benefits to the healthcare and employment sectors in terms of cost avoidance associated with prevented cardiovascular events, including stroke, myocardial infarction and heart failure, and lost productivity due to disability and premature death in the working population. Cumulative monetary benefits of ramipril over 18 years (1994–2011) were estimated at CA$3.9 billion, of which the developing manufacturer accounted for 41%, generic manufacturers 4%, the healthcare sector 40% and employment sectors 15%. The benefits for the developing manufacturer were dominant before, but dramatically decreased after, the patent period. Benefits for the healthcare sector started to decrease from 2007, corresponding to the decreasing population taking ramipril during the same period. Higher compliance or efficacy lead to larger benefits for the healthcare and employment sectors, whereas monetary benefits for manufacturers were unchanged. Societal monetary benefits of ramipril are distributed differently for the four beneficiaries and over time. Patent, compliance and efficacy play a vital role in the determination of the benefits. High pricing of generic drugs probably makes the benefits to the healthcare and employment sectors lower than expected because that most likely influences some patients to discontinue or switch to alternative drugs.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".