BENEFITS OF PHARMACEUTICAL INNOVATION: THE CASE OF SIMVASTATIN IN CANADA
Bibliographic record
Abstract
BACKGROUND: The benefits of pharmaceutical innovations are widely diffused; they accrue to the healthcare providers, patients, employers, and manufacturers. We estimate the societal monetary benefits of simvastatin in Canada and its distribution among different beneficiaries overtime. METHODS: Monetary benefits to developing and generic manufacturers were estimated by calculating public and private revenues minus the development costs of simvastatin and the contribution toward further research and development. We used a dynamic Markov model to estimate monetary benefits to healthcare and employment sectors in terms of cost avoidance associated with prevented cardiovascular events, including stroke and myocardial infarction, and lost productivity due to disability and premature death in working population. RESULTS: Cumulative monetary benefits of simvastatin from 1990 to 2009 were $4.8 billion (2010 CA$), of which developing and generic manufacturers, and healthcare and employment sectors accounted for 32 percent, 27 percent, 32 percent, and 9 percent, respectively. The yearly trend showed that after the patent expired in 2002 the generic manufacturers became dominant in the market. Benefits for the healthcare sector started to decrease from 2003 corresponding to the decreasing population taking simvastatin during the same time period. Sensitivity analysis showed the higher the compliance or the efficacy, the larger the benefits to healthcare and employment sectors, while monetary benefits for manufacturers were unchanged. CONCLUSIONS: Societal monetary benefits of simvastatin are significant and the distributions of the benefits have changed overtime. Patent, compliance, and efficacy play a vital role in the estimation of the benefits. Analysis of all beneficiaries separately overtime is important when assessing the value of pharmaceutical innovation.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".