The Life Cycle of Pharmaceuticals: A Cross-National Perspective
Bibliographic record
Abstract
This study provides a life cycle perspective on cross-national differences in pharmaceutical prices, volumes and expenditures. Most previous studies have focused solely on cross-national differences in drug prices, comparing prices for a small sample of branded products at a single point in time. However, policymakers are increasingly concerned with total drug expenditures, which reflect volume and average prices for all products. Similarly, the pharmaceutical industry, in making decisions about pricing and launch of a drug in different countries, is concerned not only with launch price but also with the total life cycle profile of price, volume and lifetime return on investment. Globally, both policymakers and industry should be concerned with the relative contributions of different countries to the common costs of pharmaceutical research and development (R&D). These relative contributions depend not only on relative prices but also on per capita consumption and hence total expenditures. The life cycle price, volume and expenditure profiles reported here address some of these policy and corporate questions, while also making a contribution to the methodology of cross-national comparisons for medicines. In this study we use comprehensive data on outpatient sales for all drugs in seven countries (Canada, France, Germany, Italy, Japan, the UK, and the US) for the period 1981-1992 to provide cross-national comparisons of the profiles for average price, per capita volume and expenditure over the life of a molecule. Since our price data are at the manufacturer level, our measures can be interpreted as per capita expenditure on pharmaceuticals from the payer's perspective or per capita revenue from the manufacturer's perspective. The life cycle expenditure profiles are also converted to a discounted present value at launch, which provides a rough summary measure of each country's per capita contribution to the common costs of R&D.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".