Trends in drug revenue among major pharmaceutical companies: A 2010‐2019 cohort study
Bibliographic record
Abstract
BACKGROUND: Over the past 2 decades there has been a substantial increase in the number of new cancer medicines; this has been accompanied by a dramatic rise in drug costs. It is unknown how these trends impact the revenue of the pharmaceutical sector. METHODS: Retrospective cohort study to characterize temporal trends of revenue generated from cancer medicines as a proportion of total drug revenue among 10 large pharmaceutical companies from 2010 to 2019. Itemized product-sales data publicly available through company websites or annual filings were used to identify annual drug revenue. Revenue data were adjusted for inflation and converted to 2019 US dollars. RESULTS: During the study period, cumulative annual revenue generated from cancer drugs increased by 70%: from $55.8 billion to $95.1 billion, while cumulative revenue from nononcology drugs decreased 18%: from $342.2 billion to $281.5 billion. The proportion of total drug revenue generated from oncology drugs increased substantially over the study period: from 14% in 2010 to 25% in 2019 (τ = 1.0, P < .001). CONCLUSIONS: Among 10 of the world's largest pharmaceutical companies, revenues generated from the sale of cancer drugs have increased by 70% over the past decade, while revenues from other medicines have decreased by 18%. Revenues from cancer drugs now account for one-quarter of the net revenues from these companies. Further work is needed to understand if this increase in sales revenue reflects industry profit, and to what extent increased spending has translated into improvements in patient and population outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".