The utilization of new oncology drugs: A global perspective
Bibliographic record
Abstract
6612 Background: A number of new innovative cancer drugs have recently been approved or are in the process of being approved. We have analysed the access and uptake of 65 oncology drugs in 25 countries (19 European countries, Australia, Canada, Japan, New Zealand, South Africa and the USA) over a 10 year period based on sales data provided by IMS Health. Methods: We calculated an index of number of patients treated based on sales per inhabitant or per person who died from a specific cancer type. The age composition (vintage) of the drug arsenal used was calculated based on sales for cancer drugs introduced before 1995; between 1995–1999, 2000–2002 and after 2002 respectively. The vintage of the drug arsenal used was also analysed in relation to different economic and health care system characteristics. We performed three types of analysis of the effect of cancer drug vintage on cancer survival and mortality using difference-in-difference research designs. Results: Different patterns of uptake were seen in the countries studied, both with respect to speed of uptake and level of use. Fast uptake of most new drugs was seen in Austria, France, Switzerland, Spain and the USA, and slow uptake as well as low usage was seen in Poland, Hungary, New Zealand, South Africa and the UK. For some of the most recently approved drugs the variation in uptake is especially marked. The vintage of the cancer drug “arsenal” used also differs significantly between countries. Nearly half (44%) of the observed improvement in the two-year cancer survival rate between 1992 and 2000 at 50 USA cancer centres could be attributed to the use of newer cancer drugs. Around one sixth (14% − 19%) of the inter-country differences in 5-year cancer survival rates across 5 major European countries is due to differences in the uptake of newer drugs (post-1985) in each country. Nearly one third (30%) of the decline in cancer mortality rates seen during the period 1995 –2003, could be accounted for by the use of newer drugs. The observed decrease in mortality of 16% would have been only 11% if newer drugs had not been used. Conclusions: Patient access to innovative cancer drugs varies significantly between countries affecting mortality rates, and further research is needed into the determinants and consequences of these variations. No significant financial relationships to disclose.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".