An exploration of how a countries' HTA process can affect patient access.
Bibliographic record
Abstract
e18052 Background: Drugs in the USA become available from the moment of FDA approval. Access to oncology therapies outside of the USA may be delayed by regulatory and additional payer HTA processes. This study aimed to examine differences in oncology conditions that may impact time from regulatory approval to an HTA reimbursement decision in countries with mandatory HTA. Methods: Oncology HTAs (N = 569) for medicines approved by the EMA, Health Canada, and the Therapeutic Goods Administration (Australia) were matched on indication with HTAs from France, Germany, Canada, England, Scotland, and Australia. Resubmissions were excluded. The date of the first reimbursement decision was subtracted from the date of the regulatory approval to determine the time to issue a reimbursement decision. Trends by disease were examined. Results: The time between regulatory approval and reimbursement decision was significantly longer for solid-state oncology drugs than for hematology oncology drugs (mean 344 days vs. 280 days, respectively; p = 0.03). Within hematology oncology, Non-Hodgkin’s Lymphoma had the shortest time to a decision (229 days; n = 37) while Myelofibrosis had the longest (411 days; n = 3). For solid-state diseases, Sarcoma had the shortest time (5 days; n = 2) and Small-cell Lung Cancer had the longest (752 days; n = 5) time to a decision. Conclusions: Time to patient access to oncology medications varies by disease condition, which may reflect disease-related factors that impact assessment. The time to a decision for a hematology medicine was approximately two months shorter than the time to a decision for a solid-state medicine. It is possible that unmet need, budget impact, or competitive features within hematology conditions are driving the speed of assessments in these medicines. Further research is needed to determine why there are differences in time to decisions by oncology disease conditions.
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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 | 0.014 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".