OP136 A Comparison Of Health Technology Assessment Recommendations In Australia, Canada, And England: Is There Opportunity For Further Alignment?
Bibliographic record
Abstract
Introduction Over the past decade there have been increasing interactions between health technology assessment (HTA) agencies through international networks at the policy level and European joint actions at the product level. A pilot project is underway to explore collaboration beyond Europe between HTA agencies in Australia, Canada, and the UK. This study the compared HTA recommendations of new active substances (NAS) appraised by Australia’s Pharmaceutical Benefits Advisory Committee (PBAC), Canada’s CADTH, and England’s National Institute for Health and Care Excellence (NICE). Methods Using publicly available data and established benchmarking methodology, we examined 45 NAS appraised by PBAC, CADTH, and NICE between 2017 and 2021. Analysis was performed to assess rollout time from regulatory to HTA recommendation, and to the first HTA recommendation. Results Most products were submitted to the Europe Medicine Agency first (89%). However, 71 percent of NAS in Australia and 69 percent in Canada were submitted to HTA in parallel with regulatory review, which shortened overall rollout time. The median HTA submission gap among the three agencies was 140 days in Australia, 102 days in Canada, and 8 days in England. PBAC had the highest number of negative recommendations (51%), followed by CADTH (18%), and NICE (5%). The congruence of HTA decisions was highest between CADTH and NICE (56%), compared with PBAC and CADTH (11%), and PBAC and NICE (20%) Conclusions To achieve a more collaborative HTA process it is necessary to understand the rollout time in these jurisdictions. This study identified submission gaps among the regulatory agencies, but there may be more synergy in the future as regulators in the three jurisdictions work collaboratively through the Access Consortium. Currently, the submission gap for the three HTA agencies was mostly within six months, making collaboration on joint assessment possible. We observed divergences in HTA recommendations due to the methodology and decision criteria applied by each agency. Therefore, collaboration on assessment should build on the clinical aspects, although HTA decisions should be grounded in the local context.
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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 | 0.057 | 0.224 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".