Companies’ Health Technology Assessment Strategies and Practices in Australia, Canada, England, France, Germany, Italy and Spain: An Industry Metrics Study
Bibliographic record
Abstract
Background:Health technology assessment (HTA) has increased in importance in supporting payer decision making by assessing the relative effectiveness and cost effectiveness of new medicines. Thus, pharmaceutical companies need to address the HTA requirements early during development to improve reimbursement outcomes. Currently, there is a lack of research to assess the impact of HTA on development and jurisdictional outcome from companies’ perspectives. This study aimed to assess companies’ HTA strategy and characterise HTA practice in seven jurisdictions. Methods:A multi-year, annual study collected information for individual products, focusing on development activities regarding inclusion of HTA requirements and selection of global comparators. The generation of local contextual information, submission strategies and predictability of HTA outcomes was examined jurisdictionally in Australia, Canada, England, France, Germany, Italy and Spain. The study questionnaire was built into a secure online data collection platform and data were provided annually by participating companies. Results:Data for 169 compounds were provided by nine international companies between 2014 and 2018. HTA requirements were implemented in evidence generation plan for 63% of products during development. Global comparators were accepted by HTA bodies for more than half of studied products; Spain showed the highest acceptance rate (85%). Companies took advantages of parallel process in Australia and Canada to shorten product rollout time. Australia demonstrated general consistency in HTA review time, and England had the longest variation (interquartile range, 216 days). Requirements for additional information after submission occurred at all HTA bodies. Germany and Italy showed the highest percentage of products being reimbursed as per regulatory label (80 and 68%, respectively). Canada was the most predictable jurisdiction, with the highest proportion of review outcome (90%) that met companies’ expectations. Conclusion:Companies are addressing HTA requirements during development for many products; however, they are challenged by varying requirements and practices and product success ultimately depends on how HTA organisations and payers assess added value in the context of the national healthcare systems. This ongoing study created a baseline to help capture fact-based changes for company HTA strategies and HTA body practices.
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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 | 0.039 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".