Investigation of Factors Considered by Health Technology Assessment Agencies in Eight Countries
Bibliographic record
Abstract
BACKGROUND: Health technology assessment (HTA) organizations play a crucial role in optimizing healthcare resources, but the factors influencing decision making vary by country. OBJECTIVE: HTAs of cancer and hepatitis C drugs were evaluated across developed countries to understand differences in decision processes and criteria. METHODS: The HTA organizations evaluated are from France, Germany, Italy, Spain, the United Kingdom (UK), Australia, Canada and Japan. Economic evaluation types and 28 factors in the following categories were evaluated: clinical uncertainties/issues; disease/population/treatment consideration factors including National Institute for Health and Care Excellence's (NICE) special circumstances factors (e.g. end-of-life and innovation); and International Society for Pharmacoeconomics and Outcomes Research (ISPOR) additional value elements. Qualitative and correspondence analyses were conducted to assess the differences across organizations. RESULTS: Incremental cost-effectiveness ratio (ICER) using quality-adjusted life-year (QALY) was evaluated in Canada, the UK, Australia and Japan. The highest observed clinical uncertainties were clinical benefits and comparator. For cancer drugs, correspondence analysis showed France, Australia, Canada and the UK to have common attributes observed, such as unmet needs and stakeholder persuasion. In addition, the UK reported end-of-life, issues around current treatment and innovation, whereas Germany reported manageable/insignificant adverse events more frequently. Finally, fear of contagion, equity and scientific spillover value elements were only observed in Australia. CONCLUSION: Although clinical factors play a predominant role in the decision to reimburse medicine, HTA organizations consider additional aspects as well. If the methodology of HTA was clearly outlined, there would be more transparency in HTA systems leading to better understanding amongst stakeholders about decision making.
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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 | 0.068 | 0.183 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".