International diffusion of new health technologies: A ten-country analysis of six health technologies
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to examine and explain the differential international diffusion of six health innovations. METHODS: A retrospective diffusion study was undertaken of sildenafil, cyclooxygenase-II (COX II) inhibitors, beta interferon, verteporfin, deep brain stimulators, and drug-eluting coronary stents in ten countries-Australia, Canada, Denmark, France, The Netherlands, Norway, Spain, Sweden, Switzerland, and the United Kingdom. We plotted diffusion curves of daily defined doses per quarter, vials or implants per million population, and examined the association between diffusion and five key variables. RESULTS: Canada, Switzerland, and Sweden are generally high users of new technologies; Spain, Denmark, and particularly the United Kingdom are low users. Almost all countries experienced rapid adoption of sildenafil with diffusion to a similar level; there was variable adoption and diffusion of COX II inhibitors, verteporfin, and interferon beta; drug-eluting stents penetrated the market in a similar way in all but one country; and two countries had very different adoption patterns for deep brain stimulators. Above average health spending and the presence of health technology assessment (HTA) or other guidance reports are consistently associated with increased diffusion. Early warning activity and a national coverage decision being taken are more likely to be associated with a reduced diffusion. CONCLUSIONS: The significant differences in diffusion between different countries are not consistent with a neat evidence-based world. The tools available to policy makers to control diffusion (early warning systems, HTA, and a fourth hurdle) play some part in influencing diffusion but need close scrutiny of how successfully they operate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".