National economic indices and reimbursement systems determine transcatheter aortic valve implantation use in Western Europe
Bibliographic record
Abstract
Purpose: Regional differences in the adoption of transcatheter aortic valve implantation (TAVI) technology have emerged since European CE mark approval in 2007. We sought to identify factors that may influence TAVI adoption and inequitable patient access in Western Europe. Methods: TAVI use was determined across 11 European nations: Germany, France, Italy, United Kingdom (UK), Spain, The Netherlands, Switzerland, Belgium, Portugal, Denmark and Ireland. The following national economic indices and healthcare parameters were assessed in order to establish factors associated with TAVI use: (1) the proportion of gross domestic product (GDP) spent on healthcare; (3) the total healthcare expenditure (US dollars) per capita; (4) the principal source of healthcare funding (social insurance or taxation); and (5) the system of TAVI reimbursement (TAVI-specific or constrained). Results: Between 2007-2011, a cumulative total of 34504 patients underwent TAVI in the 11 study nations. Significant linear correlations were found between TAVI utilisation and healthcare spending as a percentage of GDP (r=0.68, p=0.025), and healthcare spending per capita (r=0.80, p=0.005). There was a trend towards increased TAVI use in those nations where healthcare was funded principally by social insurance (Germany, France, the Netherlands, Switzerland, and Belgium) compared to those principally funded by taxation (Italy, UK, Spain, Portugal, Denmark, Ireland) (571±290 versus 252±192 implants per million ≥75 years, p=0.056). TAVI reimbursement strategies across the study nations were heterogeneous. TAVI-specific national DRG-based reimbursement occurs in Germany, France, Switzerland, and Denmark. Constrained reimbursement systems were noted for the UK, Spain, the Netherlands, Belgium, Portugal and Ireland where the cost of TAVI is borne by a local healthcare trust (UK) or by the hospital budget. TAVI-specific reimbursement systems were associated with a 3.3-fold higher TAVI utilisation than constrained systems (698±232 versus 213±112, p=0.002). Furthermore, TAVI-specific reimbursement systems were associated with 2.5 times more TAVI implants per centre than constrained systems (69±18 vs. 26±20 implants per centre p=0.008). Conclusions: National economic indices and reimbursement strategies are closely linked with TAVI use and may explain the inequitable adoption of TAVI across nations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".