Competitive tenders on analogue hospital pharmaceuticals in Denmark 2017–2020
Bibliographic record
Abstract
BACKGROUND: Competitive tenders on pharmaceuticals are one of the most effective cost-containment instruments in healthcare systems. Its effectiveness has been demonstrated, among other things, in markets for generic medicine and biosimilars. In Denmark, an internationally unique model for competitive tenders on analogue substitutable pharmaceuticals has been developed and implemented for all public hospitals. METHODS: We obtained data on all analogue competitive tenders carried out by the Danish Medicines Council from its foundation on January 1, 2017, to October 9, 2020. We calculated univariate descriptive statistics, pairwise correlations and made a multiple regression analysis on tender savings. RESULTS: Average annual saving on hospital pharmaceutical purchase prices was 44.1% ranging from 0.4% to 92.8% between therapeutic areas and areas of indication. There was a significant positive correlation between tender savings and the number of competitors participating in the tender, and a significant negative correlation between tender savings and the number of days since market authorization. CONCLUSIONS: This study finds analogue tenders to be similar in effect and mechanism to competitive tenders in markets for generic medicine and biosimilars. It supports the increasing number of empirical findings that competitive tendering has a high potential to generate substantial savings on healthcare budgets.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".