Public acceptance of different approaches to determine drug reimbursement prices and whether it is influenced by framing
Bibliographic record
Abstract
Purpose – In Germany, the Institute for Quality and Efficiency in Health Care (Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen (IQWiG)) makes recommendations for reimbursement prices of drugs based on an evaluation of the relationship between incremental costs and effects. In 2009, the German government (“Koalitionsvertrag”) announced its intention to evaluate the transparency and acceptability of IQWiG's work. The purpose of this paper is to conduct a survey of a sample of the German population: to check the acceptance of the IQWiG's decision rule compared to a decision rule used in countries such as England and Wales; and to test if the way the IQWiG's decision rule is presented affects its acceptance. Design/methodology/approach – A questionnaire was developed eliciting acceptability of IQWiG's decision rule and a single cost-effectiveness threshold applying to all diseases (i.e. absolute rule (AR)). To check for a framing effect IQWiG's decision rule was presented in two ways. One version presented it, based on IQWiG's official publication, as a constant ratio between incremental costs and effects within a therapeutic area (proportional rule (PR), Version 1). The other version presented it in terms of a proportional relationship between costs and effects within a therapeutic area (PR, Version 2).χ2goodness of fit test and two-proportionz-test tested for differences between acceptance rates of decision rules. Findings – A sample of 108 persons from the general public (average age: 41 years, 48 per cent male) filled out the questionnaire. Acceptance rates for AR, PR Version 1, and PR Version 2 were 33, 48, and 39 per cent, respectively. Direct comparisons show that acceptance of PR Version 1 was significantly higher than acceptance of AR (p=0.026) and that there was a clear trend towards higher acceptance of PR Version 1 compared to Version 2 (p=0.22). The difference between PR Version 2 and AR was not significant (p=0.32). Research limitations/implications – In our study, different presentations of the IQWiG decision rule did not significantly alter its acceptance, although there was a clear trend towards accepting IQWiG's official version. Furthermore, the acceptance of the IQWiG's official version was significantly higher than AR. Because of the relevance of the research question we recommend additional research for confirmation and explanation of the choices made. Originality/value – This is one of the few studies on the impact of framing on allocation preferences in health care.
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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 | 0.150 | 0.319 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".