Opportunity Cost of Funding Drugs for Rare Diseases
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Both ethical and economics concerns have been raised with respect to the funding of drugs for rare diseases. This article reports both the cost-effectiveness of eculizumab for the treatment of paroxysmal nocturnal hemoglobinuria (PNH) and its associated opportunity costs. METHODS: Analysis compared eculizumab plus current standard of care v. current standard of care from a publicly funded health care system perspective. A Markov model covered the major consequences of PNH and treatment. Cost-effectiveness was assessed in terms of the incremental cost per life year and per quality-adjusted life year (QALY) gained. Opportunity costs were assessed by the health gains foregone and the alternative uses for the additional resources. RESULTS: Eculizumab is associated with greater life years (1.13), QALYs (2.45), and costs (CAN$5.24 million). The incremental cost per life year and per QALY gained is CAN$4.62 million and CAN$2.13 million, respectively. Based on established thresholds, the opportunity cost of funding eculizumab is 102.3 discounted QALYs per patient funded. Sensitivity and subgroup analysis confirmed the robustness of the results. If the acquisition cost of eculizumab was reduced by 98.5%, it could be considered cost-effective. LIMITATIONS: The nature of rare diseases means that data are often sparse for the conduct of economic evaluations. When data were limited, assumptions were made that biased results in favor of eculizumab. CONCLUSIONS: This study demonstrates the feasibility of conducting economic evaluations in the context of rare diseases. Eculizumab may provide substantive benefits to patients with PNH in terms of life expectancy and quality of life but at a high incremental cost and a substantial opportunity cost. Decision makers should fully consider the opportunity costs before making positive reimbursement decisions.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it