Cost-Effectiveness of Organ Donation: Evaluating Investment into Donor Action and Other Donor Initiatives
Bibliographic record
Abstract
Initiatives aimed at increasing organ donation can be considered health care interventions, and will compete with other health care interventions for limited resources. We have developed a model capable of calculating the cost-utility of organ donor initiatives and applied it to Donor Action, a successful international program designed to optimize donor practices. The perspective of the payer in the Canadian health care system was chosen. A Markov model was developed to estimate the net present value incremental lifetime direct medical costs and quality adjusted life years (QALYs) as a consequence of increased kidney transplantation rates. Cost-saving and cost-effectiveness thresholds were calculated. The effects of changing the success rate and time frame of the intervention was examined as a sensitivity analysis. Transplantation results in a gain of 1.99 QALYs and a cost savings of Can$104,000 over the 20-year time frame compared with waiting on dialysis. Implementation of an intervention such as Donor Action, which produced as few as three extra donors per million population, would be cost-effective at a cost of Can$1.0 million per million population. The cost-effectiveness of Donor Action and other organ donor initiatives compare favorably to other health care interventions. Organ donation may be underfunded in North America.
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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 | 0.017 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".