Cost analysis of Medical Assistance in Dying legalisation in Greece: Preliminary Findings and Limitations
Bibliographic record
Abstract
Introduction: Medical Assistance in Dying (MAiD) is a complex and sensitive topic that raises ethical, political, legal, religious, and economic concerns. Despite this, an increasing number of countries, offer assisted dying options. The economic implications of legalising MAiD, including the costs and savings, have become a central issue in the discussions of legalising MAiD. Objective and Aim: The objective of this study was to analyze the expenses and potential monetary savings associated with the potential legalisation in Greece. This analysis aims to provide valuable insights for policymakers to make informed decisions. Materials & Methods: We estimated MAiD savings using Emanuel and Battin's model and real-world data from Canada, the Netherlands, and Belgium. MAiD is projected to cause 1%-4% of deaths, with 65% being cancer patients and 35% being non-cancer patients. Direct costs were estimated using Canada's procedure, EOPYY reimbursements, and expert opinion. End-of-life costs were calculated using limited Greek evidence and logical assumptions, with multiple sensitivity analyses due to data uncertainty. Results: MAiD accounts for 4% of deaths in the high-level scenario, with 5613 end-of-life patients choosing it, 3646 of whom have cancer. In the 1% scenario, 912 patients with cancer choose MAiD, totaling 1403 patients. MAiD could reduce healthcare spending by €6.560.230 million in the high scenario, with costs at the highest sensitivity level. In the low scenario, potential savings are €806.598. The estimated gross savings average at €4.592.279 million, surpassing the average direct costs of implementation at €908.865. Interpretation: Legalizing MAiD in Greece would not result in any additional financial burden but would only have a modest net financial benefit of about 0.5% of overall healthcare spending. More complete data on end-of-life costs is necessary for a thorough economic evaluation, given the current limitations. Therefore, it's crucial to prioritize examining ethical considerations while carefully weighing financial implications before legalising MAiD.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".