Cost analysis of medical assistance in dying in Canada
Bibliographic record
Abstract
BACKGROUND: The legalization of medical assistance in dying will affect health care spending in Canada. Our aim was to determine the potential costs and savings associated with the implementation of medical assistance in dying. METHODS: Using published data from the Netherlands and Belgium, where medically assisted death is legal, we estimated that medical assistance in dying will account for 1%-4% of all deaths; 80% of patients will have cancer; 50% of patients will be aged 60-80 years; 55% will be men; 60% of patients will have their lives shortened by 1 month; and 40% of patients will have their lives shortened by 1 week. We combined current mortality data for the Canadian population with recent end-of-life cost data to calculate a predicted range of savings associated with the implementation of medical assistance in dying. We also estimated the direct costs associated with offering medically assisted death, including physician consultations and drug costs. RESULTS: Medical assistance in dying could reduce annual health care spending across Canada by between $34.7 million and $138.8 million, exceeding the $1.5-$14.8 million in direct costs associated with its implementation. In sensitivity analyses, we noted that even if the potential savings are overestimated and costs underestimated, the implementation of mdedical assistance in dying will likely remain at least cost neutral. INTERPRETATION: Providing medical assistance in dying in Canada should not result in any excess financial burden to the health care system, and could result in substantial savings. Additional data on patients who choose medical assistance in dying in Canada should be collected to enable more precise estimates of the impact of medically assisted death on health care spending and to enable further economic evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".