Policies and cost analyses of voluntary assisted dying (VAD) laws – a mapping review & analysis
Bibliographic record
Abstract
To investigate the current literature on healthcare policies and cost analyses around international Voluntary Assisted Dying (VAD) laws. The study design is a mapping literature review following Preferred-Reporting-Items-for-Systematic-Reviews-and-Meta-Analyses (PRISMA) guidelines. Original research articles published between January 1990 to March 2023, investigating the financial cost and healthcare budget effect of VAD laws internationally. Citations were screened for relevance and eligibility, and any non-full-text research that did not explore cost analysis was excluded. The following data sources were screened: MEDLINE, PubMed, EMBASE, CINAHL and any relevant international health authority annual reports were also reviewed. Of the 2790 screened articles, eight studies met the inclusion criteria and three were included in the mapping review. The reviewed studies included prospective studies, two Canadian and one US. Only one of the Canadian studies provided a cost analysis using data from current VAD laws. All three studies showed VAD laws would reduce healthcare spending, with the US approximating $627million in 1995. Canada approximating $17.1 to $77.1million in 2017 and $86.9 to $149.0million in 2021, overall, leading to an average percentage reduction in costs of approximately 87% compared to original costs of end-of-life care. This review identifies a scarcity in cost-analysis literature and provides a summary of the latest international VAD laws, from which a potential cost reduction is apparent. The absence of retrospectively collated financial VAD data highlights a need for future research to inform policymakers of the economic factors affecting current policies with a need for annual fiscal reports and to optimise future legislative frameworks internationally. - This study highlights the absence of cost analysis reports on the provision of VAD globally, at a time when VAD is becoming an evolving part of end-of-life care particularly in developed countries base on the notion of patient autonomy. - The impact of this study is its ability to inform future research and policymakers of the economic factors affecting current VAD policies with a need for annual fiscal reports and to optimise future legislative frameworks internationally.
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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 | 0.033 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".