Economic Benefits of Investment in Palliative Care: An Appraisal of Current Evidence and Call to Action
Bibliographic record
Abstract
BACKGROUND: World Health Assembly Resolution 67.19 affirms that palliative care is an ethical responsibility of health systems and urges member states to ensure domestic funding. Yet, even in countries with established services, palliative care is often fragmented and underfunded, with limited government support. AIM: The aim of this special article is thus to present a health economic appraisal of palliative care, building on established evidence of its clinical benefits. METHODOLOGY: Under the auspices of the World Health Organization, a multidisciplinary, international team of palliative care researchers, global health experts and health economists conducted a literature review addressing six themes: current health financing for advanced illness, models of palliative care, cost savings for health systems and households, improved patient and caregiver outcomes, and strategies for sustainable financing. RESULTS: Results show that palliative care can prevent catastrophic health expenditures for families, increase efficiency in use of health resources, and support integration within national health systems. Palliative care also facilitates monitoring of service costs and enables the development of effective financing frameworks. CONCLUSION: This review offers practical guidance for policymakers, funders, and health system leaders to integrate palliative care into universal health coverage schemes. It underscores the dual imperative-ethical and economic-of investing in palliative care to promote equity and sustainability in health care delivery.
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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 | 0.041 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".