Economic Evaluation of Palliative Care Interventions: A Review of the Evolution of Methods From 2011 to 2019
Bibliographic record
Abstract
BACKGROUND: End-of-life care is a driver of increasing healthcare costs; however, palliative care interventions may significantly reduce these costs. Economic evaluations that measure the incremental cost per quality adjusted life years (QALY) are warranted to inform cost-effectiveness of the intervention relative to a comparator and permit evaluation of investment against other therapeutic interventions. Evidence from the literature up to 2011 indicates a scarcity of cost-utility studies in palliative care research. AIM: This literature review evaluates economic studies published between 2011 and 2019 to determine whether the methods of economic evaluations have evolved since 2011. DESIGN AND DATA SOURCES: A literature search was completed using CENTRAL, OVID MEDLINE, EMBASE and other sources for publications between 2011 and 2019. Study characteristics, methodology and key findings of publications that met the inclusion criteria were reviewed. Quality of studies were assessed using indicators developed by authors of the previous literature review. RESULTS: 46 papers were included for qualitative synthesis. Among them only 6 studies conducted formal cost-effectiveness evaluations-of these 5 measured QALYs and 1 employed probabilistic analyses. In addition, with the exception of 1 costing analysis, all other economic evaluations undertook a healthcare payer perspective. Quality of evidence were comparable to the previous literature review published in 2011. CONCLUSION: Despite the small increase in the number of cost-utility studies, the methods of palliative care economic evaluations have not evolved significantly since 2011. More probabilistic cost-utility analyses of palliative care interventions from a societal perspective are necessary to truly evaluate the value for money.
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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 | 0.066 | 0.240 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".