Bridging gaps to universal palliative care access in Chile: serious health-related suffering and the cost of expanding the package of care services
Bibliographic record
Abstract
Background: The Lancet Commission on Palliative Care (PC) and Pain Relief quantified the burden of serious health-related suffering (SHS), proposing an Essential Package of PC (EPPC) to narrow the global PC divide. We applied the EPPC framework to analyze PC access in Chile, identify gaps in coverage, and provide recommendations to improve PC access. Methods: Total SHS and population in need of PC was estimated using official 2019 government data. We differentiated between cancer and non-cancer related SHS given guaranteed Chilean PC coverage for cancer. We calculated differences between the Chilean PC package and the Lancet Commission EPPC to estimate the cost of expanding to achieve national coverage of palliative care. Findings: In 2019, nearly 105,000 decedent and non-decedent Chileans experienced SHS with a lower-bound estimate of 12.1 million days and an upper-bound estimate of 42.4 million days of SHS. Each individual experienced between 116 and 520 days of SHS per year. People living with a cancer diagnosis had PC access with financial protection, accounting for almost 42% of patients in need. People with non-cancer diagnoses-about 61 thousand patients-lacked PC coverage. Expanding coverage of the EPPC for all patients in need would cost just above $123 million USD, equivalent to 0.47% of Chilean National Health Expenditure. Interpretation: Achieving universal PC access is urgent and feasible for Chile, classified as a high-income country. Expanding PC services and coverage to the EPPC standard are affordable and critical health system responses to ensuring financial protection for patients with SHS. In Chile, this requires closing large gaps in PC coverage pertaining to patients with non-cancer conditions and treatment of symptoms that go beyond pain. Our research provides an empirical approach for applying the Lancet Commission SHS framework to estimate the cost of achieving national universal PC access anchored in a package of health care services. Funding: This research was partially funded by the Chilean Government through the Fondo Nacional de Ciencia y Tecnología (Fondecyt Regular) grant number 1201721, the U.S. Cancer Pain Relief Committee grant AWD-003806 awarded to the University of Miami and by the University of Miami Institute for Advanced Study of the Americas. We acknowledge NIH/NCI award P30CA008748.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".