Palliative Care in Low and Middle-Income Countries to Reduce Cancer Suffering: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: In low- and middle-income countries, the incidence of cancer is rising, with diagnoses frequently made at advanced stages, limited access to treatments, and high mortality rates. Palliative care may alleviate suffering, enhance quality of life, and reduce costs. In this review, we aimed to explore the development of palliative care in low- and middle-income settings and assess if and how this contributes to mitigating or alleviating the human suffering of people with cancer. METHODS: A best-fit framework synthesis approach was employed, and seven databases were systematically searched in 2025. Data from eligible studies were extracted and qualitatively synthesized into themes and subthemes, initially using a framework constructed from indicators from the WHO Conceptual Model for Palliative Care Development and the Integrated Palliative Care Oncology Practice Model. The review is registered with PROSPERO (CRD42024511158) FINDINGS: Studies (n = 81) were reviewed. Interventions by multidisciplinary teams, combined with a bio-psycho-socio-spiritual approach, led to improved patient outcomes. Palliative care reduced hospital stays, decreased unnecessary end-of-life treatments, and facilitated home deaths. Community-based palliative care empowered patients and families to manage care at home, cope with uncertainties, and lower healthcare costs. Successful implementation relied on a participatory public health system and support from nongovernmental organizations, which provided access to palliative care, telehealth, and training for primary care providers. However, when compared to the indicators from the two models, only a few aligned with core elements. INTERPRETATION: Although progress has been made in integrating palliative care in low- and middle-income countries, evidence linking this development to international indicators of palliative care progress is insufficient. It is likely that strengthening patient-family partnerships, promoting shared decision-making, and advocating for patient rights contribute to alleviating the human suffering from cancer. FUNDING: The conduct of this review is supported by an unrestricted grant from the End-of-Life Care in India Task Force (ELICIT). ELICIT supports end-of-life education, research, and policy.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".