Financial Toxicity: Unveiling the Burden of Cancer Care on Patients in Rwanda
Bibliographic record
Abstract
INTRODUCTION: Cancer is a major public health problem in Rwanda and other low- and middle-income countries (LMICs). While there have been some improvements in access to cancer treatment, the cost of care has increased, leading to financial toxicity and treatment barriers for many patients. This study explores the financial toxicity of cancer care in Rwanda. METHODS: This prospective cross-sectional study was conducted at 3 referral hospitals in Rwanda, which deliver most of the country's cancer care. Data were collected over 6 months from June 1 to December 1, 2022 by trained research assistants (RAs) using a modified validated data collection tool. RAs interviewed consecutive eligible patients with breast cancer, cervical cancer, colorectal cancer, Hodgkin's and non-Hodgkin's lymphoma who were on active systemic therapy. The study aimed to identify sources of financial burden. Data were analyzed using descriptive statistics. RESULTS: 239 patients were included; 75% (n = 180/239) were female and mean age was 51 years. Breast, cervix, and colorectal cancers were the most common diagnoses (42%, 100/239; 24%, 58/239; and 24%, 57/239, respectively) and 54% (n = 129/239) were diagnosed with advanced stage (stages III-IV). Financial burden was high; 44% (n = 106/239) of respondents sold property, 29% (n = 70/239) asked for charity from public, family, or friends, and 16% (n = 37/239) took loans with interest to fund cancer treatment. CONCLUSION: Despite health insurance which covers many elements of cancer care, a substantial proportion of patients on anti-cancer treatment in Rwanda experience major financial toxicity. Novel health financing solutions are needed to ensure accessible and affordable cancer care.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".