The out-of-pocket cost of breast cancer care at a public tertiary care hospital in Nigeria: an exploratory analysis
Bibliographic record
Abstract
Introduction: in Nigeria, the incidence of breast cancer has increased by over 80% in the last four decades. This study quantifies the out-of-pocket (OOP) cost of breast cancer management and the associated rate of catastrophic healthcare expenditure (CHE) at a public tertiary care facility in Ile-Ife, Nigeria. Methods: patients treated between December 2017 - August 2018 were identified from a prospective breast cancer database. A questionnaire was developed to capture the total cost of care, including direct and indirect expenses. Three commonly used thresholds for a CHE were used in this analysis. The cost of radiotherapy and targeted therapy were captured separately. Results: data was collected from 22 eligible patients. Sixty-eight percent had no form of health insurance. The mean cost of diagnosis and treatment was $2,049 (SD $1,854). At a threshold of 10% and 25% of annual income, 95% and 86% of households experienced a CHE. Based on a household´s capacity-to-pay, 90% experienced a CHE. The mean cost of radiotherapy was $462 (SD $223) and the mean cost of trastuzumab was $6,568 (SD $2,766). Cost precluded surgery in 14% of patients with resectable disease. As a result of accessing treatment, 72% of households had to borrow money and 9% of households interrupted a child´s education. Conclusion: the out-of-pocket cost of breast cancer care in Nigeria is significant. This results in a CHE for 68-95% of households, which has significant health and economic sequelae. Greater financial protection is essential as the burden of breast cancer increases in Nigeria.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".