The out-of-pocket cost of breast cancer care at a public tertiary care hospital in Nigeria: an exploratory analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it