The cost‐effectiveness of treating childhood cancer in 4 centers across sub‐Saharan Africa
Bibliographic record
Abstract
BACKGROUND: The treatment of childhood cancer often is assumed to be costly in African settings, thereby limiting advocacy and policy efforts. The authors determined the cost and cost-effectiveness of maintaining childhood cancer centers across 4 hospitals throughout sub-Saharan Africa. METHODS: Within hospitals representing 4 countries (Kenya, Nigeria, Tanzania, and Zimbabwe), cost was determined either retrospectively or prospectively for all inputs related to operating a pediatric cancer unit (eg, laboratory costs, medications, and salaries). Cost-effectiveness was calculated based on the annual number of newly diagnosed patients, survival rates, and life expectancy. RESULTS: Cost per new diagnosis ranged from $2400 to $31,000, attributable to variances with regard to center size, case mix, drug prices, admission practices, and the treatment abandonment rate, which also affected survival. The most expensive cost input was found to be associated with medication in Kenya, and medical personnel in the other 3 centers. The cost per disability-adjusted life-year averted ranged from 0.3 to 3.6 times the per capita gross national income. Childhood cancer treatment therefore was considered to be very cost-effective by World Health Organization standards in 2 countries and cost-effective in 1 additional country. In all centers, abandonment of treatment was common; modeling exercises suggested that public funding of treatment, additional psychosocial personnel, and modifications of inpatient policies would increase survival rates while maintaining or even improving cost-effectiveness. CONCLUSIONS: Across various African countries, childhood cancer treatment units represent cost-effective interventions. Cost-effectiveness can be increased through the control of drug prices, appropriate policy environments, and decreasing the rate of treatment abandonment. These results will inform national childhood cancer strategies across Africa.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".