Evidence From Ghana Indicates That Childhood Cancer Treatment in Sub-Saharan Africa Is Very Cost Effective: A Report From the Childhood Cancer 2030 Network
Bibliographic record
Abstract
PURPOSE: No published study to date has examined total cost and cost-effectiveness of maintaining a pediatric oncology treatment center in an African setting, thus limiting childhood cancer advocacy and policy efforts. METHODS: Within the Korle Bu Teaching Hospital in Accra, Ghana, costing data were gathered for all inputs related to operating a pediatric cancer unit. Cost and volume data for relevant clinical services (eg, laboratory, pathology, medications) were obtained retrospectively or prospectively. Salaries were determined and multiplied by proportion of time dedicated toward pediatric patients with cancer. Costs associated with inpatient bed use, outpatient clinic use, administrative fees, and overhead were estimated. Costs were summed for a total annual operating cost. Cost-effectiveness was calculated based on annual patients with newly diagnosed disease, survival rates, and life expectancy. RESULTS: The Korle Bu Teaching Hospital pediatric cancer unit treats on average 170 new diagnoses annually. Total operating cost was $1.7 million/y. Personnel salaries and operating room costs were the most expensive inputs, contributing 45% and 21% of total costs. Together, medications, imaging, radiation, and pathology services accounted for 7%. The cost per disability-adjusted life-year averted was $1,034, less than the Ghanaian per capita income, and thus considered very cost effective as per WHO-CHOICE methodology. CONCLUSION: To our knowledge, this study is the first to examine institution-level costs and cost-effectiveness of a childhood cancer program in an African setting, demonstrating that operating such a program in this setting is very cost effective. These results will inform national childhood cancer strategies in Africa and other low- and middle-income country settings.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".