The cost and cost‐effectiveness of childhood cancer treatment in El Salvador, Central America: A report from the Childhood Cancer 2030 Network
Bibliographic record
Abstract
BACKGROUND: Although previous studies have examined the cost of treating individual childhood cancers in low-income and middle-income countries, to the authors' knowledge none has examined the overall cost and cost-effectiveness of operating a childhood cancer treatment center. Herein, the authors examined the cost and sources of financing of a pediatric cancer unit in Hospital Nacional de Ninos Benjamin Bloom in El Salvador, and make estimates of cost-effectiveness. METHODS: Administrative data regarding costs and volumes of inputs were obtained for 2016 for the pediatric cancer unit. Similar cost and volume data were obtained for shared medical services provided centrally (eg, blood bank). Costs of central nonmedical support services (eg, utilities) were obtained from hospital data and attributed by inpatient share. Administrative data also were used for sources of financing. Cost-effectiveness was estimated based on the number of new patients diagnosed annually and survival rates. RESULTS: The pediatric cancer unit cost $5.2 million to operate in 2016 (treating 90 outpatients per day and experiencing 1385 inpatient stays per year). Approximately three-quarters of the cost (74.7%) was attributed to 4 items: personnel (21.6%), pathological diagnosis (11.5%), pharmacy (chemotherapy, supportive care medications, and nutrition; 31.8%), and blood products (9.8%). Funding sources included government (52.5%), charitable foundations (44.2%), and a social security contribution scheme (3.4%). Based on 181 new patients per year and a 5-year survival rate of 48.5%, the cost per disability-adjusted life-year averted was $1624, which is under the threshold considered to be very cost effective. CONCLUSIONS: Treating childhood cancer in a specialized unit in low-income and middle-income countries can be done cost-effectively. Strong support from charitable foundations aids with affordability. Cancer 2018;124:391-7. © 2017 American Cancer Society.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".