The cost and cost-effectiveness of a pediatric cancer unit (PCU) in the context of universal health coverage (UHC): A report from the Childhood Cancer 2030 Network.
Bibliographic record
Abstract
e18891 Background: No costing data for comprehensive childhood cancer service delivery in an upper-middle income country (UMIC) are available. Further, there are no PCU costing data from any low or middle-income country (LMIC) with a UHC system. Building on prior work in other non-UHC LMIC, we tabulated the cost and cost-effectiveness of operating a PCU in Mexico, an UMIC with UHC. Methods: Administrative data on costs and volumes of inputs were determined for the PCU at the Hospital Civil de Guadalajara (HCG) by retrospectively determining the services used (e.g. imaging, pathology, medications) and their unit costs. Salaries of medical and non-medical personnel were multiplied by the percentage of time devoted to the care of children with cancer. Costs associated with inpatient bed use, central administration, and utilities were calculated using hospital occupancy rates and WHO-CHOICE estimates. Cost-effectiveness was estimated based on number of annual new patients diagnosed at HCG and 5-year survival. Results: The cost of pediatric cancer services at HCG, involving 165 new diagnoses, was $8.6 million in 2016. Cost by category is provided in the Table. Based on a 5-year survival of 73%, the cost per Disability Adjusted Life Year averted among patients treated at HCG was $3034 (3% discounting; accounted for early mortality and survivorship-associated late-effects), well below the WHO-CHOICE threshold of “very cost-effective” (i.e. less than the Mexican GDP/capita of $8201). The state of Jalisco (74%), federal Seguro Popular (23%) and private charities (3%) financed all non-capital expenditures. Conclusions: This is the first comprehensive cost analysis of a UMIC PCU. We found that childhood cancer services delivered by such a PCU are very cost-effective. These data and methodologies can guide policymakers and stakeholders when planning resource allocation for pediatric cancer services in Mexico and other LMIC with UHC. Category USD $ '000 % Personnel 1715 19.9 Hoteling 2232 25.8 Outpatient 517 6.0 Pathology 306 3.5 Pharmacy 1391 16.1 Radiation 74 0.9 Imaging 234 2.7 Surgery 124 1.4 Blood Bank 579 6.7 Utilities 576 6.7 Central Administration 891 10.3 Total 8638
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".