Health and Economic Benefits of Well-Designed Evaluations: Some Lessons From Evaluating Neuroblastoma Screening
Bibliographic record
Abstract
BACKGROUND: Well-designed evaluations of health services are frequently made today. However, the extent of the evaluations' benefits and costs is not well documented, creating uncertainty whether their use is optimal from society's perspective. We examined these costs and benefits using data from one well-designed evaluation, the Quebec Neuroblastoma Screening Project (QNSP). It screened most Quebec newborns between 1989 and 1994 for neuroblastoma. As previously reported, the screening did not reduce neuroblastoma mortality and caused adverse health effects. METHODS: We compared the cost of doing the QNSP with its benefits. Had the QNSP not been undertaken, neuroblastoma screening would have been implemented throughout North America. We assume that screening would have started in 1989 and ended in 2002. The QNSP's benefits include the health costs and adverse health effects averted by not using ineffective screening during those 14 years. In our calculations we used neuroblastoma incidence data for the QNSP and for Ontario where there was no screening, detailed data describing the health services used by the patients, and Quebec cost data for those services. RESULTS: The QNSP cost 8.77 million dollars (2002 US dollars). By not implementing similar screening programs between 1989 and 2002, the United States and Canada avoided 574.1 million dollars in health costs, the unnecessary treatment of 9223 children, and false-positive findings for 5003 children screened. CONCLUSIONS: The health care costs and adverse health effects averted by the QNSP justify its costs. These results show that well-designed evaluations can yield--at least sometimes--benefits substantially greater than their high costs. This raises an important policy issue: are these evaluations now being under- or over used?
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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 | 0.001 | 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".