An Enhanced Approach for Economic Evaluation of Long-Term Benefits of School-Based Health Promotion Programs
Bibliographic record
Abstract
Chronic diseases constitute a tremendous public health burden globally. Poor nutrition, inactive lifestyles, and obesity are established independent risk factors for chronic diseases. Public health decision-makers are in desperate need of effective and cost-effective programs that prevent chronic diseases. To date, most economic evaluations consider the effect of these programs on body weight, without considering their effects on other risk factors (nutrition and physical activity). We propose an economic evaluation approach that considers program effects on multiple risk factors rather than on a single risk factor. For demonstration, we developed an enhanced model that incorporates health promotion program effects on four risk factors (weight status, physical activity, and fruit and vegetable consumption). Relative to this enhanced model, a model that considered only the effect on weight status produced incremental cost-effectiveness ratio (ICER) estimates for quality-adjusted life years that were 1% to 43% higher, and ICER estimates for years with chronic disease prevented that were 1% to 26% higher. The corresponding estimates for return on investment were 1% to 20% lower. To avoid an underestimation of the economic benefits of chronic disease prevention programs, we recommend economic evaluations consider program effects on multiple risk factors.
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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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".