Economic Value of Initial Implementation Activities for Proposed Ban on Sales of Over-The-Counter Diet Pills and Muscle-Building Supplements to Minors
Bibliographic record
Abstract
•Start-up costs for states implementing prevention policies are typically excluded from cost-effectiveness analyses.•Age-restriction bans on over-the-counter diet pills and muscle-building supplements are being considered.•Such bans prevent eating disorders, illicit anabolic steroid use, and other adverse health outcomes.•Start-up administrative costs of this ban are less than the starting salary of a secretary. IntroductionOver-the-counter diet pills, weight-loss supplements, and muscle-building supplements often contain harmful ingredients and are associated with eating disorder diagnoses and other negative health outcomes. This study estimated the value of state initial implementation activities, for example, regulation development, to implement a ban on the sale of dangerous over-the-counter diet pills and muscle-building supplements to minors.MethodsWe enumerated minimum, best, and maximum values for 22 inputs among 11 activities state employees may undertake if the legislation were signed into law. For employment costs, we estimated staff hours on the basis of data from 10 key informants and obtained salary ranges from a state government website. Data were collected and analyzed between September 2021 and January 2022. We calculated 95% CIs using 10,000 Monte Carlo simulations that varied inputs simultaneously and probabilistically. We conducted two sensitivity analyses using all minimum and all maximum salaries.ResultsThe estimated value of state start-up activities was $47,536 (95% CI=$36,831–$57,381). Inputs with the largest impact on this estimate corresponded to combinations of the highest salary and greatest hours per task.ConclusionsThe state's one-time opportunity cost to initiate this age-restriction policy would be minimal considering potential health gains. Sensitivity analyses did not change the conclusion, especially if the state produces subregulations linked to existing law rather than new regulations.
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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 | 0.000 | 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".