To ban or not to ban tanning bed use for minors: A cost‐effectiveness analysis from multiple US perspectives for invasive melanoma
Bibliographic record
Abstract
BACKGROUND: Tanning bed use is common among US adolescents, but is associated with increased melanoma risk. The decision to ban tanning bed use by adolescents should be made in consideration of the potential health benefits and costs. METHODS: The US population aged 14 to 17 years was modeled by microsimulation, which compared ban versus no ban strategies. Lifetime quality-adjusted life years (QALYs) and costs were estimated from a health care sector perspective and two societal perspectives: with and without the costs of policy enforcement and the economic losses of the indoor-tanning bed industry. RESULTS: Full adherence to the ban prevented 15,102 melanoma cases and 3299 recurrences among 17.1 million minors, saving $61in formal and informal health care costs per minor and providing an increase of 0.0002 QALYs. Despite the intervention costs of the ban and the economic losses to the indoor-tanning industry, banning was still the dominant strategy, with a savings of $12 per minor and $205.4 million among 17.1 million minors. Findings were robust against varying inspection costs and ban compliance, but were sensitive to lower excess risk of melanoma with early exposure to tanning beds. CONCLUSIONS: A ban on tanning beds for minors potentially lowers costs and increases cost effectiveness. Even after accounting for the costs of implementing a ban, it may be considered cost effective. Even after accounting for the costs of implementing a ban and economic losses in the indoor-tanning industry, a tanning bed ban for US minors may be considered cost effective. A ban has the potential to reduce the number of melanoma cases while decreasing health care costs. LAY SUMMARY: Previous meta-analyses have linked tanning bed use with an increased risk of melanoma, particularly with initial use at a young age. Yet, it remains unclear whether a ban of adolescents would be cost effective. Overall, a ban has the potential to reduce the number of melanoma cases while promoting a decrease in health care costs. Even after accounting for the costs of implementing a ban and the economic losses incurred by the indoor-tanning industry, a ban would be cost effective.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".