Simulation Of Quitting Smoking In The Military Shows Higher Lifetime Medical Spending More Than Offset By Productivity Gains
Bibliographic record
Abstract
Despite the documented benefits of quitting smoking, studies have found that smokers who quit may have higher lifetime medical costs, in part because of increased risk for medical conditions, such as type 2 diabetes, brought on by associated weight gain. Using a simulation model and data on 612,332 adult smokers in the US Department of Defense's TRICARE Prime health plan in 2008, we estimated that cessation accompanied by weight gain would increase average life expectancy by 3.7 years, and that the average lifetime reduction in medical expenditures from improved health ($5,600) would be offset by additional expenditures resulting from prolonged life ($7,300). Results varied by age and sex: For females ages 18-44 at time of cessation, there would be net savings of $1,200 despite additional medical expenditures from prolonged life. Avoidance of weight gain after quitting smoking would increase average life expectancy by four additional months and reduce mean extra spending resulting from prolonged life by $700. Overall, the average net lifetime health care cost increase of $1,700 or less per ex-smoker would be modest and, for employed people, more than offset by even one year's worth of productivity gains. These results boost the case for smoking cessation programs in the military in particular, along with not selling cigarettes in commissaries or at reduced prices.
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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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".