Health Economic Modeling for Policy Simulation
Bibliographic record
Abstract
Abstract Economic models are powerful tools for simulating the long-term return on investment of policy interventions, making them ideal for examining both health outcomes and costs over extended periods. In this workshop, we will present a case study of policy simulation using the example of a menthol cigarette ban. Menthol cigarette use is associated with higher nicotine dependence and lower quit rates, particularly among females and ethnic minority groups. Several countries, including Canada, have already implemented bans on menthol cigarettes. Using a simulation model, we will demonstrate how the long-term impacts of such a ban can be evaluated, explicitly linking changes in smoking prevalence to later-life health outcomes and health system costs. Participants will be exposed to methods for modeling a simulated population cohort, defined by current smoking status, and project individual transitions across smoking-related health states (e.g., current smoker, recent quitter, long-term quitter) over a lifetime horizon. We will model major health events with high smoking-attributable risk, including cardiovascular and cerebrovascular diseases, lung cancer and other cancers, respiratory diseases like COPD, and other smoking-related conditions. Based on disease events, we will estimate lifetime health outcomes and associated health system costs from a policy-focused, decision-maker perspective, comparing the incremental costs and benefits of a menthol ban to current practice. Finally, we will demonstrate how stratified analyses can reveal differences in policy impact across subgroups.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".