Unlocking the power of tobacco taxation to mitigate the social costs of smoking in Mexico: a microsimulation model
Bibliographic record
Abstract
Despite being the most cost-effective tobacco control policy, tobacco taxation is the least implemented component of the World Health Organization MPOWER package to reduce smoking worldwide. In Mexico, both smoking prevalence and taxation have remained stable for more than a decade. This study aims to provide evidence about the potential effects of taxation to reduce the burden of tobacco-related diseases and the main attributable social costs in Mexico, including informal (unpaid) care costs, which are frequently ignored. We employ a first-order Monte Carlo microsimulation model that follows hypothetical population cohorts considering the risks of an adverse health event and death. First, we estimate tobacco-attributable morbidity and mortality, direct medical costs and indirect costs, such as labour productivity losses and informal care costs. Then, we assess the potential effects of a 50% cigarette price increase through taxation and two alternative scenarios of 25% and 75%. The inputs come from several sources, including national surveys and vital statistics. Each year, 63 000 premature deaths and 427 000 disease events are attributable to tobacco in Mexico, while social costs amount to MX$194.6 billion (US$8.5)-MX$116.2 (US$5.1) direct medical costs and MX$78.5 (US$3.4) indirect costs-representing 0.8% of gross domestic product. Current tobacco tax revenue barely covers 23.3% of these costs. Increasing cigarette prices through taxation by 50% could reduce premature deaths by 49 000 over the next decade, while direct and indirect costs averted would amount to MX$87.9 billion (US$3.8) and MX$67.6 billion (US$2.9), respectively. The benefits would far outweigh any potential loss even in a pessimistic scenario of increased illicit trade. Tobacco use imposes high social costs on the Mexican population, but tobacco taxation is a win-win policy for both gaining population health and reducing tobacco societal costs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".