Health, economic and social burden of tobacco in Latin America and the expected gains of fully implementing taxes, plain packaging, advertising bans and smoke-free environments control measures: a modelling study
Bibliographic record
Abstract
OBJECTIVE: To investigate the tobacco-attributable burden on disease, medical costs, productivity losses and informal caregiving; and to estimate the health and economic gains that can be achieved if the main tobacco control measures (raising taxes on tobacco, plain packaging, advertising bans and smoke-free environments) are fully implemented in eight countries that encompass 80% of the Latin American population. DESIGN: Markov probabilistic microsimulation economic model of the natural history, costs and quality of life associated with the main tobacco-related diseases. Model inputs and data on labour productivity, informal caregivers' burden and interventions' effectiveness were obtained through literature review, surveys, civil registrations, vital statistics and hospital databases. Epidemiological and economic data from January to October 2020 were used to populate the model. FINDINGS: In these eight countries, smoking is responsible each year for 351 000 deaths, 2.25 million disease events, 12.2 million healthy years of life lost, US$22.8 billion in direct medical costs, US$16.2 billion in lost productivity and US$10.8 billion in caregiver costs. These economic losses represent 1.4% of countries' aggregated gross domestic products. The full implementation and enforcement of the four strategies: taxes, plain packaging, advertising bans and smoke-free environments would avert 271 000, 78 000, 71 000 and 39 000 deaths, respectively, in the next 10 years, and result in US$63.8, US$12.3, US$11.4 and US$5.7 billions in economic gains, respectively, on top of the benefits being achieved today by the current level of implementation of these measures. CONCLUSIONS: Smoking represents a substantial burden in Latin America. The full implementation of tobacco control measures could successfully avert deaths and disability, reduce healthcare spending and caregiver and productivity losses, likely resulting in large net economic benefits.
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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) | 0.001 | 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".