Health burden and economic costs of smoking in Chile: The potential impact of increasing cigarettes prices
Bibliographic record
Abstract
BACKGROUND: Globally, tobacco consumption continues to cause a huge burden of preventable diseases. Chile has been leading the tobacco burden ranking in the Latin American region for the last ten years; it has currently a 33. 3% prevalence of current smokers. METHODS: A microsimulation economic model was developed within the framework of a multi-country project in order to estimate the burden attributable to smoking in terms of morbidity, mortality, disability-adjusted life-years (DALYs), and direct costs of care. We also modelled the impact of increasing cigarettes' taxes on this burden. RESULTS: In Chile, 16,472 deaths were attributable to smoking in 2017, which represent around 16% of all deaths. This burden corresponds to 416,445 DALYs per year. The country's health system spends 1.15 trillion pesos annually (in Dec 2017 CLP, approx. U$D 1.8 billion) in health care treatment of illnesses caused by smoking. If the price of tobacco cigarettes was to be raised by 50%, around 13,665 deaths and 360,476 DALYs from smoking-attributable diseases would be averted in 10 years, with subsequent savings on health care costs, and increased tax revenue collection. In Chile, the tobacco tax collection does not fully cover the direct healthcare costs attributed to smoking. CONCLUSION: Despite a reduction observed on smoking prevalence between 2010 (40.6%) and 2017 (33.3%), this study shows that the burden of disease, and the economic toll due to smoking, remain high. As we demonstrate, a rise in the price of cigarettes could lead to a significant reduction of this burden, averting deaths and disability, and reducing healthcare spending.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".