Modeling the Impact of Smoking on Mortality in Argentina From 2000 to 2100. A Maximum Potential Reduction in Premature Mortality Analysis
Bibliographic record
Abstract
INTRODUCTION: In Argentina, 23% of adults smoke; the future burden of smoking on mortality in the country is unknown. We estimate future smoking-attributable mortality if current smoking trends continue and compare this with an ideal scenario in which all smoking ceases in 2024. AIMS AND METHODS: We developed a discrete deterministic compartmental simulation model of cigarette smoking by birth cohort in Argentina. The model was validated against observed sex-specific adult smoking prevalence. We then simulated smoking prevalence, smoking-attributable deaths (SADs), and life-years lost (LYL) from 2000 to 2100 under a Status Quo scenario, where future smoking prevalence follows current trends. Additionally, we modeled an ideal scenario where all smoking ceases starting in 2024. We calculated the Maximum Potential Reduction in Premature Mortality (MPRPM) as the LYL difference between the two scenarios from 2024 to 2100. RESULTS: The model adequately reproduces observed smoking prevalence in Argentina. Approximately 55,700 SADs are estimated to occur in 2024. Under the Status Quo, over 4 million deaths due to smoking and around 79 million LYL would occur from 2000 to 2100. If all smoking had ceased in 2024, 49 million LYL due to smoking would still occur, resulting in an MPRPM of 30 million years, about 38% of the expected burden. CONCLUSIONS: Argentina faces a significant smoking-attributable mortality burden this century, with a substantial portion already unavoidable due to past smoking. Further tobacco control interventions, however, could still considerably reduce premature deaths and years of life lost. Prompt action is needed to realize these potential health gains. IMPLICATIONS: This modeling study provides an estimation of the future burden of smoking-attributable mortality in Argentina and highlights the maximum potential health benefits if all smoking would cease by 2024. While a portion of smoking-related mortality is unavoidable due to past smoking, the results show that further tobacco control interventions could still prevent a substantial number of premature deaths and life-years lost. These findings underscore the need for continued public health efforts to reduce smoking rates and mitigate its long-term effects on population health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".