Smoking patterns by birth cohort in Argentina: an age-period-cohort population-based modeling study
Bibliographic record
Abstract
Background: Argentina's smoking rates remain high. We aim to estimate Argentina age-specific histories of smoking initiation, cessation, prevalence, and intensity by birth-cohort to inform policy interventions. Methods: Modeling study. Data from three Argentinian nationally representative surveys conducted from 2004 to 2018 (n = 268,193) were used to generate smoking histories. The Cancer Intervention and Surveillance Modeling (CISNET) Network Lung Working Group age, period, and cohort modeling approach was used to calculate smoking initiation and cessation probabilities, ever and current smoking prevalence, and intensity (cigarettes per day, CPD) by age, sex, and birth cohort from 1950 to 2018. Findings: Ever smoking prevalence increases with age up to 25 and decreases with birth cohorts after 1990. Smoking initiation peaks between 15 and 18 years of age. Among females, initiation probabilities increased until the 1955 cohort, reaching a second peak in 1980-85 cohorts and declining thereafter. Males have higher initiation probabilities than females. Among males, initiation has decreased since the 1950 birth cohort, with a slight increase around the 1985 cohort. Current smoking prevalence has been decreasing since the 1960 birth cohort, except for a peak in 1980-85 cohorts. Cessation increases with age. Mean CPD increases with age and peaks around age 40, appearing flat in females since the 1985 cohort. Interpretation: Recent birth cohorts seem to be experiencing lower rates of initiation, stable rates of quitting and lower current and ever smoking prevalence. The stabilization of cessation probabilities and mean CPD indicate the need to strengthen existing tobacco control measures and advance new ones. Funding: NIH/NCI U01CA253858 grant.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".