Patterns of Birth Cohort‒Specific Smoking Histories in Brazil
Bibliographic record
Abstract
INTRODUCTION: Smoking prevalence has decreased considerably in Brazil from 34.8% in 1989 to 12.6% in 2019 owing to the implementation of strong tobacco control policies. However, recent data show that the downward trend may be stagnating. Detailed analyses of historical smoking patterns by birth cohort could guide tobacco control decision making in Brazil. METHODS: Using the 2008 Global Adult Tobacco Survey and the 2013 and 2019 National Health Surveys, historical smoking patterns in Brazil were estimated, supplemented with data from the 2006‒2019 Surveillance System of Risk Factors for Chronic Diseases by Telephone Interviews. Age‒period‒cohort models with constrained natural splines were applied to estimate the annual probabilities of smoking initiation and cessation, current smoker prevalence, and mean cigarettes smoked per day by age, gender, and birth cohort. Analysis was conducted in 2021‒2022. RESULTS: Current smoker prevalence has declined considerably since the 1950 and 1955 birth cohorts for males and females, respectively, reflecting decreased smoking initiation and increased smoking-cessation probabilities over time. Among female cohorts born on or after 2000, smoking initiation may be increasing even as their smoking cessation has increased considerably. Mean cigarettes smoked per day has remained relatively constant across period and cohorts, showing only a minor decrease among males. CONCLUSIONS: These detailed cohort-specific smoking parameters can be used to inform models that evaluate the impact of tobacco use and policies on long-term health outcomes and guide public health decision making in Brazil. Stagnant mean cigarettes smoked per day, increasing female smoking initiation, and limited improvement in male cessation among recent cohorts present challenges to tobacco control.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".