Temporal Trends in Tobacco Smoking Prevalence During the Period 2010–2020 in Vietnam: A Repeated Cross-Sectional Study
Bibliographic record
Abstract
Objectives: This study used repeated cross-sectional data from three national surveys in Vietnam to determine tobacco smoking prevalence from 2010 to 2020 and disparities among demographic and socioeconomic groups. Methods: Tobacco smoking temporal trends were estimated for individuals aged 15 and over and stratified by demographic and socioeconomic status (SES). Prevalence estimates used survey weights and 95% confidence intervals. Logistic regression models adjusted for survey sample characteristics across time were used to examine trends. Results: Tobacco smoking prevalence dropped from 23.8% in 2010 to 22.5% in 2015 and 20.8% in 2020. The adjusted OR for 2015 compared to 2010 was 0.87, and for 2020 compared to 2010 was 0.69. Smoking decreased less for employed individuals than unemployed individuals in 2020 compared to 2010. Smoking was higher in the lower SES group in all 3 years. Higher-SES households have seen a decade-long drop in tobacco use. Conclusion: This prevalence remained constant in lower SES households. This highlights the need for targeted interventions to address the specific challenges faced by lower-SES smokers and emphasizes the importance of further research to inform effective policies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".