A longitudinal study of transitions between smoking and smokeless tobacco use from the ITC Bangladesh Surveys: implications for tobacco control in the Southeast Asia region
Bibliographic record
Abstract
Background: In Southeast Asia, tobacco use is a major public health threat. Tobacco users in this region may switch between or concurrently use smoked tobacco and smokeless tobacco (SLT), which makes effective tobacco control challenging. This study tracks transitions of use among different product users (cigarettes, bidis, and SLT) in Bangladesh, one of the largest consumers of tobacco in the region, and examines factors related to transitions and cessation. Methods: Four waves (2009-2015) of the International Tobacco Control (ITC) Bangladesh Survey with a cohort sample of 3245 tobacco users were analysed. Generalized Estimating Equations (GEE) models were used to explore the socioeconomic correlates of transitions from the exclusive use of cigarettes, bidis, or SLT to the use of other tobacco products or quitting over time. Findings: Among exclusive cigarette users, most remained as exclusive cigarette users (68.1%). However, rural smokers were more likely than urban smokers to transition to bidi use (odds ratio [OR] = 3.02, 95% confidence interval [CI] = 1.45-6.29); to SLT use (OR = 2.68, 95% CI = 1.79-4.02) and to quit tobacco (OR = 1.57, 95% CI = 1.06-2.33). Among exclusive bidi users, transitional patterns were more volatile. Fewer than half (43.3%) of the exclusive bidi users maintained their status throughout the waves. Those with higher socio-economic status (SES) were more likely to quit (OR = 4.16, 95% CI = 1.08-13.12) compared to low SES smokers. Exclusive SLT users either continued using SLT or quit with minimal transitions to other products (≤2%). Nevertheless, males were more likely to switch to other tobacco products; younger (OR = 2.94, 95% CI = 1.23-6.90 vs. older), more educated (OR = 1.55, 95% CI = 1.77-3.12 vs. less educated), and urban SLT users (OR = 0.52, 95% CI = 0.30-0.86 for rural vs. urban users) were more likely to quit. Interpretation: Complex transitional patterns were found among different types of tobacco product users over time in Bangladesh. These findings can inform more comprehensive and multi-faceted approaches to tackle diversified tobacco use in Bangladesh and neighbouring countries in the Southeast Asia region with similar tobacco user profiles of smoked tobacco and SLT products. Funding: This is an unfunded observational study with the use the ITC Bangladesh datasets. The ITC Bangladesh Surveys were supported by grants from the US National Cancer Institute (P01 CA138389), the International Development Research Centre (IDRC Grant 104831-003), and Canadian Institutes of Health Research (MOP-79551, MOP-115016).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".