Prevalence, pattern and sociodemographic differentials in smokeless tobacco consumption in Bangladesh: evidence from a population-based cross-sectional study in Chakaria
Bibliographic record
Abstract
BACKGROUND: The health hazards associated with the use of smokeless tobacco (SLT) are similar to those of smoking. However, unlike smoking, limited initiatives have been taken to control the use of SLT, despite its widespread use in South and Southeast Asian countries including Bangladesh. It is therefore important to examine the prevalence of SLT use and its social determinants for designing appropriate strategies and programmes to control its use. OBJECTIVE: To investigate the use of SLT in terms of prevalence, pattern and sociodemographic differentials in a rural area of Bangladesh. DESIGN: Population-based cross-sectional household survey. SETTING AND PARTICIPANTS: A total of 6178 individuals aged ≥13 years from 1753 households under the Chakaria HDSS area were interviewed during October-November 2011. METHODS: The current use of SLT, namely sadapatha (dried tobacco leaves) and zarda (industrially processed leaves), was used as the outcome variable. The crude and net associations between the sociodemographic characteristics of respondents and the outcome variables were examined using cross-tabular and multivariable logistic regression analysis, respectively. RESULTS: 23% of the total respondents (men: 27.0%, women: 19.3%) used any form of SLT. Of the respondents, 10.4% used only sadapatha,13.6% used only zarda and 2.2% used both. SLT use was significantly higher among men, older people, illiterate, ever married, day labourers and relatively poorer respondents. The odds of being a sadapatha user were 3.5-fold greater for women than for men and the odds of being a zarda user were 3.6-fold greater for men than for women. CONCLUSIONS: The prevalence of SLT use was high in the study area and was higher among socioeconomically disadvantaged groups. The limitation of the existing regulatory measures for controlling the use of non-industrial SLT products should be understood and discussion for developing new strategies should be a priority.
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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 | 0.000 | 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".