Socioeconomic inequality in smoking: Evidence from a decomposition analysis
Bibliographic record
Abstract
ProblemVietnam is a lower-middle-income country with a very high prevalence of smoking among men. The poor are more likely to smoke than the better off. In this study, we examine socioeconomic inequality in smoking and factors associated with the wealth-related inequality of smoking in Vietnam.MethodsWe use data from the 2010 and 2015 Global Adult Tobacco Surveys (GATS). We use a concentration index approach to assess differences in smoking behavior over the distribution of wealth levels. We also use the regression-based decomposition method developed by Wagstaff et al. (2003) and Doorslaer and Koolman (2004) to decompose wealth-related inequality in smoking into inequalities in wealth and other explanatory variables, such as men's age and education.ResultsPoorer men are more likely to smoke and smoke more than those better off. In 2015, 47.9% in the poorest wealth quintile smoked every day compared to 29.1% in the richest quintile. In 2015, the concentration index of wealth-related inequality in daily smoking was estimated at −0.104 (CI: 0.135; −0.072). Education and occupation are important factors in wealth-related inequality in smoking, because the poor tend to have lower education levels and are employed in unskilled jobs but are more likely than the rich to be smokers. At 41.4%, unskilled workers make the largest contribution to wealth-related inequality in smoking.ConclusionsOur findings suggest that tobacco prevention efforts should be focused on poor, less educated people. Policies that boost the access of the poor to education and better employment can help them not only increase their wealth level but also reduce smoking, thereby narrowing the wealth-inequality gap in smoking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".