The case for gender considerate tobacco control policies in Albania
Bibliographic record
Abstract
Background: Tobacco use is a serious health concern in Albania. While the prevalence of tobacco smoking has traditionally been higher for men, the increasing prevalence of smoking for women is becoming a concern. The 2007 Tobacco Control policy mandated smoke-free indoor spaces, banned various forms of tobacco advertising, required written health warnings on packaging and levied excise taxes on cigarette sales. Since smoking behavior varies among different demographic groups, each group's response to a uniform policy will differ, blunting the effectiveness of these efforts as a result. This study examines various socioeconomic, demographic and behavioral factors affecting both the likelihood and frequency of smoking in Albanian households in order to provide insights on targeting various populations more effectively. Methods: The study utilizes data from Albanian 2008-09 and 2017-18 Demographic and Health Surveys consisting of adults aged 15-49 years. The outcome variable includes respondents' current tobacco smoking behaviour and its frequency. The exposure variables include respondents' sociodemographic and lifestyle characteristics. We use a two-level random intercept model with the two-stage residual inclusion estimation method to determine the association between outcome and exposure variables. By including a time variable, we capture the change in smoking behavior during the 2009-2018 period. We also extend the analysis by assessing the differential influence of gender on the likelihood of smoking, both by income quintiles and education. Results: The results suggest that the likelihood of smoking decreased by 23% in 2017-18 compared to 2008-09, after controlling for various socioeconomic and demographic factors. Tobacco smoking is also found to be linked to alcohol consumption, with binge drinkers 59% more likely to smoke tobacco compared to moderate drinkers. We also found significant inter-quintile and inter-educational differences in smoking practices within each gender category. While the likelihood of tobacco smoking decreases with increasing wealth and educational attainment among men, the opposite (for wealth) or more involved (for educational attainment) patterns are true among women. Conclusions: To further enhance the effectiveness of the current Tobacco Control policy, the Government of Albania should target various demographic groups (such as poor males, rich and educated females) in a differentiated fashion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".