The Epidemiology of Tobacco Use among Khat Users: A Systematic Review
Bibliographic record
Abstract
Khat, an "amphetamine-like green leaf," may influence the consumption of tobacco. This study reviews the epidemiology of tobacco use among khat users. Electronic database searches using appropriate keywords/terms were conducted to identify observational studies of khat use. Assessment of quality and risk of bias of all included studies was conducted, and the results were synthesised descriptively. Nine eligible cross-sectional studies were identified. All assessed self-reported tobacco among khat users and were carried out in Africa and the Middle East. Eight reported cigarettes and one reported waterpipes as the mode of use. Methods of tobacco use prevalence assessment varied. Prevalence of "current" tobacco use among students and university teachers ranged from 29 to 37%; "lifetime" tobacco use in university teachers was 58% and "undefined" tobacco use in nonspecific adults and students ranged from 17 to 78%. Daily tobacco use among adults was reported as 17% whilst simultaneous tobacco and khat use was reported as between 14 and 30% in students. In conclusion, tobacco prevalence among khat users appears significant. Findings should be interpreted cautiously due to self-reported tobacco use, diversity in questions assessing tobacco use, and type of tobacco consumption. Future research should address the methodological shortcomings identified in this review before appropriate policy interventions can be developed.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".