Interventions for waterpipe tobacco smoking prevention and cessation: a systematic review
Bibliographic record
Abstract
Waterpipe tobacco smoking is growing in popularity despite adverse health effects among users. We systematically reviewed the literature, searching MEDLINE, EMBASE and Web of Science, for interventions targeting prevention and cessation of waterpipe tobacco smoking. We assessed the evidence quality using the Cochrane (randomised studies), GRADE (non-randomised studies) and CASP (qualitative studies) frameworks. Data were synthesised narratively due to heterogeneity. We included four individual-level, five group-level, and six legislative interventions. Of five randomised controlled studies, two showed significantly higher quit rates in intervention groups (bupropion/behavioural support versus placebo in Pakistan; 6 month abstinence relative risk (RR): 2.3, 95% CI 1.4-3.8); group behavioural support versus no intervention in Egypt, 12 month abstinence RR 3.3, 95% CI 1.4-8.9). Non-randomised studies showed mixed results for cessation, behavioural, and knowledge outcomes. One high quality modelling study from Lebanon calculated that a 10% increase in waterpipe tobacco taxation would reduce waterpipe tobacco demand by 14.5% (price elasticity of demand -1.45). In conclusion, there is a lack of evidence of effectiveness for most waterpipe interventions. While few show promising results, higher quality interventions are needed. Meanwhile, tobacco policies should place waterpipe on par with cigarettes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".