Health risks of secondhand exposure to alternative tobacco products
Bibliographic record
Abstract
Background: Electronic nicotine delivery systems (ENDS), hookah, and bidis pose known harm to users, but the health risks of their secondhand exposure are still poorly understood. Objectives: To evaluate existing evidence on the health impacts of passive exposure to these products. Methods: We systematically reviewed original human studies on clinical or biologic effects of secondhand exposure to ENDS, hookah, and bidis (inception–December 2024) in Cinahl, Web of Science, Scopus, PubMed, Cochrane, and Embase. RCTs, cohort, case-control, cross-sectional, and experimental designs were eligible. Two authors independently assessed risk of bias using an adapted Newcastle-Ottawa scale. Study quality was rated as good, fair, or poor based on risk of bias, study design, and methodological rigor. Results: Of 8,810 articles, 21 were included (9 cross-sectional, 2 case-control, 8 experimental, 1 cohort). Thirteen studied secondhand e-cigarette exposure (5 good quality, 4 fair, 4 poor), showing increased ear infections, mental health issues, asthma diagnoses and exacerbations, as well as nose/throat symptoms and heightened inflammatory markers. Six examined hookah (3 good, 3 fair), finding higher urinary levels of benzene, toluene, and other toxicants/carcinogens, as well as increased risk of childhood cancer and COPD in women. One study (poor quality) linked passive bidis to lung cancer; another (poor quality) linked heat-not-burn products to elevated volatile organic compounds and particulate matter. Conclusions: Due to heterogeneity, meta-analysis was not feasible. However, the aggregated evidence suggests secondhand exposure to these products is hazardous and should be considered when devising public health policies.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".