Association of Non-Tobacco Products (NTP) with Oral, Esophageal, and Pharyngeal Cancer and Oral Potentially Malignant Disorders (OPMD) in Adults: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: Non-tobacco products (NTP) are increasingly popular as an alternative to traditional tobacco use, raising concerns about potential health risks. This systematic review and meta-analysis examine the association of NTP use with oral, esophageal, & pharyngeal cancer, and oral potentially malignant disorders (OPMD) in adults. METHODS: A comprehensive electronic search was conducted in PubMed, Embase, and Google Scholar, for observational studies that evaluated the association between NTP use, and oral, esophageal, & pharyngeal cancer and OPMD, published till December 2023 in the English language. The quality of the included studies was assessed using the Newcastle-Ottawa scale. Pooled odds ratio (OR) with a 95% Confidence Interval (CI) was obtained using a random effects model, heterogeneity was assessed using I2 statistic. Sub-group analysis was conducted based on the year of publication, a comparator for association, type of NTP user, and country. RESULTS: Forty-nine studies were included in the meta-analysis with a combined total of 1,32,390 participants, revealing a significant association between NTP use and increased risk of cancers- oral (OR:5.0, 95% CI 3.3-7.7), esophageal (OR:3.0, 95%CI 1.2-7.5), and OPMD (OR:15.3, 95%CI 8.8-26.7). The proportion of the annual burden of oral and esophageal cancers attributable to NTP was 33% (30,667 cancers), and 19% (8,025 cases) respectively in India. CONCLUSION: This review provides strong evidence for the association between NTP use and increased risk of oral, and esophageal cancer and OPMD. The findings highlight the need for targeted interventions, firmer regulations, and awareness campaigns addressing NTP. Incorporating NTP as a separate category in national surveys can help reduce their burden, particularly in high-risk populations.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".