Association of Tobacco Use and Cancer Incidence in India: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: To estimate the strength of the association between tobacco use and cancer incidence among the Indian population. MATERIALS AND METHODS: Data from PubMed, Embase, and Virtual Health Library were searched from inception of databases till April 30, 2022. There were no restrictions except for English language and human study. Case-control and cohort studies on cancer incidence in relation to tobacco use were selected. Data were extracted independently by two investigators, and discrepancies were resolved by a third reviewer. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. The quality assessment was done using the Newcastle Ottawa Scale. RESULTS: The majority were case-control designs (60, 89.6%), covering diverse geographic regions, with Maharashtra (18, 30%) and Kerala (12, 20%) being the most studied. Pooled effect sizes were calculated using the random-effects model, and forest plots were generated. The risk of any cancer associated with smoked and smokeless tobacco was 2.71 (95% CI, 2.25 to 3.16) and 2.68 (95% CI, 2.22 to 3.14), respectively, indicating similar risks. Gender-wise, smoked tobacco had an association of 2.35 (95% CI, 2.05 to 2.65) for males, whereas for smokeless tobacco, it was 1.77 (95% CI, 1.47 to 2.07) for males and 2.34 (95% CI, 1.26 to 3.42) for females. Regardless of gender, tobacco type, and affected body parts, the risk of cancer due to tobacco use was consistent in the Indian population. Site-specific analysis showed higher risks of respiratory system cancers of 4.97 (95% CI, 3.62 to 6.32) and head and neck cancers of 3.95 (95% CI, 3.48 to 4.42). CONCLUSION: This study underscores that both smoked and smokeless tobacco are equally harmful to human health among the Indian population, providing insights for stakeholders and policymakers to arrive at tobacco-specific interventions.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".