Assessing mortality trends among patients with lip and oral cavity cancer due to tobacco consumption: A systematic analysis of the Global Burden of Disease-2021.
Bibliographic record
Abstract
e18134 Background: Tobacco is an established risk factor for lip and oral cavity cancer (LOC). While a previous study quantified the global and regional burden of LOC and other pharyngeal cancers using the 2019 Global Burden of Diseases, Injuries, and Risk Factors (GBD) study estimates, comprehensive and up-to-date evaluations of tobacco-attributable LOC burden across different locations remain lacking. Given that LOC is an important contributor to the global cancer burden, this study aims to provide a thorough evaluation of the tobacco-attributed global and regional burden of LOC to enhance effective policy planning. Methods: In this analysis of GBD 2021 data, we examined temporal trends in mortality, years lived with disability (YLD), years of life lost (YLL), and disability-adjusted life-years (DALY) globally and in 204 countries and territories from 1990 to 2021 using age-adjusted standardized rates across all ages and sexes. Temporal trends were assessed using the average annual percentage change (AAPC) and corresponding 95% confidence intervals (CI). Metrics were calculated using standardized GBD methods, and the findings were stratified by country, with a comprehensive global summary highlighting the overarching pattern. Results: Globally, the burden of lip and oral cancers caused by tobacco has increased across all metrics from 1990 to 2021. The global AAPC was 0.38 (95%CI 0.38-0.45, p<0.001) for death, 1.02 (95%CI 0.93-1.11, p<0.001) for YLD, 0.28 (95%CI 0.21-0.34, p<0.001) for YLL and 0.29 (95%CI 0.23-0.35, p<0.001) for DALY. Regionally, Cabo Verde experienced the highest increases across all metrics, with AAPC values of 8.45 (95%CI 5.8-11.1) for mortality, 8.99 (95%CI 6.4-11.5) for YLD, 8.10 (95%CI 5.4-10.7) for YLL and 8.12 (95%CI 5.4-10.8) for DALY indicating a concerning rise in disease burden. Conversely, Canada showed the largest declines, with AAPC values of -2.78 (95%CI -2.9 to -2.5) for mortality, -2.06 (95%CI -2.1 to -1.9) for YLD, -2.95 (95%CI -3.1 to -2.7) for YLL and -2.89 (95%CI -3.0 to -2.7) for DALY reflecting notable improvements, likely attributed to effective tobacco control efforts. Conclusions: The global burden of LOC caused by tobacco remains substantial with marked regional disparities. Cabo Verde has emerged as a region requiring urgent targeted interventions, while Canada’s declining trends highlight the effectiveness of comprehensive tobacco control strategies. These findings enhance our understanding of the distribution and disparities in the LOC burden and underscore the need for synergistic, systematic, and multi-sectoral efforts, modeled after Canada, to mitigate this burden.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".