The Global, Regional, and National Burdens of Cervical Cancer Attributable to Smoking From 1990 to 2019: Population-Based Study
Bibliographic record
Abstract
BACKGROUND: Cervical cancer is the fourth most common cause of cancer death in women worldwide. Smoking is one of the risk factors for cervical cancer. Understanding the global distribution of the disease burden of cervical cancer attributable to smoking and related changes is of clear significance for the prevention and control of cervical cancer in key populations and for tobacco control. As far as we know, research on the burden of cervical cancer attributable to smoking is lacking. OBJECTIVE: We estimated the disease burden and mortality of cervical cancer attributable to smoking and related trends over time at the global, regional, and national levels. METHODS: Data were obtained from the Global Burden of Disease study website. Age-standardized rates were used to facilitate comparisons of mortality and disability-adjusted life years (DALYs) at different levels. The estimated annual percentage change (EAPC) was used to assess trends in the age-standardized mortality rate (ASMR) and the age-standardized DALY rate (ASDR). A Pearson correlation analysis was used to evaluate correlations between the sociodemographic index and the age-standardized rates. RESULTS: In 2019, there were 30,136.65 (95% uncertainty interval [UI]: 14,945.09-49,639.87) cervical cancer-related deaths and 893,735.25 (95% UI 469,201.51-1,440,050.85) cervical cancer-related DALYs attributable to smoking. From 1990 to 2019, the global burden of cervical cancer attributable to smoking showed a decreasing trend around the world; the EAPCs for ASMR and ASDR were -2.11 (95% CI -2.16 to -2.06) and -2.22 (95% CI -2.26 to -2.18), respectively. In terms of age characteristics, in 2019, an upward trend was observed for age in the mortality of cervical cancer attributable to smoking. Analysis of the trend in DALYs with age revealed an initially increasing and then decreasing trend. From 1990 to 2019, the burden of disease in different age groups showed a downward trend. Among 204 countries, 180 countries showed downward trends, 10 countries showed upward trends, and the burden was stable in 14 countries. The Pearson correlation analysis revealed a significant negative correlation between sociodemographic index and the age-standardized rates of cervical cancer attributable to smoking (ρ=-0.228, P<.001 for ASMR and ρ=-0.223, P<.001 for ASDR). CONCLUSIONS: An increase over time in the absolute number of cervical cancer deaths and DALYs attributable to smoking and a decrease over time in the ASMR and ASDR for cervical cancer attributable to smoking were observed in the overall population, and differences in these variables were also observed between countries and regions. More attention should be paid to cervical cancer prevention and screening in women who smoke, especially in low- and middle-income countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 0.001 |
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".