Global Trends of Bladder Cancer Incidence and Mortality, and Their Associations with Tobacco Use and Gross Domestic Product Per Capita
Bibliographic record
Abstract
BACKGROUND: Bladder cancer is a major urological disease, with approximately 550 000 new cases diagnosed in 2018. OBJECTIVE: We examined gender-specific incidence and mortality patterns, and trends of bladder cancer from a global perspective. We further investigated their associations with tobacco use and gross domestic product (GDP) per capita. DESIGN, SETTING, AND PARTICIPANTS: We retrieved data on the incidence and mortality of bladder cancer from the GLOBOCAN database, Cancer Incidence in Five Continents, and the WHO mortality database. Data on the rate of tobacco use were retrieved from the WHO Global Health Observatory. Data on GDP per capita was retrieved from the United Nations Human Development Report. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We performed two sets of analyses. The first set of analysis is based on bladder cancer incidence and mortality data in 2018. The gender-specific age-standardised rates (ASRs) of incidence and mortality, and their correlations with the rate of tobacco use and GDP per capita were investigated. A multivariable linear regression analysis was also performed. In the second set of analysis, we examined the 10-yr temporal trends of bladder cancer incidence and mortality by average annual percent change using joinpoint regression analysis. A further exploratory analysis on GDP per capita in countries with decreasing trends of tobacco use was also performed. RESULTS AND LIMITATIONS: Wide variations in bladder cancer incidence and mortality were observed globally. There were positive correlations between the rate of tobacco use and the ASRs of bladder cancer incidence (r=0.20) and mortality (r=0.38) in men, and between the rate of tobacco use and the ASRs of bladder cancer incidence (r=0.67) and mortality (r=0.22) in women. There were positive correlations between GDP per capita, and the ASRs of bladder cancer incidence in men (r=0.48) and women (r=0.44). There was a weak positive correlation between GDP per capita and bladder cancer mortality in men (r=0.19), but no correlation with bladder cancer mortality in women (r=0.06). Upon multivariable linear regression analysis, tobacco use was significantly associated with bladder cancer incidence and mortality in men, and bladder cancer incidence in women. Regarding the 10-yr temporal trends of bladder cancer, Europe has an increasing incidence but decreasing mortality, and Asia has a decreasing incidence but increasing male mortality. Among countries with decreasing trends of tobacco use, the mean GDP per capita was higher in countries with decreasing trends of bladder cancer mortality than in those with increasing trends of bladder cancer mortality. A major limitation of the study is that cancer incidence might be underdetected and under-reported in less developed nations. CONCLUSIONS: There were observable trends of bladder cancer incidence and mortality globally. Tobacco use was significantly associated with both bladder cancer incidence and mortality. A certain level of economic capacity might be needed to further reduce bladder cancer mortality in countries with a decreasing trend of tobacco use. PATIENT SUMMARY: There are different trends of bladder cancer incidence and mortality globally. Smoking is significantly associated with the incidence and mortality of bladder cancer. A higher financial capacity may be needed to further improve the disease outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".