Global, Regional and National Burden of Bladder Cancer, 1990 to 2016: Results from the GBD Study 2016
Bibliographic record
Abstract
PURPOSE: Bladder cancer is among the leading causes of cancer death worldwide. Data on the bladder cancer burden are valuable for policy-making. We aimed to estimate the burden of bladder cancer by country, age group, gender and sociodemographic status between 1990 and 2016. MATERIALS AND METHODS: Data from vital registration systems and cancer registries were the input to estimate the bladder cancer burden. Mortality was estimated in an ensemble model approach, incidence was estimated by dividing mortality by the mortality-to-incidence ratio and prevalence was estimated using the mortality-to-incidence ratio as a surrogate for survival. We modeled the years lived with disability using disability weights of bladder cancer sequelae. Years of life lost were calculated by multiplying the number of deaths by age by the standard life expectancy at that age. Disability adjusted life-years were calculated by summing the years lived with disability and the years of life lost. Moreover, we also estimated the burden attributable to bladder cancer risk factors, smoking and high fasting plasma glucose using the comparative risk assessment framework of the Global Burden of Disease study. RESULTS: In 2016 there were 437,442 incident cases (95% UI 426,709-447,912) of bladder cancer with an age standardized incidence rate of 6.69/100,000 (95% UI 6.52-6.85). Bladder cancer led to 186,199 deaths (95% UI 180,453-191,686) in 2016 with an age standardized rate of 2.94/100,000 (95% UI 2.85-3.03). Bladder cancer was responsible for 3,315,186 disability adjusted life-years (95% UI 3,193,248-3,425,530) in 2016 with an age standardized rate of 49.45/100,000 (95% UI 47.68-51.11). Of bladder cancer deaths 26.84% (95% UI 19.78-33.91) and 7.29% (95% UI 1.49-16.19) were due to smoking and high fasting glucose, respectively, in 2016. CONCLUSIONS: Although the number of bladder cancer incident cases is growing globally, the age standardized incidence and number of deaths are decreasing, as mirrored by a decreasing smoking contribution.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".