Secular Trends of Liver Cancer Mortality and Years of Life Lost in Wuhan, China 2010–2019
Bibliographic record
Abstract
BACKGROUND: Liver cancer has caused a heavy burden worldwide. This study aimed to estimate the trends in the mortality and years of life lost (YLL) due to liver cancer and decompose the total deaths into three contributors: population growth, population aging, and mortality change. METHODS: Our study used data from the cause-of-death surveillance system in Wuhan. The mortality and YLL rates were standardized according to the sixth national population census in China. This study calculated the estimated annual percentage change (EAPC) to estimate the trends in the age-standardized mortality rate (ASMR) and age-standardized YLL rate (ASYR). Meanwhile, a decomposition analysis was used to explore the effect of population growth, population aging, and age-specific mortality change on the change in liver cancer deaths. RESULTS: The ASMR of liver cancer declined at an annual rate of 4.6% from 30.87 per 100,000 people in 2010 to 20.29 per 100,000 people in 2019, while the ASYR was at an annual rate of 5.6% from 969.35 per 100,000 people in 2010 to 581.82 per 100,000 people in 2019. Similar downward trends were seen in men and women. The decomposition analysis found that total deaths number changed by -12.42% from 2010 to 2019, of which population growth and population aging caused the total death numbers to increase by 9.75% and 21.15%, while the age-specific mortality change caused the total death numbers to decrease by 43.32%. CONCLUSION: Although the ASMR of liver cancer has declined in recent years in Wuhan, it still causes a heavy burden with the increasing population and rapid population aging and remains an essential public health issue. The government should take measures to reduce the burden of liver cancer, especially among men.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".