Burden of malignant mesothelioma in China during 1990–2019 and the projections through 2029
Bibliographic record
Abstract
Objective: To provide the most up-to-date data on the burden of malignant mesothelioma (MM) and the projections through 2029 in China. Methods: Data on patients diagnosed with MM from China during 1990-2019 were obtained from the Global Burden of Disease (GBD) 2019 database, including annual cases and deaths data and age-standardized rates of incidence, mortality, and disability-adjusted life-years (DALYs) associated with MM among different age groups. Temporal trends during 1990-2019 were analyzed by the Joinpoint regression models using 95% confidence interval (CI), while the projections through 2029 were calculated by the Bayesian age-period-cohort model. Data on the production and consumption of asbestos in China were obtained from the United States Geological Survey on Mineral Commodity Summaries during 1996-2023. Results: We observed a significant elevation in incident new cases and deaths over the last 3 decades, increasing from 1193 in 1990 to 2815 in 2019 for incident cases and from 1134 in 1990 to 2773 in 2019 for death cases. We found a roughly 6% increase in the proportion of incident cases for those aged >70 years (30% in 2019 versus 24% in 1990), while for the proportion of deaths similar elevation for those aged >70 years was found. Additionally, men had significantly higher DALYs due to MM across age groups compared with women. Asbestos consumption in China dramatically dropped since 2012 and reached the bottom in 2017 with 230 kilotons. By 2029, the projected age-standardized rate for incidence and mortality is expected to reach 1.2 per million for both. Conclusion: We found, for the first time using GBD data on the Chinese population, that the burden of MM has been significantly increasing in China over the last three decades and will continue to increase in the upcoming decade, suggesting an urgent need for a complete ban on chrysotile asbestos in China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".