Secular Trends in Gastric and Esophageal Cancer Attributable to Dietary Carcinogens From 1990 to 2019 and Projections Until 2044 in China: Population-Based Study
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
BACKGROUND: Little is known about trends in or projections of the disease burden of dietary gastric and esophageal cancer (GEC) in China. OBJECTIVE: We aim to report GEC deaths and disability-adjusted life years (DALYs) from 1990 to 2019, predict them through 2044, and decompose changes in terms of population growth, population aging, and epidemiological changes. METHODS: We retrieved dietary GEC data from the Global Burden of Disease (GBD) online database and used joinpoint regression and age-period-cohort models to analyze trends in dietary GEC deaths and DALYs from 1990 to 2019 in China. We used a Bayesian age period cohort model of integrated nested Laplace approximations to predict the disease burden of GEC through 2044 and obtained the estimated population of China from 2020 to 2050 from the Global Health Data Exchange website. Finally, we applied a recently developed decomposition method to attribute changes between 2019 and 2044 to population growth, population aging, and epidemiological changes. RESULTS: The summary exposure values and age-standardized rates decreased significantly from 1990 to 1999, with percentage changes of -0.06% (95% CI -0.11% to -0.02%) and -0.05% (95% CI -0.1% to -0.02%), respectively. From 1990 to 2019, for dietary esophageal cancer, the percentage change in age-standardized mortality rate (ASMR) was -0.79% (95% CI -0.93% to -0.58%) and the percentage change in age-standardized DALY rate (ASDR) was -0.81% (95% CI -0.94% to -0.61%); these were significant decreases. For dietary stomach cancer, significant decreases were also observed for the percentage change in ASMR (-0.43%, 95% CI -0.55% to -0.31%) and the percentage change in ASDR (-0.47%, 95% CI -0.58% to -0.35%). In addition, data from both the joinpoint regression and annual percentage change analyses demonstrated significantly decreasing trends for the annual percentage change in ASMR and ASDR for GEC attributable to dietary carcinogens. The overall annual percentage change (net drift) was -5.95% (95% CI -6.25% to -5.65%) for dietary esophageal cancer mortality and -1.97% (95% CI -2.11% to -1.83%) for dietary stomach cancer mortality. Lastly, in 2044, dietary esophageal cancer deaths and DALYs were predicted to increase by 192.62% and 170.28%, respectively, due to age structure (121.58% and 83.29%), mortality change (76.81% and 92.43%), and population size (-5.77% and -5.44%). In addition, dietary stomach cancer deaths and DALYs were predicted to increase by 118.1% and 54.08%, with age structure, mortality rate change, and population size accounting for 96.71% and 53.99%, 26.17% and 3.97%, and -4.78% and -3.88% of the change, respectively. CONCLUSIONS: Although the predicted age-standardized rates of mortality and DALYs due to dietary GEC show downward trends, the absolute numbers are still predicted to increase in the next 25 years due to rapid population aging 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".