The Burden and Risk Factors of Gastric Cancer in Eastern Asia From 1990 to 2021: Longitudinal Observational Study of the Global Burden of Disease Study 2021
Bibliographic record
Abstract
Background: Eastern Asia has historically had the highest global incidence and mortality rates of gastric cancer (GC) while substantial disparities exist between countries. The overall burden of GC remains insufficiently explored. Objective: Using the Global Burden of Disease Study 2021, this research aims to estimate the burden and risk factors of GC in Eastern Asia from 1990 to 2021. Methods: Incidence, age-standardized incidence rate (ASIR), deaths, age-standardized mortality rate (ASMR), disability-adjusted life years, age-standardized disability-adjusted life year rate (ASDR), and risk factor burdens for GC were analyzed in Eastern Asia from 1990 to 2021. Joinpoint analysis determined average annual percent change (AAPC) and annual percent change, while age-period-cohort analysis assessed temporal trends. The Bayesian age-period-cohort model projected GC burden from 2021 to 2035. All analyses used R software (version 4.4.1; R Foundation for Statistical Computing). Results: In 2021, Eastern Asia reported 748,235 new GC cases and 527,054 deaths, accounting for 60.8% (748,235/1,230,232) of new cases and 55.2% (527,054/954,373) of deaths reported globally. From 1990 to 2021, South Korea showed the largest declines in ASIR, ASMR, and ASDR, with ASMR decreasing from 55.4 per 100,000 to 13.3 per 100,000 (AAPC -4.5, 95% CI -4.8 to -4.3). ASIR, ASMR, and ASDR also showed a downward trend in Japan and China, with an AAPC of -3.0 (95% CI -3.2 to -2.8) for ASMR in Japan and -2.4 (95% CI -2.6 to -2.3) in China. The GC burden of North Korea was basically stable, with an AAPC of ASMR of -0.8 (95% CI -0.8 to -0.8). Mongolia showed a slight decline, with an AAPC of ASMR of -1.4 (95% CI -1.7 to -1.0), and the burden of GC was the highest. High-sodium diets and smoking were the main risk factors for disability-adjusted life years of GC in 2021. Smoking contributed to a decline in ASDR as the sociodemographic index increased. Projections suggest continued ASDR reductions across Eastern Asia from 2022 to 2035, though Mongolia will maintain the highest burden. Conclusions: Despite a decrease from 1990 to 2021, GC remains a significant public health issue in Eastern Asia. Addressing it necessitates prioritizing primary and secondary prevention, including reducing risk factors and enhancing early screening.
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 | 0.001 |
| 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".