Abstract
Objectives
To comprehensively assess the gastric cancer burden in China over the past three decades and analyze its epidemiological trends to inform control strategies.
Study design
This was a population-based observational study that systematically evaluated the burden of gastric cancer in China from 1990 to 2023, utilizing data from the Global Burden of Disease (GBD) 2023 study.
Methods
We evaluated gastric cancer burden metrics from GBD 2023, including incidence, mortality, YLLs, YLDs, and DALYs. Joinpoint regression and age-period-cohort modeling were employed to analyze trends and epidemiological characteristics.
Results
Between 1990 and 2023, the age-standardized incidence rate decreased from 54.23 to 25.04 per 100,000 , and mortality decreased from 50.93 to 16.62 per 100,000. Joinpoint analysis showed an average annual percentage change of -2.8% in incidence, with a significant trend reversal in females during 2020-2023. Age-period-cohort analysis revealed peak risk at 65-69 years and lower risk in recent birth cohorts.
Conclusions
While China has achieved substantial reductions in gastric cancer burden over 34 years, emerging challenges include trend reversals in females and persistent sex disparities, necessitating targeted prevention strategies.
Keywords: Gastric cancer, Disease burden, China, Incidence, Prevalence, Age-period-cohort, Joinpoint regression, ARIMA forecasting
1. Introduction
Gastric cancer has been recognized as a significant public health concern since the 1980s, as it poses a substantial socioeconomic burden to populations in high-incidence regions [1,2]. However, gastric cancer is inadequately characterized in temporal dynamics in China [3]. This phenomenon is particularly critical among elderly populations aged 65-69 years [4,5]. Strengthening early detection programs and promoting gender-specific prevention strategies are essential steps to alleviate the disease burden. Furthermore, males with persistent high-risk profiles face even greater challenges, making it imperative to identify and target these high-risk populations for early intervention. Therefore, comprehensive gender-disaggregated temporal analysis can aid in informing policy development. Although previous studies have examined gastric cancer trends in China, existing research is limited in temporal scope and has focused only on aggregate national trends without gender-specific analysis [6]. There is a need for comprehensive gender-disaggregated temporal analysis to provide evidence-based gender-specific prevention frameworks. Advanced temporal modeling may offer quantitative evidence for policymakers, optimize resource allocation to minimize the burden, and help fill existing knowledge gaps.
This article examined temporal trends in associated with gastric cancer in China from 1990 to 2023. We explored gender-specific burden patterns via joinpoint regression analysis and age-period-cohort (APC) modeling. Furthermore, we focused on decomposing temporal variations, including age effects, period effects and birth cohort effects. Data from the Global Burden of Disease (GBD) 2023 study were used to characterize temporal trends and gender disparities in China and promote development of evidence-based prevention strategies through the national cancer control program.
2. Methods
2.1. Overview
The Global Burden of Disease Study 2023 encompasses 375 diseases and injuries across 204 countries and territories, incorporating estimations from various models for disease and injury outcomes [7]. The primary statistical methods and mathematical models employed for estimation include the Cause of Death Ensemble models (CODEm), the spatiotemporal Gaussian process regression (ST-GPR), and the Disease Model - Meta-Regression (DisMod-MR). These are utilized to estimate prevalence, incidence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs). Data on gastric cancer burdens in China from 1990 to 2023 were collected via the Global Health Data Exchange platform (http://ghdx.healthdata.org/gbd-results-tool).
2.2. Joinpoint regression analysis
Joinpoint regression models assess the variation in the trends in incidence rates by identifying the combinations of trends [8]. Joinpoint regression analyses were performed following the methodology established by the National Cancer Institute (NCI), implemented in Python. The core algorithm applies the permutation test approach described by Kim et al. (2000) to identify statistically significant joinpoints and compute annual percentage change (APC) and average annual percentage change (AAPC). To validate our implementation, we benchmarked key outputs (joinpoint locations, APC estimates, and AAPC) against the NCI Joinpoint Regression Program (version 5.0.2) using identical input datasets, yielding consistent results. Analysis code is available upon request. Additionally, we assessed whether segment-specific trends were statistically significant by testing whether the AAPC differed from zero (P < 0.05).
2.3. Age-period-cohort (APC) analysis
The age-period-cohort model reflects a disease's incidence or mortality time trend by age, period, and cohort. This study used the technique of age-period-cohort model analysis, which was thoroughly explained by Carstensen from the Lexis diagram [[9], [10], [11]]. We performed age-period-cohort model fitting using the intrinsic estimator (IE) method, which provides an estimable solution to the APC identification problem by applying the Moore-Penrose pseudoinverse to the design matrix—an approach mathematically equivalent to the IE as described by Yang et al. (2004, 2008) [12,13]. This method is widely used in cancer epidemiology and produces unique, estimable functions of the age, period, and cohort parameters. Analyses were implemented in Python using the statsmodels library.
3. Results
3.1. Descriptive analysis
From 1990 to 2023, there were decreases of 25.0% in prevalence, 53.8% in incidence, and 67.4% in -related deaths (Table 1, Table 2). Throughout the years 1990-2023, all gastric cancer-related measures demonstrated a predominantly declining trend in China, with notable variations across the timeline including a recent reversal in the most recent period (Fig. 1).
Table 1.
Prevalence, incidence, deaths, YLLs, YLDs, and DALYs in 1990 for Stomach cancer in China.
| Measure | Age-standardized rates per 100,000 people |
||
|---|---|---|---|
| Male | Female | Both | |
| Prevalence | 106.73 (79.90, 142.43) | 52.48 (42.79, 67.76) | 79.38 (63.11, 97.68) |
| Incidence | 72.74 (55.03, 97.43) | 36.92 (29.81, 47.88) | 54.23 (44.02, 67.34) |
| Deaths | 68.13 (51.67, 91.38) | 35.34 (28.23, 45.99) | 50.93 (41.71, 63.39) |
| YLDs | 17.11 (11.15, 24.43) | 8.52 (5.27, 11.72) | 12.71 (8.78, 17.36) |
| YLLs | 1724.09 (1319.10, 2272.10) | 890.16 (729.85, 1143.56) | 1301.98 (1059.52, 1606.36) |
| DALYs | 1741.20 (1330.18, 2291.76) | 898.68 (736.42, 1155.20) | 1314.69 (1070.83, 1621.11) |
Table 2.
Prevalence, incidence, deaths, YLLs, YLDs, and DALYs in 2023 for Stomach cancer in China.
| Measure | Age-standardized rates per 100,000 people |
||
|---|---|---|---|
| Male | Female | Both | |
| Prevalence | 91.93 (64.96, 136.70) | 28.26 (21.45, 37.11) | 59.55 (43.13, 83.85) |
| Incidence | 37.90 (26.44, 51.34) | 12.93 (10.03, 16.63) | 25.04 (18.93, 32.90) |
| Deaths | 24.61 (16.77, 32.76) | 9.24 (7.43, 11.81) | 16.62 (13.15, 20.84) |
| YLDs | 10.69 (6.61, 16.27) | 3.49 (2.27, 4.83) | 7.01 (4.52, 10.20) |
| YLLs | 574.31 (409.12, 780.02) | 199.32 (166.66, 249.20) | 383.13 (302.18, 485.04) |
| DALYs | 585.00 (415.52, 792.08) | 202.81 (169.55, 254.03) | 390.14 (307.72, 493.75) |
Fig. 1.
Temporal trends of gastric cancer health burden metrics in China, 1990-2023. A. Prevalence rate per 100,000 population. B. Incidence rate per 100,000 population. C. Mortality rate per 100,000 population. D. YLDs rate per 100,000 population. E. YLLs rate per 100,000 population. F. DALYs rate per 100,000 population.
3.2. Joinpoint regression analysis
We conducted a joinpoint regression analysis on the age-standardized incidence rate of gastric cancer from 1990 to 2023 (Fig. 2). There was a moderate declining trend from 1990 to 2003 (APC = –1.39), followed by a sharper decrease from 2003 to 2015 (APC = –4.23). The downward trend continued from 2015 to 2019 (APC = –3.08), and the overall trend showed a fluctuating downward trend (AAPC = –2.80). After 2019, rates showed a moderate increase, though male incidence remained consistently higher than female incidence (Table 3).
Fig. 2.
Joinpoint regression analysis of temporal trends in gastric cancer health burden in China, 1990-2023. The joinpoint regression model is used to identify significant changes in temporal trends. The Y-axis represents the age-standardized rate per 100,000 population, whereas the X-axis represents the years.
Table 3.
Joinpoint regression analysis: Trends in age-standardized incidence rates in China, 1990-2023.
| Gender | Period | APC (95% CI) | AAPC (95% CI) |
|---|---|---|---|
| Both sexes | 1990-2003 | −1.4 (-1.5, -1.3) | −2.8 (-3.0, -2.6) |
| 2003-2015 | −4.2 (-4.5, -3.9) | ||
| 2015-2019 | −3.1 (-5.3, -0.9) | ||
| 2019-2023 | 1.7 (-0.9, 4.4) | ||
| Males | 1990-2007 | −1.5 (-1.6, -1.4) | −2.3 (-2.5, -2.1) |
| 2007-2014 | −4.6 (-4.8, -4.3) | ||
| 2014-2019 | −1.8 (-3.5, -0.0) | ||
| 2019-2023 | 2.0 (-1.2, 5.2) | ||
| Females | 1990-2002 | −1.6 (-1.7, -1.5) | −3.8 (-4.1, -3.5) |
| 2002-2015 | −5.5 (-5.7, -5.3) | ||
| 2015-2020 | −5.0 (-6.6, -3.4) | ||
| 2020-2023 | 3.0 (2.0, 4.0) |
3.3. Effects of age, period, and cohort on the prevalence rate
We analyzed changes in the age-standardized prevalence rates (ASPRs) of gastric cancer in China from 1990 to 2023. As shown in Fig. 3A, prevalence generally increased with age from 30 to 34 to 65–69 years, with the steepest rise in the 60–64 and 65–69 groups. Fig. 3B shows that earlier-born cohorts had higher prevalence rates at the same age than later-born cohorts. Fig. 3C further demonstrates a consistent decline in prevalence across all age groups from 1990 to 2023.
Fig. 3.
Age-period-cohort (APC) model analysis of gastric cancer prevalence in China, 1990-2023. A. Age-specific changes in prevalence across different calendar periods. B. Prevalence changes by birth cohort across the life course. C. Period-specific prevalence trends stratified by age group. D. Diagram of age-period-cohort effect estimates.
We applied the APC model using the intrinsic estimator method to assess the independent effects of age, period, and cohort on gastric cancer prevalence trends (Table 4). The model showed excellent fit for both sexes, with R2 values of 0.9981 for males and 0.9984 for females. To improve interpretability, Table 4 presents summary point estimates, standard errors, and 95% confidence intervals for the age, period, and cohort effects, stratified by sex.
Table 4.
Summary point estimates, standard errors, and 95% confidence intervals from the age-period-cohort model for gastric cancer prevalence in China, 1990-2023.
| Sex | APC component | Point estimate | SE | 95% CI | Model fit (R2) |
|---|---|---|---|---|---|
| Males | Age effect | 0.371 | 0.784 | −1.165 to 1.907 | 0.9981 |
| Males | Period effect | 0.090 | 0.067 | −0.042 to 0.221 | |
| Males | Cohort effect | −0.054 | 0.108 | −0.265 to 0.157 | |
| Females | Age effect | 0.435 | 0.645 | −0.830 to 1.699 | 0.9984 |
| Females | Period effect | 0.084 | 0.190 | −0.289 to 0.457 | |
| Females | Cohort effect | 0.008 | 0.114 | −0.215 to 0.230 |
The age effect showed the largest variation among the three APC components, suggesting that age was the dominant contributor to differences in gastric cancer prevalence. In both males and females, the age effect increased with advancing age and peaked around older age groups, especially 65-69 years. Period effects were relatively smaller and generally suggested a downward temporal pattern across 1990-2023. Cohort effects indicated lower relative risk in more recent birth cohorts compared with earlier cohorts, supporting the presence of favorable generational changes in gastric cancer risk (Fig. 3D).
3.4. ARIMA projection analysis
Using the ARIMA framework, we projected the burden of gastric cancer in China from 2024 to 2033 (Supplementary Table 5). Projections revealed divergent sex-specific trends: mortality is projected to decrease by 51.3% in males but increase by 7.6% in females. The age-standardized DALY rate is projected to decline by 56.4%, and the incidence rate by 30.2% (Fig. 4). Model fit was superior for females, with the ARIMA(0,2,0) model for mortality achieving the lowest RMSE of 6.82 (Supplementary Table 4). It should be noted that the ARIMA (0,2,0) model represents a parsimonious baseline forecast, and confidence intervals may extend below zero for rates approaching the lower bound, reflecting inherent statistical uncertainty rather than biological feasibility. These projections should be interpreted as indicative trends rather than precise estimates.
Fig. 4.
ARIMA forecasting model for gastric cancer health burden in China, 2024-2033. The autoregressive integrated moving average (ARIMA) model is used to forecast future trends based on historical data from 1990 to 2023.
4. Discussion
4.1. Main findings summary & age-specific patterns
We observed a substantial decline in the gastric cancer burden from 1990 to 2023, with the age-standardized incidence rate falling by 53.8% and the mortality rate by 67.4%. These trends reflect improvements in primary prevention, early detection, and treatment.
The present study uses GBD 2023 data, which represent a substantive methodological and data advance over prior GBD iterations used in earlier gastric cancer burden analyses of China. Compared with GBD 2021, GBD 2023 extends the analytical time horizon by two additional years (2022–2023), incorporates updated vital registration and cancer registry data from China, and refines the Cause of Death Ensemble model (CODEm) with improved covariate selection and spatiotemporal smoothing. Relative to GBD 2019 and GBD 2017, the 2023 release additionally integrates COVID-19-era disruption data and applies revised disability weights calibrated through updated population surveys. These methodological refinements result in non-trivial revisions to point estimates and trend inflection points compared with earlier releases—most notably, the identification of a statistically significant female incidence reversal from 2020 to 2023 that was not captured in GBD 2021 analyses, given the absence of 2022–2023 data. Together, these advances justify the re-examination of gastric cancer burden in China using GBD 2023 and enable novel insights that extend beyond an incremental update.
Population aging is increasing the absolute number of cases, particularly among those aged 65 and above. This elevated burden is linked to delayed diagnosis from nonspecific symptoms, greater healthcare system strain, and increased palliative care needs [14]. Etiologies also differ by age: early-onset gastric cancer (<50 years) is often associated with hereditary syndromes and diffuse-type histology, while late-onset (≥60 years) is more frequently linked to chronic H. pylori infection and intestinal-type histology [15,16]. These distinct pathways contribute to the observed age-specific incidence patterns.
With progressive population aging in China, the proportion of elderly patients and late-onset is expected to increase. These demographic shifts and the increased absolute disease burden pose significant challenges in controlling gastric cancer effectively in the future.
4.2. Temporal trend analysis & sex differences
Joinpoint regression analysis revealed that age-standardized incidence rate in China had non-linear declining trends with multiple inflection points. The critical turning points were in 2002 (females), 2007 (males), and 2019-2020 (both sexes). Female incidence declined modestly before 2002 (–1.78% annually), then accelerated sharply to –5.49% per year until around 2015, before moderating to –3.03% and subsequently reversing to a significant increase of +3.01% annually from 2020 to 2023. However, GBD estimates for 2020–2023 are subject to greater uncertainty due to data lag and potential disruptions to cancer registration caused by the COVID-19 pandemic. The apparent trend reversal in females may partly reflect a data artifact, and cautious interpretation is warranted pending validation with independent surveillance data.
Subgroup analyses indicated that age-standardized incidence for males consistently exceeded that for females each year. The sex differences may be due to differences in smoking prevalence, occupational carcinogen exposure, dietary patterns favoring high-salt intake among males, hormonal protective factors in females, and other factors [17,18].
4.3. Risk factor analysis & phenotype interpretation
In 2023, the major predisposing factors for gastric cancer-related mortality included dietary risks, metabolic risks, and environmental and occupational risks. Recent studies have highlighted the association with metabolic syndrome, indicating that patients with both H. pylori infection and metabolic disorders might exhibit a different disease phenotype than those with H. pylori infection alone [19]. Specifically, associated with metabolic syndrome is frequently categorized as metabolic-driven phenotype, which is characterized by insulin resistance-mediated carcinogenesis [20].
Previous studies have shown that both current smoking and secondhand smoke exposure are independent risk factors for gastric cancer mortality, with tobacco use adversely affecting DNA methylation patterns, immune surveillance, and gastric mucosal integrity [21,22]. Furthermore, smoking is associated with increased all-cause and cancer-specific mortality in survivors [23]. Therefore, enhanced tobacco control interventions for high-risk populations aged 40-69 years in China is essential.
Occupational exposures-such as to asbestos, coal dust, silica, and industrial chemicals-also contribute to gastric cancer. An estimated 3–5% of gastric cancer deaths are linked to occupational risks, underscoring the need for stricter safety regulations and health surveillance [24].
4.4. External event impact analysis (COVID-19)
The following observations regarding COVID-19's impact on gastric cancer trends are necessarily speculative due to limited direct evidence and should be interpreted with caution. The GBD 2023 study provides, for the first time, comprehensive estimates of disease burden attributable to COVID-19 across 2020-2023. In 2021, COVID-19 became the leading cause of years of life lost globally, surpassing ischemic heart disease, stroke, and chronic respiratory diseases. COVID-19-related mortality was the 2nd leading cause of age-standardized death rate [25]. Notably, GBD 2021 did not analyze how COVID-19 influenced incidence and mortality rates of other diseases through direct causal relationship measurements. Instead, it reflected the pandemic's impact on other health outcomes via factors such as healthcare access disruption, delayed cancer screening and diagnosis, and treatment interruptions. In our study, joinpoint regression results and time series plots do not indicate a significant trend in mortality between 2019 and 2021. This observation may imply that the influence of COVID-19 on disease burden in Chinese population is relatively limited. This suggests that the lack of a significant impact on burden in China may be attributable to resilient healthcare system adaptation and prioritization of cancer care, particularly among patients requiring urgent intervention.
4.5. Overall progress summary & policy validation
In conclusion, research on prevention and control in China has made substantial and comprehensive progress in recent years, leading to dramatic reductions across all six burden indicators. National H. pylori screening and eradication campaigns have diminished infection-related gastric cancer risk. Improved endoscopic screening programs and advances in minimally invasive surgical techniques have improved early detection rates and treatment outcomes. Furthermore, rising socioeconomic development and improved food safety standards have fostered healthier dietary transitions and expanded access to fresh foods through refrigeration. These changes underscore the efficacy of multi-sectoral public health policies on chronic disease prevention.
4.6. Limitations & future directions
Our study has several limitations. First, the ecological study design using aggregate population-level data and reliance on Global Burden of Disease modeling estimates cannot establish causal relationships and are prone to ecological fallacy and estimation uncertainty. Second, this study did not examine subnational geographic heterogeneity or socioeconomic disparities in burden within China. Future research should address this gap to identify high-risk regions and vulnerable populations for targeted interventions. Third, longer follow-up periods beyond 2023 and validation studies comparing ARIMA projections with observed data are essential for assessing forecast accuracy and model reliability. Future studies could improve uncertainty quantification by incorporating ensemble forecasting methods or Bayesian approaches. Despite these limitations, our research offers valuable and updated insights into temporal trends in Chinese gastric cancer epidemiology and evidence-based targets for policy priority-setting.
4.7. Conclusion and policy recommendations
The incidence, mortality, and disability-adjusted life years of in China notably decreased from 1990 to 2023. Despite the overall declining trend, remains a significant concern, particularly among males and elderly populations aged 60 years and above, who face greater age-specific disease burden. The primary contributors to gastric cancer burden are dietary risks, with high sodium intake and H. pylori infection being a major factor. These findings highlight the importance of focusing on recent trend reversals, especially the statistically significant increase in female incidence since 2020, and tailoring prevention strategies to address the needs of different sex and age groups.
Author contributions
All authors contributed to the final approval of the paper. M.M.C. designed study. X.L. made significant contributions to manuscript writing. S.Q.C.and H.L.Y. analyzed data, M.M.C. revised the manuscript. All authors have read and approved the final manuscript
Ethical statement
This study used publicly available aggregated data from the Global Burden of Disease Study 2023 and did not involve direct human participants; therefore, ethical approval was not required.
Funding
This study was funded by Guiyang Second People's Hospital (Jinyang Hospital) Youth Talent Fund Program (No. QN[2024] 12) to M.M.C.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We sincerely thank the IHME Global Health Data Exchange (GHDx) for providing the publicly available Global Burden of Disease 2023 data that made this study possible.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.puhip.2026.100830.
Appendix A. Supplementary data
The following are the Supplementary data to this article.
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