Health Inequity of Stage and Survival of Gastric Cancer in California
by
, , , ,
Philip H. G. Ituarte
1,†,
Kevin Sullivan
1,†,
Marta M. Jankowska
2
,
Rebecca Nelson
3,
Robert Huang
4,
Matthew C. Hernandez
1,
Chi Wan Wong
5,
Supriya Deshpande
1,
I. Benjamin Paz
1,
Laleh Melstrom
1,
Edward S. Kim
6,
Yuman Fong
1,
Yanghee Woo
1,* and
on behalf of the Social Determinants of Health Research Working Group
1
Department of Surgery, City of Hope National Medical Center, Duarte, CA 91010, USA
2
Department of Population Sciences, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA 91010, USA
3
Department of Computational and Quantitative Medicine at the Beckman Research Institute, City of Hope National Medical Center, Duarte, CA 91010, USA
4
Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University, Stanford, CA 94305, USA
5
Department of AI and Data Science, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA 91010, USA
6
City of Hope Lennar Foundation Cancer Center, Irvine, CA 92618, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Cancers 2025, 17(22), 3596; https://doi.org/10.3390/cancers17223596
Submission received: 15 October 2025
/
Revised: 1 November 2025
/
Accepted: 4 November 2025
/
Published: 7 November 2025
(This article belongs to the Special Issue Clinical Epidemiology and Risk Prediction for Gastrointestinal Cancers)
Simple Summary
Gastric cancer (GC) disproportionately affects racial and ethnic minorities in the U.S. Although GC mortality has declined, disease burden remains unequally distributed, contributing to health inequities. In this largest ethnically enriched, population-based study of GC in the U.S., race, ethnicity, socioeconomic factors, and environmental factors jointly influenced stage at diagnosis and survival outcomes. Korean patients had the highest likelihood of early-stage diagnosis and the lowest GC-specific mortality, whereas Mexican, Filipino, and uninsured patients were more likely to present with advanced disease. Younger age (<45 years), male sex, and comorbidities such as GERD, smoking, and type II diabetes also were associated with worse outcomes and should be considered in identifying at-risk individuals. These findings support the development of race- and ethnicity-informed risk stratification tools and highlight the need for targeted early-detection strategies, including upper GI endoscopy, to reduce GC mortality and promote health equity in an increasingly diverse U.S. population.
Abstract
Background: Gastric cancer (GC) remains a significant health burden in the U.S, particularly among ethnic minorities. We identified patient-level risk factors contributing to advanced-stage (AS) diagnosis and poor survival to guide strategies to address GC-related health disparities. Methods: We conducted a retrospective cohort analysis of 18,396 histologically confirmed GC cases (4102 early-stage (ES) and 14,294 AS) diagnosed between 2000 and 2019, using data from the California Cancer Registry linked to the California Office of Statewide Health Planning and Development. Eligible cases were adults age ≥ 18 with complete diagnostic and follow-up data. Multivariable logistic and Cox regression models were used to identify predictors of AS-GC and five-year disease-specific (DSS) and overall-survival (OS) outcomes. Analyses were further stratified by Asian and Hispanic subgroups. Results: Korean heritage was the strongest predictor of ES-GC [OR 0.58 (95% CI, 0.47–0.71), p < 0.001] and was independently associated with the lowest GC-specific mortality risk [HR 0.73 (95% CI: 0.67–0.80), p < 0.0001]. The youngest age group (18–44 years) had the highest AS-GC rate (91.4%). Asian ethnicity, receipt of care at NCI-designated cancer centers, and prior upper endoscopy were associated with improved OS and DSS. In contrast, comorbidities such as GERD, diabetes, liver disease, smoking and alcohol abuse, and older age ≥ 75, U.S.-birth, and rural residence were linked to worse outcomes. Conclusions: Distinct demographic, clinical, and healthcare access factors contribute to disparities in GC outcomes. These findings support the development of culturally tailored early-detection programs, and risk-based screening for GC care, particularly in vulnerable populations.
1. Introduction
Gastric adenocarcinoma (GC) remains a major global health challenge and a leading cause of cancer-related mortality worldwide [1]. Although relatively uncommon in the U.S., GC imposes a disproportionate burden on racial and ethnic minority populations. Geographic and ethnic disparities in incidence, stage at diagnosis, and survival outcomes highlight significant inequities in early detection and access to care. For example, in countries like South Korea and Japan where there are high incidences of GC, national screening programs have led to early-stage (ES) diagnosis in over 60% of cases, resulting in markedly improved survival over the course of their successful implementation. In contrast, the majority of the GC cases in Western countries without screening programs, including the U.S., are detected at advanced stages (AS) and associated with significantly worse prognosis [1,2].
Importantly, the incidence and mortality of GC in the U.S. are unequally distributed by ethnicity. Korean, Japanese, and Hispanic Americans face significantly higher risks of developing GC compared to non-Hispanic Whites (NHWs) [3,4]. More specifically, Korean Americans exhibit the highest incidence, four to five times greater than that of NHWs as shown in prior analyses of the California Cancer Registry (CCR) data. While risk factors for GC such as Helicobacter pylori [5] or Epstein–Barr virus infections [6], age, male sex, tobacco use [7,8,9], dietary patterns low in fiber or high in salt [10,11,12,13,14,15,16], and obesity [17] are well-documented contributors to GC development, less is known about how these and other clinical and socioeconomic factors [18,19] influence stage at diagnosis and long-term survival in the U.S. [20,21].
Understanding how demographic, clinical, and socioeconomic factors influence GC presentation and outcomes is critical for identifying high-risk populations and guiding targeted early-detection strategies—especially in low-incidence countries where screening is not standard practice. To address this gap, we conducted a large, population-based study using linked data from the CCR and the California Office of Statewide Health Planning and Development (OSHPD). Our objective was to identify demographic, clinical, and healthcare access factors associated with AS diagnosis and poor survival in GC, with particular attention to racial and ethnic disparities in outcomes.
2. Methods
2.1. Patients
Gastric adenocarcinoma (GC) cases diagnosed between 2000 and 2019 were identified from the California Cancer Registry (CCR) using site and histology codes defined by the American Joint Committee on Cancer (AJCC) 6th, 7th, or 8th Edition. The inclusion criteria were histologically confirmed GC as the first or only primary malignancy, age ≥ 18 years, known AJCC stage at diagnosis, and complete follow-up data. Cases with in situ disease, missing tumor size, “zero tumor size”, or “no tumor found” were excluded. ES-GC was defined as AJCC stage I, and AS-GC as stages II–IV.
Each CCR case was linked at the patient level by patient identifier to hospital and ambulatory surgery discharge records from the OSHPD. The primary and secondary diagnoses and procedures codes were extracted in ICD-9-CM and ICD-10 formats. This study was approved by the Institutional Review Board (IRB #13479) and the California State Committee for the Protection of Human Subjects (#2019-225).
2.2. Variables and Definitions
Demographic, clinical, and treatment-related risk factors were extracted from variables available in CCR. Race/ethnicity was categorized into non-Hispanic White (NHW), non-Hispanic Black (NHB), Native American, Hispanic subgroups (Mexican, Cuban/Puerto Rican, Central/South American, other Hispanic), and disaggregated Asian/Pacific Islander subgroups (e.g., Korean, Japanese, Filipino, Chinese, Vietnamese, Cambodian/Thai, Laotian/Hmong, South Asian, Hawaiian/Samoan, other Asian). Socioeconomic status (SES) was measured at the census tract level based on a composite index of income, education, employment, and housing characteristics [22].
Additional variables included birthplace (U.S.-born, non-U.S.-born, unknown), tumor site, size, grade, and histology (intestinal, diffuse, or other epithelial type). Treatment variables included receipt of upper gastrointestinal endoscopy (UGIE), surgery at the primary site, and chemotherapy. Rural-Urban Commuting Area (RUCA) classification and whether there was treatment at a National Cancer Institute (NCI)-designated center were also studied.
Common co-existing medical conditions and comorbidities associated with GC risk were identified using ICD-9 and ICD-10 diagnosis codes (Table S1). These included H. pylori, peptic ulcer disease (PUD), gastroesophageal reflux disease (GERD), diabetes mellitus type II (DM-II), obesity, smoking, alcohol dependency, and presence of liver disease associated with cirrhosis. Measures of PUD and liver disease were based on ICD-9 or ICD-10 diagnosis codes associated with these subsets of chronic disease represented in the Deyo–Charlson comorbidity scale [23]. Metabolic syndrome was defined by a validated ICD-9-based algorithm and was included as a possible risk factor [24,25]. We translated ICD-9 codes into corresponding ICD-10 equivalent codes using a web-based crosswalk (Convert ICD-9-CM Codes to/from ICD-10-CM/PCS (accessed on 3 November 2025)). Comorbidity score was measured by applying the Deyo modification of the Charlson Comorbidity Index, excluding cancer-related subscales.
2.3. Statistics
For univariable analyses comparing ES-GC to AS-GC, Student’s t-test or chi-square were applied. Multivariable stepwise logistic regression with backward entry comparing ES-GC to AS-GC was performed. Stepwise entry was applied as there was no a priori assumption about the impact of potential risk factors on outcomes. For overall survival (OS) or disease-specific survival (DSS) (GC mortality) outcomes at five years, Cox proportional hazards analysis with backward stepwise entry was also applied. Residuals were examined to ensure that the proportional hazards assumption was met. The inclusion criterion in multivariable models was p < 0.20 for stepwise models, and p < 0.05 was used to identify significant predictors of AS-GC or survival time. All analyses were performed using Stata/MP version14.2 (Stata Corp, College Station, TX, USA).
3. Results
3.1. Univariable Analyses of Early Versus Advanced Stage Gastric Cancer
A total of 18,396 GC cases from 2000 to 2019 met our predetermined inclusion criteria. There were 22.3% ES-GC (stage I; n = 4102) and 77.7% AS-GC (stage II–IV; n = 14,294) cases. As expected, differences in patient, tumor, and treatment factors were observed between ES-GC and AS-GC (Table 1). We used raw percentages to compare ES-GC and AS-GC, and the highest percentage of AS-GC was observed in the youngest patient group (18–44; 91.4%), while the lowest was observed in patients aged 75 years and greater (68.2%). A greater percentage of men (78.2%) than women (77.0%) had AS-GC. For race/ethnicity, the highest percentage of AS-GC was observed in Mexicans (85.0%), followed by Filipinos (82.6%), Hawaiian or Samoans (84.9%), and Cambodian or Thai (82.4%). Low percentages of AS-GC were observed among Cuban or Puerto Ricans (70.8%), Other Pacific Islanders (70.0%), and Other Asian (67.6%), while the lowest percentage was observed in Koreans (59.8%). Very low (80.0%) or low SES (78.2%) were associated with a higher frequency of AS-GC compared with middle and higher SES. Uninsured (86.6%) and Medicaid (85.5%) cases were associated with a higher frequency of AS-GC compared with the lowest frequency for health insurance represented by Medicare (70.7%). Surprisingly, patients with a comorbidity score of zero had a higher percentage of AS-GC (79.4%) compared with those with a score of 2 or higher (71.9%). Patients treated at NCI cancer centers had a slightly lower frequency of AS-GC (76.1%) compared with those treated at non-NCI facilities (77.9%; p = 0.05). Poorly differentiated grade (85.6%), largest tumor size (>7 cm; 93.3%), stomach-overlapping sites (88.7%), and diffuse histopathology (84.0%) were significantly associated with AS-GC. Receipt of UGIE (69.2%) was associated with lower frequency of AS-GC compared to cases without UGIE (78.4%, p <0.001).
Frequency of ES-GC versus AS-GC did not differ significantly in patients with liver disease, alcohol abuse, or smoking compared to those without these medical conditions. Surprisingly, patients with chronic conditions like metabolic syndrome, peptic ulcer disease, H. pylori, GERD, diabetes type II, and obesity had lower frequency of AS-GC compared with those without these conditions (Table 1). Patients living in urban areas had a higher frequency of AS-GC (77.8%) compared with those living in relatively rural-large town areas (71.8%) or rural-isolated areas (75.9%).
3.2. Multivariable Analyses and Early- Versus Advanced-Stage Gastric Cancer
After stepwise procedure eliminated several variables, the resulting multivariable logistic regression model identified factors associated with AS-GC risk (Table 2). Patients aged 18–44 had a higher risk compared with the reference group (65–74-year-old), and women had a lower risk compared to men. Compared with NHW, only Koreans, Native Americans, South Asians, and Asian-other had significantly lower odds of AS-GC among all racial/ethnic groups. Middle SES, a comorbidity score of ≥2, antrum site, surgery, and large-rural town residence had lower odds of AS-GC. The area under the curve for this logistic regression model was 0.90, indicating a good fit between the data and the statistical model.
As expected, compared with well-differentiated tumor grade, moderately well-differentiated and poorly/undifferentiated grades were significantly associated with greater odds of AS-GC. Diffuse or intestinal pathology and chemotherapy had higher odds of AS-GC. Figure S1 shows a forest plot of odds ratios from this model.
3.3. Multivariable Overall Survival
Greater mortality risk included patients aged 75 or older (HR 1.25, 95% CI 1.19–1.30, p < 0.001), who had nearly 25% greater mortality risk than 65–74-year-olds. Being NHW (reference category) was presumed to be associated with relatively worse survival as all other race/ethnicity categories were under 1.0, except for Laotian or Hmong (HR 1.07; p = 0.549). U.S. birthplace was associated with a 9% higher risk (HR 1.09, 95% CI 1.04–1.13, p < 0.001), and NCI treatment was associated with a 10% lower mortality risk (HR 0.90, 95% CI 0.86–0.95, p < 0.001).
As expected, risk of death increased with higher stage and tumor grade. Antrum and lesser curvature of stomach were associated with lower risk, but overlapping sites were associated with higher risk (HR 1.08, 95% CI 1.04–1.21, p = 0.055). Surgery and chemotherapy treatments were associated with lower mortality risk. Positive LN status, liver disease, smoking, and diabetes were each associated with increased mortality risk. Surprisingly, obesity was associated with significantly lower mortality risk (HR 0.86, 95% CI 0.82–0.91, p < 0.001). Rural area (large rural town) was associated with greater mortality risk (Table 3). Figure S2 shows a forest plot summarizing the HRs from this model.
3.4. Multivariable Disease-Specific Survival
The multivariable Cox model for DSS (Table 3) indicated a greater mortality risk for patients aged 75 or older (HR 1.22, 95% CI 1.16–1.29, p < 0.001). Compared with NHW race/ethnicity, a lower risk was observed for the Mexican, Central/South American, Chinese, Japanese, Filipino, Vietnamese, Other Asian groups, while the Korean group (HR 0.73, 95% CI 0.67–0.80, p < 0.001) had the lowest mortality risk. Compared with private insurance, Medicare was associated with lower mortality risk, as was treatment at an NCI-designated center (HR 0.90, 95% CI 0.85–0.95, p < 0.001). Compared with non-U.S. births, patients born in the U.S. had an 8% greater mortality risk (HR 1.08, 95% CI 1.04–1.13, p < 0.001) (Table 3).
Overlapping sites within the stomach was associated with greater mortality risk (HR 1.12, 95% CI 1.03–1.22, p = 0.011), whereas lesser curvature of the stomach (HR 0.86, 95% CI 0.79–0.95, p < 0.002) was associated with lower risk compared with fundus, the reference group. Diffuse (HR 1.21, 95% CI 1.03–1.22, p < 0.001) but not intestinal histopathology was associated with greater mortality risk compared with other epithelial histology. Increasing levels of AJCC stage and tumor grade were associated with worse DSS. UGIE (HR 0.85, 95% CI 0.79–0.92, p < 0.001), surgery (HR 0.38, 95% CI 0.35–0.41, p < 0.001), and chemotherapy (HR 0.66, 95% CI 0.64–0.69, p < 0.001) again demonstrated lower risk. Positive lymph nodes (LNs) (HR 2.41, 95% CI 2.23–2.61, p < 0.001), liver disease, smoking, diabetes, and GERD were associated with greater DSS risk (Table 3). As with OS, obesity was associated with lower DSS risk (HR 0.86, 0.81–0.91, p < 0.001). Compared with urban areas, residence in rural-large towns was associated with greater risk (HR 1.21, 95% CI 1.07–1.36, p = 0.003). Figure S3 shows a forest plot summarizing HRs from this model.
4. Discussion
In this large, population-based cohort of over 18,000 GC patients, we found that more than three-quarters (77.7%) presented with AS disease (stage II–IV), consistent with known patterns of late GC diagnosis in the U.S. We identified multiple sociodemographic, clinical, and system-level factors independently associated with stage at presentation and survival, emphasizing persistent disparities and the multifactorial determinants of GC outcomes.
4.1. Disparities in Stage at Diagnosis
To our knowledge, this is the first population-based study of an ethnically enriched U.S. population to identify the impact of a comprehensive set of patient-specific factors on AS-GC diagnosis and five-year DSS and OS. AS-GC was more frequent among younger patients, men, and certain racial/ethnic minority groups including Mexicans, Filipinos, Hawaiians/Samoans, and Cambodians/Thais.
Socioeconomic disadvantages, public (e.g., Medicaid) or no insurance, and urban residence were also linked with higher odds of advanced disease. These findings highlight structural barriers that delay diagnosis, such as limited access to primary care, language and cultural obstacles, and delay in symptom recognition or endoscopic referrals.
Unexpectedly, patients without other medical conditions were more likely to present with advanced disease—suggesting that individuals with chronic conditions may have more frequent interactions with the healthcare system that lead to increased opportunities for detection. This is further supported by our finding that the receipt of UGIE was strongly associated with ES diagnosis, reinforcing its value in early detection. Notably, younger patients (18–44 years), a group typically perceived as low-risk, had the highest risk of AS-GC—likely due to both delayed presentation and low clinical suspicion. Previously, we had shown that young Hispanic men were most likely to be diagnosed with stage-IV GC with peritoneal disease [26]. As GC often remains asymptomatic in ES and younger patients are less likely to be concerned about GC, patients may present after many months of GC-associated symptoms. Moreover, lack of GC awareness may contribute to symptoms being overlooked or misdiagnosed within the medical communities, further delaying GC diagnosis. Potentially reflecting good baseline performance status and ability to tolerate more aggressive treatments, no difference was seen in this age group with respect to OS, compared to the reference age group (65–74 years).
We also found striking differences by ethnicity; NHW, NHB, and Hmong patients were at highest risk for AS-GC and had the poorest survival. Conversely, Korean patients were more likely to be diagnosed with ES-GC and had significantly better DSS and OS. This likely reflects a combination of cultural awareness, provider vigilance, and healthcare practices imported from South Korea, where national screening has improved early detection and outcomes [27]. In Korean American communities, especially in Los Angeles, endoscopic screening may be more frequently recommended and accepted, despite the absence of a formal U.S. screening program [28,29].
Interestingly, Central/South American patients had better DSS and OS than NHW. These findings are not readily explained by earlier stage of diagnosis, as multivariable analysis demonstrated no significant difference in ES-GC versus AS-GC diagnosis for these patients. A plausible explanation includes the so-called “Hispanic paradox,” referring to lower mortality risk for Hispanics compared to NHW or NHB patients for several health conditions [30]. However, studies are mixed with respect to this effect, with some showing improved mortality in Hispanic cancer patients [31,32,33,34] but not others [35,36]. Ultimately, the cause of improved OS or DSS in subgroups of Hispanic patients cannot be determined by this retrospective population-based registry study.
Surprisingly, the presence of comorbidities (excluding smoking and alcohol use) was associated with ES-GC, and patients with a history of H. pylori infection had improved survival. This likely reflects greater healthcare engagement and opportunities for symptom evaluation and endoscopy. While we could not directly assess endoscopy rates by subgroup, our findings support the hypothesis that healthcare access and utilization influence stage at diagnosis.
4.2. Pathologic and Treatment Predictors of Stage and Survival
As expected, poorly differentiated tumors, larger tumor size, diffuse histology, and non-antral location were strongly associated with AS disease. On multivariable survival analyses, increasing age, U.S. birthplace, higher tumor grade, diffuse histology, and nodal positivity were associated with worse OS and DSS. Conversely, treatment at NCI-designated centers, surgical resection, and chemotherapy were associated with significantly improved outcomes, underscoring the importance of guideline-based, multidisciplinary management.
The finding that U.S.-born patients had higher mortality compared with foreign-born individuals parallels prior studies, possibly reflecting differences in tumor biology, diet, H. pylori exposure, or health behaviors. Additionally, racial/ethnic groups such as Koreans, Central/South Americans, and Filipinos had lower GC mortality than NHW, even after adjustment, pointing to the influence of cultural, environmental, or genetic factors that warrant further exploration [37].
Interestingly, obesity was associated with lower GC mortality risk, which may reflect a form of the “obesity paradox” observed in other cancers, potentially due to better nutritional reserves, different tumor biology, or reverse causality where weight loss is an early symptom of aggressive disease.
4.3. Limitations and Implications
Our findings should be interpreted in the context of several limitations. While this study leveraged comprehensive registry data, limitations include incomplete capture of comorbidities, lifestyle factors, and treatment details. Also, socioeconomic and geographic measures were assigned based on census and residential data and may not fully reflect individual-level access barriers.
Despite these constraints, the findings highlight actionable disparities in GC diagnosis and survival. Expanding access to diagnostic endoscopy, increasing awareness, and implementing culturally tailored outreach strategies could improve outcomes, especially in ethnic subgroups with high burden and limited access to care.
5. Conclusions
In conclusion, this study demonstrates a multifactorial relationship among race, ethnicity, and socioeconomic/environmental factors. Identifying AS-GC risk factors can inform risk stratification for endoscopic screening efforts and community outreach in targeted high-risk populations to increase GC awareness. Our study suggests that younger age (<45 years), male sex, and comorbidities such as GERD, smoking history, and DM-II should be considered in identifying at-risk patients. Future work to disentangle the complex interplay among these factors can be aimed at evaluating the impact of other social determinants of health on survival and ethnicity in GC.
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/cancers17223596/s1, Figure S1: Forest plot of logistic regression of ES-GC versus AS-GC; Figure S2: Forest plot of Cox proportional hazard model for 5-year overall survival; Figure S3: Forest plot of Cox proportional hazard model for 5-year disease-specific survival; Table S1: Common co-existing medical conditions and those associated with GC risk captured using ICD-9 and ICD-10 diagnosis codes.
Author Contributions
Conceptualization: Y.W., P.H.G.I. and Social Determinants of Health Research Working Group; methodology: P.H.G.I., M.M.J., R.N., R.H. and C.W.W.; formal analysis: P.H.G.I. and R.N.; investigation: Y.W., P.H.G.I., K.S. and M.C.H.; resources: Y.W., I.B.P., L.M., E.S.K. and Y.F.; writing—original draft: P.H.G.I., K.S. and S.D.; writing: review and editing: P.H.G.I., K.S., S.D., Y.W., R.H., R.N., M.M.J., M.C.H., C.W.W., S.D., I.B.P., L.M., E.S.K. and Y.F.; visualization: Y.W. and S.D.; supervision: Y.W.; funding acquisition: Y.W., E.S.K. and Y.F. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the KURE IT CANCER RESEARCH Foundation Grant & AACR, SU2C GC Interception Award, and Capital Group Donation.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki, and approved by the City of Hope Institutional Review Board (protocol code 13479 and approval date 18 December 2017).
Informed Consent Statement
The patient consent form was waived due to the retrospective nature of this study.
Data Availability Statement
Y.W. had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.
Acknowledgments
We thank Joo Ha Hwang (Stanford CARE), Ly Nguyen (Stanford CARE), Howard Koh (Harvard University), and Aki Smith (Hope for Stomach Cancer) for their helpful contributions. Loretta Erhunmwunsee, Department of Surgery, City of Hope National Medical Center, Duarte, CA. LorettaE@coh.org; Rosemary Senguttuvan, Department of Surgery, City of Hope National Medical Center, Duarte, CA. rsenguttuvan@coh.org; Narissa Nonzee; Department of Population Science, Beckman Research Institute, City of Hope, Duarte, CA. nnonzee@coh.org; Yongzhe Wang; Department of Surgery, City of Hope National Medical Center, Duarte, CA. yonwang@coh.org; Mihae Song, Department of Surgery, City of Hope National Medical Center, Duarte, CA. misong@coh.org; Veronica Jones, Department of Surgery, City of Hope National Medical Center, Duarte, CA. vjones@coh.org; Lisa Yee, Department of Surgery, City of Hope National Medical Center, Duarte, CA. lyee@coh.org; Rebecca Nelson, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA. Rnelson@coh.org; Philip Ituarte, Department of Surgery, City of Hope National Medical Center, Duarte, CA. pituarte@coh.org; Yanghee Woo, City of Hope National Medical Center, Duarte, CA. yhwoo@coh.org.
Conflicts of Interest
Y.W. is a member of the scientific advisory board of Imugene LTD and J&J Ethicon. Y.F. is a paid scientific consultant for Medtronic, Johnson & Johnson, and Imugene, and receives royalties for inventions from Merck and Imugene. The other authors declare no conflicts of interest.
References
- Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef]
- Bray, F.; Laversanne, M.; Sung, H.; Ferlay, J.; Siegel, R.L.; Soerjomataram, I.; Jemal, A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer J. Clin. 2024, 74, 229–263. [Google Scholar] [CrossRef]
- Lee, E.; Liu, L.; Zhang, J.; Stern, M.C.; Barzi, A.; Hwang, A.; Kim, A.E.; Hamilton, A.S.; Wu, A.H.; Deapen, D. Stomach cancer disparity among Korean Americans by tumor characteristics: Comparison with non-Hispanic whites, Japanese Americans, South Koreans, and Japanese. Cancer Epidemiol. Biomark. Prev. 2017, 26, 587–596. [Google Scholar] [CrossRef]
- Huang, R.J.; Hwang, J.H. Improving the early diagnosis of gastric cancer. Gastrointest. Endosc. Clin. 2021, 31, 503–517. [Google Scholar] [CrossRef] [PubMed]
- Rawla, P.; Barsouk, A. Epidemiology of gastric cancer: Global trends, risk factors and prevention. Gastroenterol. Rev./Przegląd Gastroenterol. 2019, 14, 26–38. [Google Scholar] [CrossRef]
- Camargo, M.C.; Kim, W.H.; Chiaravalli, A.M.; Kim, K.M.; Corvalan, A.H.; Matsuo, K.; Yu, J.; Sung, J.J.; Herrera-Goepfert, R.; Meneses-Gonzalez, F.; et al. Improved survival of gastric cancer with tumour Epstein-Barr virus positivity: An international pooled analysis. Gut 2014, 63, 236–243. [Google Scholar] [CrossRef]
- González, C.A.; Pera, G.; Agudo, A.; Palli, D.; Krogh, V.; Vineis, P.; Tumino, R.; Panico, S.; Berglund, G.; Simán, H.; et al. Smoking and the risk of gastric cancer in the European Prospective Investigation Into Cancer and Nutrition (EPIC). Int. J. Cancer 2003, 107, 629–634. [Google Scholar] [CrossRef]
- Ladeiras-Lopes, R.; Pereira, A.K.; Nogueira, A.; Pinheiro-Torres, T.; Pinto, I.; Santos-Pereira, R.; Lunet, N. Smoking and gastric cancer: Systematic review and meta-analysis of cohort studies. Cancer Causes Control 2008, 19, 689–701. [Google Scholar] [CrossRef]
- Smyth, E.C.; Capanu, M.; Janjigian, Y.Y.; Kelsen, D.K.; Coit, D.; Strong, V.E.; Shah, M.A. Tobacco use is associated with increased recurrence and death from gastric cancer. Ann. Surg. Oncol. 2012, 19, 2088–2094. [Google Scholar] [CrossRef] [PubMed]
- Maddineni, G.; Xie, J.J.; Brahmbhatt, B.; Mutha, P. Diet and carcinogenesis of gastric cancer. Curr. Opin. Gastroenterol. 2022, 38, 588–591. [Google Scholar] [CrossRef] [PubMed]
- Larsson, S.C.; Bergkvist, L.; Wolk, A. Fruit and vegetable consumption and incidence of gastric cancer: A prospective study. Cancer Epidemiol. Biomark. Prev. 2006, 15, 1998–2001. [Google Scholar] [CrossRef]
- Shikata, K.; Kiyohara, Y.; Kubo, M.; Yonemoto, K.; Ninomiya, T.; Shirota, T.; Tanizaki, Y.; Doi, Y.; Tanaka, K.; Oishi, Y.; et al. A prospective study of dietary salt intake and gastric cancer incidence in a defined Japanese population: The Hisayama study. Int. J. Cancer 2006, 119, 196–201. [Google Scholar] [CrossRef] [PubMed]
- Tsugane, S.; Sasazuki, S. Diet and the risk of gastric cancer: Review of epidemiological evidence. Gastric Cancer 2007, 10, 75–83. [Google Scholar] [CrossRef]
- D’Elia, L.; Rossi, G.; Ippolito, R.; Cappuccio, F.P.; Strazzullo, P. Habitual salt intake and risk of gastric cancer: A meta-analysis of prospective studies. Clin. Nutr. 2012, 31, 489–498. [Google Scholar] [CrossRef]
- Zhang, Z.; Xu, G.; Ma, M.; Yang, J.; Liu, X. Dietary fiber intake reduces risk for gastric cancer: A meta-analysis. Gastroenterology 2013, 145, 113–120.e3. [Google Scholar] [CrossRef]
- Bertuccio, P.; Rosato, V.; Andreano, A.; Ferraroni, M.; Decarli, A.; Edefonti, V.; La Vecchia, C. Dietary patterns and gastric cancer risk: A systematic review and meta-analysis. Ann. Oncol. 2013, 24, 1450–1458. [Google Scholar] [CrossRef]
- Larsson, S.C.; Spyrou, N.; Mantzoros, C.S. Body fatness associations with cancer: Evidence from recent epidemiological studies and future directions. Metabolism 2022, 137, 155326. [Google Scholar] [CrossRef]
- Nagel, G.; Linseisen, J.; Boshuizen, H.C.; Pera, G.; Del Giudice, G.; Westert, G.P.; Bueno-de-Mesquita, H.B.; Allen, N.E.; Key, T.J.; Numans, M.E.; et al. Socioeconomic position and the risk of gastric and oesophageal cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC-EURGAST). Int. J. Epidemiol. 2007, 36, 66–76. [Google Scholar] [CrossRef] [PubMed]
- Uthman, O.A.; Jadidi, E.; Moradi, T. Socioeconomic position and incidence of gastric cancer: A systematic review and meta-analysis. J. Epidemiol. Community Health 2013, 67, 854–860. [Google Scholar] [CrossRef] [PubMed]
- Huang, R.J.; Koh, H.; Hwang, J.H.; Abnet, C.C.; Alarid-Escudero, F.; Amieva, M.R.; Bruce, M.G.; Camargo, M.C.; Chan, A.T.; Choi, I.J.; et al. A Summary of the 2020 gastric cancer summit at Stanford University. Gastroenterology 2020, 159, 1221–1226. [Google Scholar] [CrossRef]
- Huang, R.J.; Epplein, M.; Hamashima, C.; Choi, I.J.; Lee, E.; Deapen, D.; Woo, Y.; Tran, T.; Shah, S.C.; Inadomi, J.M.; et al. An approach to the primary and secondary prevention of gastric cancer in the United States. Clin. Gastroenterol. Hepatol. 2022, 20, 2218–2228.e2. [Google Scholar] [CrossRef]
- Yost, K.; Perkins, C.; Cohen, R.; Morris, C.; Wright, W. Socioeconomic status and breast cancer incidence in California for different race/ethnic groups. Cancer Causes Control 2001, 12, 703–711. [Google Scholar] [CrossRef]
- Deyo, R.A.; Cherkin, D.C.; Ciol, M.A. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J. Clin. Epidemiol. 1992, 45, 613–619. [Google Scholar] [CrossRef] [PubMed]
- Akinyemiju, T.; Sakhuja, S.; Vin-Raviv, N. In-hospital mortality and post-surgical complications among cancer patients with metabolic syndrome. Obes. Surg. 2018, 28, 683–692. [Google Scholar] [CrossRef]
- McHenry, S.; Zong, X.; Shi, M.; Fritz, C.D.; Pedersen, K.S.; Peterson, L.R.; Lee, J.K.; Fields, R.C.; Davidson, N.O.; Cao, Y. Risk of nonalcoholic fatty liver disease and associations with gastrointestinal cancers. Hepatol. Commun. 2022, 6, 3299–3310. [Google Scholar] [CrossRef] [PubMed]
- Khan, A.; Mustafa, R.; Laleh, M.; Li, H.; Yate-Ching, Y.; Lai, L.; Paz, B.; Ajay, G.; Yuman, F.; Yanghee, W. Disparate and alarming impact of gastrointestinal cancers in young adult patients. Ann. Surg. Oncol. 2021, 28, 785–796. [Google Scholar] [CrossRef] [PubMed]
- Jun, J.K.; Choi, K.S.; Lee, H.Y.; Suh, M.; Park, B.; Song, S.H.; Jung, K.W.; Lee, C.W.; Choi, I.J.; Park, E.C.; et al. Effectiveness of the Korean National Cancer Screening Program in reducing gastric cancer mortality. Gastroenterology 2017, 152, 1319–1328.e7. [Google Scholar] [CrossRef]
- Won, C.W.; Hwang, H.S. US geriatricians and gastric cancer screening in elderly Korean-American men: Differences from korean geriatricians. J. Am. Geriatr. Soc. 2012, 60, 996–997. [Google Scholar] [CrossRef]
- Kwon, H.T.; Ma, G.X.; Gold, R.S.; Atkinson, N.L.; Wang, M.Q. Primary care physicians’ cancer screening recommendation practices and perceptions of cancer risk of Asian Americans. Asian Pac. J. Cancer Prev. APJCP 2013, 14, 1999–2004. [Google Scholar] [CrossRef]
- Ruiz, J.M.; Steffen, P.; Smith, T.B. Hispanic mortality paradox: A systematic review and meta-analysis of the longitudinal literature. Am. J. Public Health 2013, 103, e52–e60. [Google Scholar] [CrossRef]
- Kumar, R.; Castillero, F.; Bhandari, S.; Malapati, S.; Kloecker, G. The Hispanic paradox in non-small cell lung cancer. Hematol./Oncol. Stem Cell Therapy 2022, 15, 21–29. [Google Scholar] [CrossRef]
- Paulozzi, L.J.; McDonald, J.A.; Sroka, C.J. A disparity beneath a paradox: Cancer mortality among young hispanic americans in the US-Mexico border region. J. Racial Ethn. Health Disparities 2021, 8, 1556–1562. [Google Scholar] [CrossRef]
- Philips, B.U., Jr.; Belasco, E.; Markides, K.S.; Gong, G. Socioeconomic deprivation as a determinant of cancer mortality and the Hispanic paradox in Texas, USA. Int. J. Equity Health 2013, 12, 26. [Google Scholar] [CrossRef]
- Escobar, K.M.; Sivaram, M.; Gorey, K.M.; Luginaah, I.N.; Kanjeekal, S.M.; Wright, F.C. Colon cancer care of Hispanic people in California: Paradoxical barrio protections seem greatest among vulnerable populations. J. Public Health Res. 2020, 9, 1696. [Google Scholar] [CrossRef]
- Pinheiro, P.S.; Callahan, K.E.; Gomez, S.L.; Marcos-Gragera, R.; Cobb, T.R.; Roca-Barcelo, A.; Ramirez, A.G. High cancer mortality for US-born Latinos: Evidence from California and Texas. BMC Cancer 2017, 17, 478. [Google Scholar] [CrossRef] [PubMed]
- Malagon-Blackwell, E.M.; Seagle, B.L.L.; Nieves-Neira, W.; Shahabi, S. The Hispanic paradox in endometrial cancer: A National Cancer Database study. Gynecol. Oncol. 2017, 146, 351–358. [Google Scholar] [CrossRef] [PubMed]
- Kang, S.U.; Nam, S.J.; Kwon, O.B.; Yim, I.; Kim, T.H.; Yeo, N.Y.; Lim, M.N.; Kim, W.J.; Park, S.W. Predictive mortality and gastric cancer risk using clinical and socio-economic data: A nationwide multicenter cohort study. Cancers 2025, 17, 30. [Google Scholar] [CrossRef] [PubMed]
Table 1.
Frequencies for variables by early- versus advanced-stage gastric cancer (n = 18,358).
| ESG | ASG | ||||
|---|---|---|---|---|---|
| Stage I | Stage II–IV | Total | |||
| Variable | Category | n = 4102 (%) | n = 14,294 (%) | n = 18,396 | p |
| Age, median (IQR) | 72.5 (62–81) | 65.0 (53–75) | 67.0 (55–77) | <0.001 | |
| Age Group | 18–44 | 156 (8.6) | 1662 (91.4) | 1818 | <0.001 |
| 45–54 | 406 (15.3) | 2244 (84.7) | 2650 | ||
| 55–64 | 668 (17.8) | 3085 (82.2) | 3753 | ||
| 65–74 | 1090 (24.0) | 3460 (76.0) | 4550 | ||
| 75+ | 1781 (31.7) | 3841 (68.2) | 5622 | ||
| Sex | Male | 2206 (21.8) | 7934 (78.2) | 10,140 | 0.05 |
| Female | 1896 (23.0) | 6360 (77.0) | 8256 | ||
| Race | non-Hispanic White | 1042 (21.8) | 3713 (78.2) | 4755 | <0.001 |
| non-Hispanic Black | 284 (22.0) | 1009 (78.0) | 1293 | ||
| Mexican | 414 (15.0) | 2340 (85.0) | 2754 | ||
| Cuban or Puerto Rican | 14 (29.2) | 34 (70.8) | 48 | ||
| Central/South American | 180 (17.3) | 863 (82.7) | 1043 | ||
| Hispanic other | 701 (21.0) | 2642 (79.0) | 3343 | ||
| Native American | 20 (27.4) | 53 (72.6) | 73 | ||
| Chinese | 334 (25.1) | 999 (74.9) | 1333 | ||
| Japanese | 152 (28.4) | 384 (71.6) | 536 | ||
| Filipino | 89 (18.4) | 394 (82.6) | 483 | ||
| Hawaiian or Samoan | 11 (15.1) | 62 (84.9) | 73 | ||
| Korean | 463 (40.2) | 688 (59.8) | 1151 | ||
| Vietnamese | 146 (22.7) | 497 (77.3) | 643 | ||
| Laotian or Hmong | 21 (22.8) | 71 (77.2) | 92 | ||
| Cambodian or Thai | 13 (17.6) | 61 (82.4) | 74 | ||
| South Asian | 32 (25.2) | 95 (74.8) | 127 | ||
| Other Pacific Islander | 21 (30.0) | 49 (70.0) | 70 | ||
| Other Asian | 147 (32.5) | 306 (67.6) | 453 | ||
| Other race/ethnicity | 18 (34.6) | 34 (65.4) | 52 | ||
| Marital status | Not married | 1740 (22.9) | 5867 (77.1) | 7607 | 0.115 |
| Married | 2362 (21.9) | 8427 (78.1) | 10,789 | ||
| Socioeconomic status | Very low | 690 (20.0) | 2763 (80.0) | 3453 | 0.003 |
| Low | 739 (21.8) | 2648 (78.2) | 3387 | ||
| Middle | 694 (22.9) | 2341 (77.1) | 3035 | ||
| High | 634 (22.3) | 2215 (77.7) | 2849 | ||
| Very high | 600 (23.6) | 1944 (76.4) | 2544 | ||
| Missing | 745 (23.8) | 2383 (76.2) | 3128 | ||
| Insurance | Private/PPO/HMO | 1313 (19.8) | 5317 (80.2) | 6630 | <0.001 |
| Medicare | 2042 (14.5) | 4920 (70.7) | 6962 | ||
| Medicaid | 424 (14.5) | 2497 (85.5) | 2921 | ||
| Uninsured | 118 (13.4) | 759 (86.6) | 877 | ||
| Other | 127 (20.1) | 506 (79.9) | 633 | ||
| Missing | 78 (20.9) | 295 (79.1) | 373 | ||
| Birthplace | Not USA | 1586 (21.9) | 5653 (78.1) | 7239 | 0.303 |
| USA | 756 (21.9) | 2701 (78.1) | 3457 | ||
| Unknown | 1760 (22.9) | 5940 (77.1) | 7700 | ||
| Comorbidity score | Zero | 2020 (20.6) | 7787 (79.4) | 9807 | <0.001 |
| One | 1183 (21.9) | 4207 (78.1) | 5390 | ||
| Two or more | 899 (28.1) | 2300 (71.9) | 3199 | ||
| NCI cancer center | No | 3582 (22.1) | 12,642 (77.9) | 16,224 | 0.05 |
| Yes | 520 (23.9) | 1652 (76.1) | 2172 | ||
| Tumor grade | Well-differentiated | 164 (64.6) | 90 (35.4) | 254 | <0.001 |
| Moderately well | 365 (28.1) | 933 (71.9) | 1298 | ||
| Poorly/Undifferentiated | 700 (14.4) | 4165 (85.6) | 4865 | ||
| Missing | 2873 (24.0) | 9106 (76.0) | 11,979 | ||
| Tumor size (cm) | >0–2 | 1325 (65.6) | 695 (34.4) | 2020 | <0.001 |
| >2–3 | 592 (37.9) | 968 (62.1) | 1560 | ||
| >3–5 | 605 (19.3) | 2535 (80.7) | 3140 | ||
| >5–7 | 280 (13.8) | 1744 (86.2) | 2024 | ||
| >7 | 156 (6.7) | 2169 (93.3) | 2325 | ||
| Unknown | 1144 (15.7) | 6145 (84.3) | 7289 | ||
| Tumor site | Fundus | 194 (20.6) | 749 (79.4) | 943 | <0.001 |
| Body | 603 (22.9) | 2025 (77.1) | 2628 | ||
| Antrum | 1556 (28.2) | 3959 (71.8) | 5515 | ||
| Pylorus | 196 (24.5) | 604 (75.5) | 800 | ||
| Lesser curvature | 598 (27.3) | 1592 (72.7) | 2190 | ||
| Greater curvature | 247 (24.2) | 773 (75.8) | 1020 | ||
| Overlapping sites | 257 (11.3) | 2018 (88.7) | 2275 | ||
| Unspecified | 451 (14.9) | 2574 (85.1) | 3025 | ||
| Histopathology | Diffuse | 1025 (16.0) | 5381 (84.0) | 6406 | <0.001 |
| Intestinal | 2682 (25.3) | 7913 (74.7) | 10,595 | ||
| Other | 385 (28.4) | 973 (71.6) | 1358 | ||
| Upper GI endoscopy | No | 3680 (21.6) | 13,348 (78.4) | 17,028 | <0.001 |
| Yes | 422 (30.8) | 946 (69.2) | 1368 | ||
| Surgery | No surgery | 1034 (11.9) | 7635 (88.1) | 8669 | <0.001 |
| Surgery | 3067 (31.5) | 6657 (68.5) | 9724 | ||
| Chemotherapy | No | 3252 (36.7) | 5603 (63.3) | 8855 | <0.001 |
| Yes | 796 (8.7) | 8366 (91.3) | 9162 | ||
| Unknown | 54 (14.3) | 325 (85.7) | 379 | ||
| Positive lymph nodes | Negative | 2437 (67.2) | 1188 (32.8) | 3625 | <0.001 |
| Positive | 235 (4.1) | 5451 (95.9) | 5686 | ||
| Unknown | 1430 (15.7) | 7655 (84.3) | 9085 | ||
| Liver disease | None | 4049 (22.3) | 14,138 (77.7) | 18,187 | 0.36 |
| Mild | 31 (27.9) | 80 (72.1) | 111 | ||
| Severe | 22 (22.5) | 76 (77.5) | 98 | ||
| Alcohol abuse | No | 3996 (22.3) | 13,941 (77.7) | 17,937 | 0.678 |
| Yes | 106 (23.1) | 353 (76.9) | 459 | ||
| Smoking | No | 3732 (22.4) | 12,933 (77.6) | 16,665 | 0.332 |
| Yes | 370 (21.4) | 1361 (78.6) | 1731 | ||
| Metabolic syndrome | No | 3440 (21.3) | 12,686 (78.7) | 16126 | <0.001 |
| Yes | 662 (29.2) | 1608 (70.8) | 2270 | ||
| Peptic ulcer disease | No | 3651 (21.8) | 13,093 (78.2) | 16,744 | <0.001 |
| Yes | 451 (27.3) | 1201 (72.7) | 1652 | ||
| H. pylori | No | 3805 (22.0) | 13,470 (78.0) | 17,275 | <0.001 |
| Yes | 297 (26.5) | 824 (73.5) | 1121 | ||
| GERD | No | 2971 (21.6) | 10,777 (78.4) | 13,748 | <0.001 |
| Yes | 1131 (24.3) | 3517 (75.7) | 4648 | ||
| DM II | No | 3082 (20.8) | 11,727 (79.2) | 14,809 | <0.001 |
| Yes | 1020 (28.4) | 2567 (71.6) | 3587 | ||
| Obesity | No | 3595 (22.0) | 12,774 (78.9) | 16,369 | 0.002 |
| Yes | 507 (25.0) | 1520 (75.0) | 2027 | ||
| RUCA | Urban | 3981 (22.2) | 13,924 (77.8) | 17,905 | 0.047 |
| Large rural area | 82 (28.2) | 209 (71.8) | 291 | ||
| Small/isolated rural | 39 (24.1) | 123 (75.9) | 162 |
Table 2.
Multivariable logistic regression for early- versus advanced-stage cancer (n = 18,396).
| Variables | Categories | OR (95% CI) | p-Value |
|---|---|---|---|
| Age group | 18–44 | 2.39 (1.92–2.97) | <0.001 |
| 45–54 | 1.23 (1.05–1.45) | 0.013 | |
| 55–64 | 1.24 (1.07–1.44) | 0.004 | |
| 65–74 (reference) | |||
| 75+ | 0.73 (0.64–0.83) | <0.001 | |
| Sex | Male (reference) | ||
| Female | 0.9 (0.81–0.99) | 0.026 | |
| Race | non-Hispanic White (reference) | ||
| non-Hispanic Black | 0.96 (0.78–1.18) | 0.691 | |
| Mexican | 1.01 (0.85–1.20) | 0.893 | |
| Cuban or Puerto Rican | 0.91 (0.37–2.20) | 0.832 | |
| Central or South American | 0.92 (0.73–1.16) | 0.500 | |
| Hispanic other | 0.88 (0.75–1.02) | 0.083 | |
| Native American | 0.44 (0.22–0.87) | 0.018 | |
| Chinese | 0.94 (0.76–1.15) | 0.522 | |
| Japanese | 0.93 (0.69–1.25) | 0.628 | |
| Filipino | 1.08 (0.78–1.50) | 0.638 | |
| Hawaiian or Samoan | 1.57 (0.70–3.52) | 0.272 | |
| Korean | 0.58 (0.47–0.71) | <0.001 | |
| Vietnamese | 0.90 (0.68–1.18) | 0.431 | |
| Laotian or Hmong | 0.74 (0.42–1.29) | 0.281 | |
| Cambodian or Thai | 1.64 (0.72–3.72) | 0.235 | |
| South Asian | 0.48 (0.29–0.80) | 0.005 | |
| Other Pacific Islander | 0.53 (0.25–1.13) | 0.099 | |
| Other Asian | 0.7 (0.52–0.94) | 0.018 | |
| Other race/ethnicity | 0.37 (0.19–0.72) | 0.003 | |
| Comorbidity | None (reference) | ||
| One | 1.00 (0.89–1.12) | 0.970 | |
| Two or more | 0.82 (0.71–0.94) | 0.004 | |
| Socioeconomic status | Very low (reference) | ||
| Low | 1.02 (0.87–1.19) | 0.841 | |
| Middle | 0.85 (0.73–1.00) | 0.056 | |
| High | 1.05 (0.88–1.24) | 0.608 | |
| Very high | 0.97 (0.81–1.16) | 0.747 | |
| Missing | 0.99 (0.84–1.18) | 0.949 | |
| NCI cancer center | No (reference) | ||
| Yes | 0.89 (0.76–1.03) | 0.116 | |
| Tumor grade | Well-differentiated (reference) | ||
| Moderately well | 1.98 (1.26–3.13) | 0.003 | |
| Poorly/Undifferentiated | 2.37 (1.54–3.66) | <0.001 | |
| Unknown | 1.41 (0.92–2.15) | 0.111 | |
| Tumor size | >0–2 (reference) | ||
| >2–3 | 2.35 (1.93–2.85) | <0.001 | |
| >3–5 | 4.97 (4.16–5.94) | <0.001 | |
| >5–7 | 7.91 (6.41–9.77) | <0.001 | |
| >7 cm | 13.54 (10.61–17.27) | <0.001 | |
| Unknown | 3.72 (3.17–4.36) | <0.001 | |
| Tumor site | Fundus (reference) | ||
| Body | 0.90 (0.70–1.14) | 0.375 | |
| Antrum | 0.74 (0.59–0.92) | 0.008 | |
| Pylorus | 0.92 (0.67–1.27) | 0.617 | |
| Lesser curvature | 0.78 (0.61–1.01) | 0.057 | |
| Greater curvature | 0.86 (0.63–1.15) | 0.305 | |
| Overlapping site | 1.19 (0.92–1.56) | 0.188 | |
| Unspecified | 1.25 (0.98–1.60) | 0.078 | |
| Histopathology | Other epithelial (reference) | ||
| Diffuse | 1.66 (1.37–2.01) | <0.001 | |
| Intestinal | 1.49 (1.25–1.78) | <0.001 | |
| Upper GI endoscopy | No (reference) | ||
| Yes | 0.90 (0.75–1.08) | 0.255 | |
| Surgery | No (reference) | ||
| Yes | 0.15 (0.13–0.18) | <0.001 | |
| Unknown | 0.41 (0.07–2.38) | 0.320 | |
| Chemotherapy | No (reference) | ||
| Yes | 3.69 (3.29–4.14) | <0.001 | |
| Unknown | 2.35 (1.55–3.54) | <0.001 | |
| Lymph node status | Negative (reference) | ||
| Positive | 34.59 (29.28–40.86) | <0.001 | |
| Unknown | 1.91 (1.59–2.30) | <0.001 | |
| Metabolic syndrome | No (reference) | ||
| Yes | 0.87 (0.75–1.01) | 0.071 | |
| H. pylori | No (reference) | ||
| Yes | 0.86 (0.70–1.06) | 0.151 | |
| RUCA | Urban (reference) | ||
| Large rural | 0.65 (0.45–0.95) | 0.025 | |
| Small rural/isolated | 0.78 (0.49–1.26) | 0.311 |
Table 3.
Multivariable models for overall and disease-specific survival at 5 years (n = 24,212).
| Overall Survival | Disease-Specific Survival | ||||
|---|---|---|---|---|---|
| Variables | Categories | HR (95% CI) | p-Value | HR (95% CI) | p-Value |
| Age group | 18–44 | 0.95 (0.90–1.01) | 0.13 | 0.95 (0.88–1.02) | 0.135 |
| 45–54 | 0.97 (0.92–1.03) | 0.324 | 0.98 (0.92–1.04) | 0.470 | |
| 55–64 | 0.96 (0.91–1.01) | 0.104 | 0.95 (0.90–1.01) | 0.088 | |
| 65–74 (reference) | |||||
| 75+ | 1.25 (1.19–1.30) | <0.001 | 1.22 (1.16–1.29) | <0.001 | |
| Race | non-Hispanic White (reference) | ||||
| non-Hispanic Black | 1.04 (0.98–1.11) | 0.183 | 1.03 (0.96–1.10) | 0.393 | |
| Mexican | 0.97 (0.92–1.02) | 0.284 | 0.93 (0.88–0.99) | 0.016 | |
| Cuban or Puerto Rican | 0.87 (0.70–1.08) | 0.219 | 0.84 (0.64–1.12) | 0.233 | |
| Central or South American | 0.86 (0.79–0.92) | <0.001 | 0.82 (0.75–0.90) | <0.001 | |
| Hispanic other | 0.98 (0.94–1.03) | 0.44 | 0.97 (0.92–1.02) | 0.183 | |
| Native American | 0.87 (0.65–1.17) | 0.36 | 0.85 (0.63–1.15) | 0.285 | |
| Chinese | 0.81 (0.75–0.86) | <0.001 | 0.79 (0.73–0.85) | <0.001 | |
| Japanese | 0.81 (0.74–0.9) | <0.001 | 0.83 (0.75–0.92) | 0.001 | |
| Filipino | 0.91 (0.82–1.01) | 0.070 | 0.82 (0.73–0.92) | 0.001 | |
| Hawaiian or Samoan | 1.02 (0.84–1.25) | 0.843 | 0.78 (0.60–1.02) | 0.071 | |
| Korean | 0.70 (0.65–0.76) | <0.001 | 0.73 (0.67–0.80) | <0.001 | |
| Vietnamese | 0.80 (0.73–0.88) | <0.001 | 0.84 (0.75–0.93) | 0.001 | |
| Laotian or Hmong | 1.07 (0.85–1.35) | 0.549 | 1.09 (0.84–1.41) | 0.504 | |
| Cambodian or Thai | 0.81 (0.62–1.06) | 0.130 | 0.77 (0.57–1.05) | 0.103 | |
| South Asian | 0.87 (0.72–1.04) | 0.128 | 0.81 (0.64–1.02) | 0.072 | |
| Other Pacific Islander | 0.93 (0.68–1.26) | 0.628 | 0.80 (0.56–1.12) | 0.195 | |
| Other Asian | 0.86 (0.77–0.97) | 0.015 | 0.87 (0.76–0.99) | 0.040 | |
| Other race/ethnicity | 0.50 (0.37–0.68) | <0.001 | 0.44 (0.30–0.63) | <0.001 | |
| Marital status a | Unmarried (reference) | ||||
| Married | 0.97 (0.94–1.01) | 0.119 | |||
| Insurance b | Private/PPO/HMO (reference) | ||||
| Medicare | 0.91 (0.87–0.96) | <0.001 | |||
| Medi-Cal | 0.99 (0.94–1.05) | 0.743 | |||
| Uninsured | 0.98 (0.89–1.08) | 0.649 | |||
| Other | 0.95 (0.87–1.05) | 0.321 | |||
| Missing | 0.95 (0.86–1.07) | 0.409 | |||
| Birthplace | Not USA | ||||
| USA | 1.09 (1.04–1.13) | <0.001 | 1.08 (1.04–1.13) | <0.001 | |
| Comorbidity score c | Zero (reference) | ||||
| One | 0.96 (0.93–1.00) | 0.081 | |||
| Two+ | 1.01 (0.95–1.07) | 0.757 | |||
| NCI Cancer Center | No | ||||
| Yes | 0.90 (0.86–0.95) | <0.001 | 0.90 (0.85–0.95) | <0.001 | |
| Stage | I (reference) | ||||
| II | 1.39 (1.29–1.51) | <0.001 | 1.52 (1.38–1.67) | <0.001 | |
| III | 2.05 (1.90–2.21) | <0.001 | 2.39 (2.18–2.61) | <0.001 | |
| IV | 3.07 (2.88–3.28) | <0.001 | 3.58 (3.31–3.89) | <0.001 | |
| Unknown | 1.83 (1.72–1.95) | <0.001 | 2.15 (1.99–2.32) | <0.001 | |
| Tumor size | >0–2 (reference) | ||||
| >2–3 | 1.21 (1.11–1.32) | <0.001 | 1.37 (1.24–1.52) | <0.001 | |
| >3–5 | 1.44 (1.34–1.55) | <0.001 | 1.62 (1.48–1.77) | <0.001 | |
| >5–7 | 1.58 (1.46–1.71) | <0.001 | 1.83 (1.67–2.01) | <0.001 | |
| >7 cm | 1.81 (1.68–1.95) | <0.001 | 2.15 (1.96–2.36) | <0.001 | |
| Unknown | 1.57 (1.47–1.68) | <0.001 | 1.77 (1.63–1.93) | <0.001 | |
| Tumor grade | Well-differentiated (reference) | ||||
| Moderately well | 1.29 (1.06–1.58) | 0.010 | 1.46 (1.13–1.89) | 0.004 | |
| Poorly/Undifferentiated | 1.54 (1.27–1.85) | <0.001 | 1.75 (1.37–2.24) | <0.001 | |
| Unknown | 1.70 (1.41–2.05) | <0.001 | 2.01 (1.57–2.57) | <0.001 | |
| Tumor site | Fundus (reference) | ||||
| Body | 0.93 (0.86–1.00) | 0.052 | 0.96 (0.88–1.04) | 0.331 | |
| Antrum | 0.91 (0.85–0.98) | 0.012 | 0.94 (0.87–1.02) | 0.139 | |
| Pylorus | 0.95 (0.86–1.05) | 0.297 | 0.95 (0.85–1.07) | 0.421 | |
| Lesser curvature | 0.85 (0.79–0.93) | <0.001 | 0.86 (0.79–0.95) | 0.002 | |
| Greater curvature | 0.93 (0.85–1.03) | 0.164 | 0.94 (0.85–1.05) | 0.284 | |
| Overlapping site | 1.08 (1.00–1.17) | 0.055 | 1.12 (1.03–1.22) | 0.011 | |
| Unspecified | 1.07 (0.99–1.15) | 0.094 | 1.07 (0.98–1.16) | 0.148 | |
| Histopathology | Other epithelial (reference) | ||||
| Diffuse | 1.18 (1.11–1.26) | <0.001 | 1.21 (1.13–1.31) | <0.001 | |
| Intestinal | 0.99 (0.93–1.06) | 0.822 | 1.00 (0.93–1.07) | 0.991 | |
| Upper GI endoscopy | No (reference) | ||||
| Yes | 0.88 (0.83–0.94) | <0.001 | 0.85 (0.79–0.92) | <0.001 | |
| Surgery | No (reference) | ||||
| Yes | 0.40 (0.38–0.44) | <0.001 | 0.38 (0.35–0.41) | <0.001 | |
| Unknown | 0.89 (0.50–1.59) | 0.699 | 0.86 (0.45–1.62) | 0.637 | |
| Chemotherapy | No (reference) | ||||
| Yes | 0.63 (0.61–0.65) | <0.001 | 0.66 (0.64–0.69) | <0.001 | |
| Unknown | 1.00 (0.91–1.10) | 0.976 | 1.02 (0.92–1.14) | 0.657 | |
| Lymph node status | Negative (reference) | ||||
| Positive | 2.14 (2.00–2.28) | <0.001 | 2.41 (2.23–2.61) | <0.001 | |
| Unknown | 1.97 (1.81–2.14) | <0.001 | 2.09 (1.89–2.31) | <0.001 | |
| Liver disease | None | ||||
| Mild | 1.31 (1.08–1.59) | 0.005 | 1.30 (1.04–1.61) | 0.018 | |
| Severe | 1.37 (1.11–1.70) | 0.003 | 1.28 (1.01–1.64) | 0.045 | |
| Alcohol abuse d | No | ||||
| Yes | 1.10 (0.99–1.23) | 0.069 | |||
| Smoking | No | ||||
| Yes | 1.11 (1.06–1.18) | <0.001 | 1.07 (1.01–1.14) | 0.027 | |
| DM II | No | ||||
| Yes | 1.15 (1.10–1.20) | <0.001 | 1.14 (1.08–1.19) | <0.001 | |
| GERD | No | ||||
| Yes | 1.05 (1.02–1.09) | 0.006 | 1.07 (1.03–1.12) | 0.001 | |
| H. pylori | No | ||||
| Yes | 0.95 (0.89–1.01) | 0.127 | 0.96 (0.90–1.04) | 0.322 | |
| Obesity | No | ||||
| Yes | 0.86 (0.82–0.91) | <0.001 | 0.86 (0.81–0.91) | <0.001 | |
| RUCA | Urban (reference) | ||||
| Large rural | 1.18 (1.06–1.31) | 0.003 | 1.21 (1.07–1.36) | 0.003 | |
| Small rural/isolated | 1.12 (0.96–1.30) | 0.142 | 1.13 (0.96–1.32) | 0.15 | |
a Only in OS model. b Only in DSS model. c Only in DSS model. d Only in OS model.
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MDPI and ACS Style
Ituarte, P.H.G.; Sullivan, K.; Jankowska, M.M.; Nelson, R.; Huang, R.; Hernandez, M.C.; Wong, C.W.; Deshpande, S.; Paz, I.B.; Melstrom, L.; et al. Health Inequity of Stage and Survival of Gastric Cancer in California. Cancers 2025, 17, 3596. https://doi.org/10.3390/cancers17223596
AMA Style
Ituarte PHG, Sullivan K, Jankowska MM, Nelson R, Huang R, Hernandez MC, Wong CW, Deshpande S, Paz IB, Melstrom L, et al. Health Inequity of Stage and Survival of Gastric Cancer in California. Cancers. 2025; 17(22):3596. https://doi.org/10.3390/cancers17223596
Chicago/Turabian StyleItuarte, Philip H. G., Kevin Sullivan, Marta M. Jankowska, Rebecca Nelson, Robert Huang, Matthew C. Hernandez, Chi Wan Wong, Supriya Deshpande, I. Benjamin Paz, Laleh Melstrom, and et al. 2025. "Health Inequity of Stage and Survival of Gastric Cancer in California" Cancers 17, no. 22: 3596. https://doi.org/10.3390/cancers17223596
APA StyleItuarte, P. H. G., Sullivan, K., Jankowska, M. M., Nelson, R., Huang, R., Hernandez, M. C., Wong, C. W., Deshpande, S., Paz, I. B., Melstrom, L., Kim, E. S., Fong, Y., Woo, Y., & on behalf of the Social Determinants of Health Research Working Group. (2025). Health Inequity of Stage and Survival of Gastric Cancer in California. Cancers, 17(22), 3596. https://doi.org/10.3390/cancers17223596
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