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Published on: May 30, 2025
Disaggregating Asian American and Pacific Islander Subpopulations in Esophageal Cancer: Stage, Histology, and
Shawn M Doss1, Vatsala Kapoor2, Ryan Brownlee2
1Division of Surgical Oncology, Department of Surgery, Medical College of Georgia, Augusta, GA, USA. shawndossworks@gmail.com.
Background:
Asian American/Pacific Islander (AAPI) patients are typically aggregated together in esophageal cancer (EC) research, which may mask differences in risk factors, histology, and survival. This study analyzed EC characteristics and outcomes in disaggregated AAPI groups.
Methods:
Using the National Cancer Database (2009-2020), we compared aggregated AAPI patients, regional subgroups (East Asian, etc.), and subpopulations (Chinese, Vietnamese, etc.) to non-Hispanic White (NHW) patients. Advanced stage (III-IV) at diagnosis and squamous cell carcinoma (SCC) histology were analyzed. After adjusting for sociodemographic factors, 3-year overall survival (OS) was assessed.
Results:
A total of 3,254 AAPI patients with EC were compared with 156,801 NHW patients. Aggregated AAPI patients had higher odds of advanced stage disease (adjusted odds ratio [aOR] 1.12, 95% confidence interval [CI] 1.03-1.21, p = 0.006), higher odds of squamous cell carcinoma (aOR 4.63, 95% CI 4.28-5.01, p < 0.0001), and better 3-year OS (hazard ratio [HR] 0.87, 95% CI 0.82-0.91, p < 0.001). For regional subgroups, only Southeast Asian (aOR 1.46, 95% CI 1.22-1.75, p < 0.0001) and Pacific Islander (aOR 1.62, 95% CI 1.19-2.23, p = 0.003) patients had higher odds of advanced stage disease. For subpopulations, Vietnamese patients had the highest odds of SCC (aOR 8.90, 95% CI 6.73-11.79, p < 0.0001) and worse 3-year OS (HR 1.29, 95% CI 1.09-1.51, p = 0.002), while Chinese (HR 0.66, 95% CI 0.60-0.74, p < 0.0001) and Korean patients (HR 0.70, 95% CI 0.59-0.84, p < 0.0001) showed better OS.
Conclusions:
Disaggregated analysis of AAPI patients revealed significant heterogeneity in EC characteristics and survival. Our findings emphasize collecting subpopulation-level data and tailoring interventions.
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