Community-Based Helicobacter pylori Screening in High-Risk U.S. Populations: Protocol for a Mixed-Methods Study to Inform Gastric Cancer Prevention and Migration-Informed Risk Modeling
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Introduction
Gastric cancer (GC) remains a persistent and underrecognized health disparity in the United States, disproportionately affecting immigrant communities from East Asia, Latin America, and other high incidence regions. Helicobacter pylori (H. pylori), a WHO Group 1 carcinogen and the leading modifiable risk factor for non cardia GC, remains largely unaddressed in U.S. screening guidelines, despite successful international eradication programs.
Methods and Analysis
This prospective, mixed methods study will enroll 2,000 adults aged 30 to 80 from Asian, Hispanic, and other high risk racial/ethnic backgrounds across the Greater New York area. Participants will be recruited through partnerships with trusted community organizations. Each will complete a multilingual survey and undergo H. pylori testing (urea breath test), with confidential follow up and referral. The survey will assess demographic, clinical, and KAP (knowledge, attitudes, practices) variables. A migration informed risk index will be developed using logistic regression and validated using k fold cross validation and bootstrap resampling.
Ethics and Dissemination
Approved by the Yale University IRB, with informed consent obtained in preferred languages. Results will be disseminated through community forums, peer reviewed publications, academic conferences, and policy briefings.
Strengths and Limitations of This Study
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Culturally and linguistically sensitive screening and navigation strategies tailored to high-risk immigrant populations
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First U.S.-based effort to develop a migration-informed risk index for H. pylori using clinical, demographic, and KAP data
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Combines multilingual outreach and venue-based recruitment with predictive modeling to inform risk-stratified screening
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Enables detailed assessment of screening barriers and supports a multidimensional prevention framework
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Limitations include potential selection bias, limited geographic generalizability, and reliance on self-reported data; external validation will be necessary
