Identifying persons at high risk for gastric cancer is needed for targeted interventions for prevention and control in low-incidence regions. Combining ethnic/cultural factors with conventional gastric cancer risk factors may enhance identification of high-risk persons. Data from a prior case–control study (40 gastric cancer cases and 100 controls) were used. A “conventional model” using risk factors included in the Harvard Cancer Risk Index's gastric cancer module was compared with a “parsimonious model” created from the most predictive variables of the conventional model as well as ethnic/cultural and socioeconomic variables. Model probability cutoffs aimed to identify a cohort with at least 10 times the baseline risk using Bayes' Theorem applied to baseline U.S. gastric cancer incidence. The parsimonious model included age, U.S. generation, race, cultural food at ages 15–18 years, excessive salt, education, alcohol, and family history. This 11-item model enriched the baseline risk by 10-fold, at the 0.5 probability level cutoff, with an estimated sensitivity of 72% [95% confidence interval (CI), 64–80], specificity of 94% (95% CI, 90–97), and ability to identify a subcohort with gastric cancer prevalence of 128.5 per 100,000. The conventional model was only able to reach a risk level of 9.8 times baseline with a corresponding sensitivity of 31% (95% CI, 23–39) and specificity of 97% (95% CI, 94–99). Cultural and ethnic data may add important information to models for identifying U.S. individuals at high risk for gastric cancer, who then could be targeted for interventions to prevent and control gastric cancer. The findings of this pilot study remain to be validated in an external dataset.

中文翻译：

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在筛查模型中利用文化和种族变量来识别胃癌高危个体：一项试点研究

在低发地区开展有针对性的预防和控制干预措施，需要识别胃癌高危人群。将种族/文化因素与传统胃癌风险因素相结合可能会增强对高危人群的识别。使用了先前病例对照研究（40 例胃癌病例和 100 例对照）的数据。使用包含在哈佛癌症风险指数胃癌模块中的风险因素的“传统模型”与根据传统模型中最具预测性的变量以及种族/文化和社会经济变量创建的“简约模型”进行了比较。模型概率临界值旨在使用应用于基线美国胃癌发病率的贝叶斯定理来确定具有至少 10 倍基线风险的队列。简约模型包括年龄、美国 世代、种族、15-18 岁的文化食物、过量的盐分、教育、酒精和家族史。这个 11 项模型将基线风险增加了 10 倍，在 0.5 概率水平截止，估计敏感性为 72% [95% 置信区间 (CI)，64-80]，特异性为 94% (95% CI , 90-97)，并且能够识别出胃癌患病率为 128.5/100,000 的亚组。传统模型只能达到基线的 9.8 倍风险水平，相应的灵敏度为 31%（95% CI，23-39），特异性为 97%（95% CI，94-99）。文化和种族数据可能会为模型添加重要信息，用于识别美国胃癌高危人群，然后他们可以成为预防和控制胃癌干预措施的目标。