Utilizing cultural and ethnic variables in screening models to identify individuals at high risk for gastric cancer: A pilot study

Haejin In, Ian Solsky, Philip E. Castle, Clyde B. Schechter, Michael Parides, Patricia Friedmann, Judith Wylie-Rosett, M. Margaret Kemeny, Bruce D. Rapkin

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish
Pages (from-to)687-697
Number of pages11
JournalCancer Prevention Research
Issue number8
StatePublished - 1 Aug 2020
Externally publishedYes


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