Utilizing Cultural and Ethnic Variables in Screening Models to Identify Individuals at High Risk for Gastric Cancer: A Pilot Study

Cancer screening allows for the earlier detection and treatment of precancers and cancers, which can reduce cancer-related morbidity and mortality (1). In the United States, screening is commonly performed for cancers of the cervix, colon, lung, prostate, and breast but it is not generally performed for gastric cancer, which has a low incidence in the United States (2). In other countries with higher incidence rates of gastric cancer such as Korea and Japan, national gastric cancer screening programs have been implemented, which have resulted in improved survival from gastric cancer (3, 4). Although a population-based national screening program for gastric cancer may not be feasible in the United States due to its relatively low prevalence, targeted screening of high-risk individuals could potentially reduce gastric cancer–related mortality, which is estimated to have caused approximately 11,140 deaths in the United States in 2019 (5). A survey tool has the potential to identify higher risk individuals that would benefit from screening by endoscopy.

Prior efforts to identify patients at risk have largely focused on surveying patient symptoms. However, early gastric cancer is frequently asymptomatic, which results in this method generally identifying individuals already in advanced stages of cancer with high mortality (6, 7). There currently are few risk prediction models of gastric cancer detection in asymptomatic individuals so that cancer can be averted or treated successfully. One such model is the gastric cancer module of the Harvard Cancer Risk Index (HCRI), which was created by utilizing risk factors identified from group consensus. It was constructed to predict individual patients' risks for various different types of cancers (8). This was later developed into an online assessment, was renamed Your Disease Risk, and provides risk assessment and suggestions for prevention for several cancer types, including gastric cancer (available from: https://siteman.wustl.edu/prevention/ydr/; ref. 9).

Gastric cancer is the third most common cancer in the world, and its incidence varies greatly by country. High-incidence countries such as Mongolia, Korea, and Japan have incidence rates 10–15 times greater than low-incidence counties such as Saudi Arabia, Indonesia, and Nigeria (10). Given the extreme variation in gastric cancer risk around the world, in countries like the United States where its population is a heterogeneous mix of race, ethnicity, and cultures, gastric cancer risk is also very likely to be associated with ethnic background and country of origin. The addition of these variables could greatly enhance our ability to identify persons at risk, and be particularly important for screening efforts in the diverse U.S. patient population. Furthermore, studies have shown that targeted gastric cancer screening efforts for high-risk racial and ethnic groups may be cost-effective (11).

The objective of our study was to determine whether adding cultural and ethnic variables not included in the HCRI model, can improve the performance of models to identify individuals at high risk for gastric cancer in the United States. This study utilizes data from a prior case–control study where data were collected using an extensive survey composed of known and proposed gastric cancer risk factors to try to identify variables associated with gastric cancer (12). The goal of this study is not to propose a specific screening instrument to be used in a generalizable way but to help guide the development of future risk prediction tools to identify high-risk cancer patients in a simple, noninvasive manner.

Data from a prior gastric cancer case–control study were utilized for this study (12). The gastric cancer cases (n = 40) were from a large urban academic medical center or an inner city public hospital serving largely racial/ethnic minorities. The controls (n = 100) were recruited from primary care (n = 47) and community settings (n = 53) to obtain a study sample with similar demographic characteristics. Primary care patients were recruited by phone or by direct recruitment at primary care clinics. Community controls were recruited from community centers, libraries, and churches neighboring the two hospitals. The survey was conducted through phone interview or paper survey. As described in detail in a prior publication (12), controls from primary care settings, as compared with controls from community settings, were more frequently older than 70 years (17% vs. 6%), Black (40% vs. 26%), less well educated [education >high school: 47% vs. 64%], and born in countries with lower rates of gastric cancer (incidence rate of country of birth >15 per 100,000: 4% vs. 19%). Furthermore, controls from primary care settings more often completed phone interviews than paper surveys (72% vs. 7%), whereas community controls more often completed paper surveys than phone interviews (93% vs. 28%; ref. 12).

Information contained in the study database exclusively contained self-reported data, including demographics, race/ethnicity, socioeconomic status (SES), food frequency, smoking, alcohol habits, family health history, history of Helicobacter pylori (H. pylori) diagnosis and treatment, ethnicity, country of birth, acculturation index, and lifetime ethnic dietary habits. Given the subjective nature of some of the questions associated with these variables, which could be challenging to complete, the aforementioned publication also included information about missing data for some of these variables indicating nonresponses.

The gastric cancer case–control survey assessed all variables contained in the gastric cancer module of the HCRI as well as ethnic and cultural, SES, and dietary variables. While the variables collected in the gastric cancer case–control study and HCRI were topically alike, some of the survey items were not identical. The survey items used in HCRI and for the gastric cancer case–control study for this analysis are presented in Table 1. In total, 14 variables were assessed. Some variables required multiple survey questions. The survey items for each variable were first compared to see whether these tools assessed comparable information. Gastric cancer case–control data were categorized to best match the information captured in the HCRI questions. Regarding family history of cancer, gastric cancer case–control data contained information up to second generation; however, the variable was regrouped to match the information asked for the HCRI, which only assessed gastric cancer in siblings or parents. For alcohol consumption, while the HCRI considered four drinks per day as higher risk, very few patients in the gastric cancer case–control study reported this level of alcohol consumption; hence, we assessed alcohol consumption as two or more drinks per day or less than two drinks per day. There was a slight difference in the smoking items, where the gastric cancer case–control study only considered people who have smoked more than 100 cigarettes to be or to have been smokers, while the HCRI did not make this distinction.

The objective of this analysis was to compare the discriminatory ability of a risk model using conventionally known risk factors as assessed in the gastric cancer module of the HCRI (ref. 8; “conventional risk factor model”) with a parsimonious model developed through the inclusion of ethnic and cultural variables associated with gastric cancer followed by variable selection (“parsimonious model”). The conventional risk variables are age, gender, family history of gastric cancer, body mass index (BMI), excessive salt intake, alcohol, smoking, blood type, and H pylori. Cultural–ethnic variables include race, immigration/generation, acculturation, and consumption of cultural foods at ages 15–18 years (age range selected as this is a time when individuals are generally in high school which is a time they should be able to recall and this range was also found to be most predictive in our prior analysis). Education was also included as a variable.

We first examined the effect size for age- and sex-adjusted individual variables. Then three logistic regression models were built and compared. First, the conventional risk factor model was created (“conventional model”). Then the ethnic and cultural variables and education were added to create an all-inclusive model (“enhanced model”). The enhanced model was then used as the basis to select a model which contained only the most predictive variables (“parsimonious model’). Parsimonious model selection utilized multiple methods to determine variables for model inclusion; backwards, forward, and stepwise selection (cut-off for inclusion and removal at the P = 0.1 level) and variable ranking using changes using Nagelkerke/Cragg and Uhler pseudo-R square statistics (13). Pseudo-R² was used to quantify the contribution of variables to the observed variation in being a gastric cancer case, and changes in the pseudo-R² statistics (delta-Pseudo-R²) were used capture the relative contribution of each variable to the model.

Sensitivities and specificities were calculated for each level of probability for all three models. Selection of model cut-off levels was based on maximizing sensitivity and specificity and achieving our operational goal of identifying a risk threshold that corresponds to a positive predictive value (PPV) that is at least 10 times the risk of the baseline U.S. annual incidence. This conservative cutoff is based on differences in incidence between the United States and Korea/Japan. Gastric cancer incidence in the United States is 7–10 times lower than Korea and Japan, making use of preventive and early detection strategies for gastric cancer in the general population unacceptable in terms of population benefits and harms. The GLOBOCAN reported age-standardized rate (ASR) for Korea and Japan, countries that screen, are 114.0 and 75.5, respectively; the ASR for the United States is 11.2 per 100,000, male and female combined, in patients' ages 40–79 years old. Note that GLOBOCAN ASRs are used for comparability in age adjustments across countries. The predicted prevalence of gastric cancer for cohort found to be high risk by the model was estimated using Bayes' Theorem (14) applied to the baseline prevalence in the U.S. population. Analyses were performed using SAS 9.4 (SAS Institute Inc.).

Demographics of 40 gastric cancer cases and 100 controls are displayed in Table 2. Cases were more likely to be male (50% vs. 24%, P = 0.003), Hispanic (60% vs. 28%, P = 0.003), have less than a high school education (47.5% vs. 19%, P = 0.001), have a family history of gastric cancer (15% vs. 5%, P = 0.047), be foreign born (85% vs. 54%, P = 0.003), consume cultural food at ages 15–18 years daily or more often (68% vs. 36%, P = 0.003), have excess salt in their diet (15% vs. 5%, P = 0.047), and more frequently report two or more drinks a day or more of alcohol (18% vs. 6%, P = 0.037). Trends were also observed for cases to be less or moderately acculturated (73% vs. 57%, P = 0.286), have a current or past history of smoking (45% vs. 32%, P = 0.263), have BMI > 30 (43% vs. 35%, P = 0.407), and less likely to have blood type A (13% vs. 17%, P = 0.509) than controls. H. pylori prevalence was similar between cases and controls (8% vs. 6%, P = 0.279).

Table 3 summarizes the selection criteria of variables for inclusion in the parsimonious model. The ranking of variable importance using delta-Pseudo-R² was a follows; age (delta-Pseudo-R², 30%), U.S. generation (12%), race (9%), cultural food at ages 15–18 years (7%), excessive salt (5%), acculturation (5%), education (3%), alcohol (3%), family history of gastric cancer (2%), smoking (2%), H. pylori (2%), gender (1%), BMI (0%), and blood type A (0%). The variables that were uniformly selected using backward, forward, and stepwise model selections were age, U.S. generation, race, cultural food at ages 15–18 years, excessive salt, and alcohol. Acculturation was selected only when using backward and forward selection, education only with backwards and forward selection, and family history only with forward and stepwise selection. Acculturation requires 20 survey items for assessment, hence we dropped this variable from consideration because the end product of this survey needs to be short and easy to use for practicality. The final variables included in the parsimonious model were age, U.S. generation, race, cultural food at ages 15–18 years, excessive salt, education, alcohol, and family history of gastric cancer.

Table 4 shows the OR estimates of each variable after adjusting for age and sex, as well as for the three multivariable models. After adjusting for age and sex, having a family history of gastric cancer, consuming excess salt, being diagnosed but not having treated H. pylori, being foreign born, having at least daily cultural food consumption, less than high school education, and being of Hispanic race were individually found to be predictive. In the conventional model, older age [OR, 1.1; 95% CI (confidence interval), 1.06–1.2] and consumption of excess salt (OR, 7.8; 95% CI, 1.5–41.0) were independently predictive. In the parsimonious model, age (OR, 1.2; 95% CI, 1.1–1.3), excess salt (OR, 15.7; 95% CI, 1.9–132.8), daily alcohol consumption of two drinks or more (OR, 78.1.0; 95% CI, 4.9–999.9), and being foreign born (OR, 29.3; 95% CI, 3.5–247.9) were found to be independently predictive.

Model AUC for the parsimonious model (0.94; 95% CI, 0.91–0.98) was improved over the conventional model (0.87; 95% CI, 0.81–0.93; P = 0.009). ROC curves are shown in Fig. 1.

Table 5 shows the selection of model cutoffs for comparison of model performance. The conventional model was unable to reach the target level of risk (10 times baseline risk) at any cut-off level with specificity of less than 100%. The parsimonious model exceeded 10 times the risk at the 0.5 probability level cutoff. This resulted in a model sensitivity of 72% (95% CI, 64%–80%), specificity of 94% (95% CI, 90%–97%), and PPV of 0.128% (95% CI, 0.086–0.359), which would translate to the prediction model being able to identify a subcohort of the population with a prevalence rate of 128.5 per 100,000. This parsimonious model performed as well as the enhanced model; the enhanced model exceeded 10 times the risk at the 0.4 probability level cutoff with a sensitivity of 90% (95% CI, 85%–95%), specificity of 93% (95% CI, 88%–97%), and PPV of 0.137% (95% CI, 0.090%–0.345%). However, the parsimonious model required only 11 survey items compared with the enhanced model that required 42 survey items. Characteristics of the three models are provided for comparison on Table 6.

We found that the inclusion of cultural and ethnic factors to conventionally known gastric cancer risk factors greatly enhanced the ability to detect individuals at higher risk for gastric cancer. Immigration/generation was highly predictive, and the addition of this variable allowed for the development of a model that used fewer survey items with considerable improvement of sensitivity, specificity, model fit, and PPV.

The findings from this study and its predecessor indicate that cultural and ethnic variables may play a critical role in identifying individuals at higher risk for developing gastric cancer in the United States. In this study, data were collected using a survey comprised of 227 items. After item reduction steps, a logistic regression model using the highest ranked eight variables using boot-strapping techniques was chosen as the final model (12). The resulting eight variables were age, gender, family history of gastric cancer, race, generation/immigration, consumption of cultural foods at ages 15–18 years, education, and acculturation, and were the basis of these five cultural–ethnic variables being included as variables explored in this study. Generation/immigration remained a strong predictor in both analyses highlighting the importance of considering cultural and ethnic variables in identifying individuals at high risk for gastric cancer.

Large racial/ethnic disparities exist for gastric cancer incidence in the United States (15, 16). While rare in whites, among Blacks, Asians, and Hispanics, gastric cancer is among the 10 most frequent cancers and has nearly double the cancer incidence rate of whites (17). Studies have shown that racial minorities in the United States have an increased incidence of gastric cancer compared with non-Hispanic Whites and that there is a higher incidence of gastric cancer among immigrants from specific countries including non-Asian countries (15, 16, 18). In a study of first-generation Hispanic males, incidence rate was 21.3 per 100,000 in men from Puerto Rico compared with 9.2 per 100,000 for non-Hispanic White males (19). In a study of Asians living in the United States, gastric cancer was among the five most frequent cancers for Koreans, Chinese, Japanese, Laotian, and Vietnamese (20). In addition, Asians had disproportionately greater tumor burden when compared with their representation in the overall population; Asians represented 16% of gastric cancer diagnosed, but only accounted for 9% of the population (15).

Diet has long been considered a risk factor for gastric cancer development. It has been suggested that pickled and salted foods put individuals at higher risk and there is some question as to whether a Western diet can modulate this risk (21–23). If this is the case, it may explain why the dietary acculturation influences gastric cancer risk: it is possible that as individuals develop “American habits” including the adoption of a Western diet, their risk for gastric cancer changes. Given that we desire to assess gastric cancer risk using the least number of questions, assessing acculturation using established instruments such as the Vancouver Acculturation Index (24) or assessing food consumption using food frequency questionnaires would not be feasible given the large number of questions required. Instead we opted to assess acculturation through the development of a dietary acculturation question; consumption of cultural foods at various ages. Ages 15–18 years was selected on the basis of our prior study that showed this age group to have the highest predictability. By asking it in this manner, we can simultaneously capture both acculturation and consumption of certain foods that may be related to greater risk in those particular ethnicities/regions. For example, compared with a Western diet, a traditional Korean diet is largely soy-based with a large amount of pickled foods and allium vegetables (garlic and onions). Instead of asking about each food item separately, asking about the frequency of Korean food can serve as a proxy for consumption of food items associated with greater or lesser risk. While undefined, this item is likely to also serve as a proxy for as yet to be discovered factors that modulate risk for gastric cancer.

While the identification of the most predictive risk factors for gastric cancer is clearly necessary, these efforts will need to go hand-in-hand with those to try to further educate providers and the public about the importance of gastric cancer screening (25). This is evident from research that has shown that many providers have insufficient understanding of gastric cancer risk and those individuals who are at higher risk (26). Furthermore, once a risk prediction tool such as a survey is developed, implementation efforts will need to be developed carefully to be cognizant to include racial and ethnic minority groups that will be at particular risk.

In the United States, only 28% of gastric cancers are diagnosed at earlier localized stages of cancer, resulting in 5-year survival of 31% (27). In Korea and Japan, where they have instituted population-based screening programs since the 1950s and 1980s, respectively, 50%–60% of gastric cancers are detected in earlier stages (28–34) and as a result these countries report superior overall 5-year gastric cancer survival of 40%–60% (32, 35) While no randomized controlled trials exist, observational studies suggest a 30%–60% mortality benefit from screening (3, 4, 36–43).

Our target PPV of 10 times the background incidence rate aims to identify a subset of the population that would have the same level of risk as countries that currently screen for gastric cancer. In our study, the parsimonious model showed potential to identify a cohort with prevalence of 128.5 per 100,000. This level of risk is also comparable with that of colorectal cancer in the United States, which has an established screening program. The GLOBOCAN ASR for colorectal cancer in persons ages 50–79, in the United States is 96.7 per 100,000 population (10).

Assuming that we are able to identify a sufficiently high-risk group, upper endoscopic screening to detect and treat premalignant lesions and diagnosis frank gastric cancer at an earlier stage is likely to have similar benefit as colorectal cancer screening in the United States or gastric cancer screening in high-risk countries. Compared with colonoscopy, upper endoscopy also has the benefit of not requiring a bowel prep and is simpler to administer. Benefits of screening for gastric cancer are especially pronounced because of extreme 5-year survival differences by stage, ranging from 90% for Tis and T1a lesions to 67% for local, 29% for regional, and 4% for metastatic disease (44, 45). In addition, gastric cancer–related mortality is not offset by effective treatments, making early detection the most promising option (46).

Although the findings of this work are important for showing the value of cultural and ethnic variables to gastric cancer risk prediction models, research is needed to identify additional risk factors that allow for even greater discrimination between those high risk versus lower risk than a survey of self-reported conventional and cultural–ethnic risk factors can allow. One possible area for exploration is the characterization of patient's oral and fecal microbiome, which could serve as a potential biomarker for gastric cancer and which may also shed further light on the relationship between diet and gastric cancer risk (47). Another possibility is adding data on H. pylori antibodies, especially those to CagA, VacA, and pepsinogen and hemoglobin A1c levels (48).

This study is not without its limitations. First, the data were taken from a prior study with a relatively small number of patients from a single geographic area. Data were collected by phone interview or survey and the different ways in which individuals completed these could have introduced some inaccuracies into the data. Furthermore, while efforts were made to obtain a diverse sample of immigrants, the sample lacked people born in the most endemic regions of the world such as Korea, Mongolia, and Japan, which precluded our ability to adequately explore the prevalence of gastric cancer in a subject's birth country as a variable of interest. The study also chose representative variables to serve as conventional and cultural–ethnic variables based upon what has been used previously; however, this pool of variables should not necessarily be considered definitive and all-encompassing as there are likely additional variables that could have been included. Neither the enhanced risk model, which includes all of the conventional and cultural–ethnic variables, nor the parsimonious model which contains the immigration/generation variable should be considered the definitive model for risk prediction for patients with gastric cancer. Development of a high-risk prediction model would need to be undertaken in a much larger patient population to make more concrete statements and fully explore how variables that put individuals at risk for gastric cancer differ by race/ethnicity. In addition, we make no claim as to what is an appropriate risk threshold at which patients should be declared at sufficiently high risk that they warrant referral for a screening endoscopy. While the parsimonious model improved PPV by 10 times compared with a model using conventional risk factors alone, most at that risk threshold or above still have no gastric precancer or gastric cancer, there needs to be continued efforts to identify other predictors of gastric cancer and improve upon our ability to identify persons at gastric cancer. Importantly, no existing sources of data contain all the variables we found to be important in this study, and these findings are reported without external validation.

Nonetheless, while this study does not propose a specific screening tool to be used at this time, this study provides further support that cultural and ethnic variables may be useful for identifying high-risk gastric cancer patients for screening in the United States. The findings of this study may prompt other investigators to begin collecting these variables with more consistency. As the importance of cultural and ethnic variables to cancer research becomes evident, it is equally important that a culturally sensitive approach be taken to care delivered at all stages of the cancer continuum to ensure that high-risk patients are not only identified but that they also receive care and support of the highest quality possible (49).

The addition of ethnic and cultural variables, particularly the immigration/generation, to conventional risk factor variables improved the ability of models to identify individuals at high risk for gastric cancer. Future efforts to develop gastric cancer risk prediction tools in racially/ethnically heterogeneous populations should look to incorporate these variables for improved risk prediction. These variables may one day contribute to the creation of a parsimonious survey–based tool that can serve as a highly scalable paradigm to identify high-risk individuals for interventions (e.g., endoscopy) to prevent and control gastric cancer.

No potential conflicts of interest were disclosed.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Conception and design: H. In, C.B. Schechter, J. Wylie-Rosett

Development of methodology: H. In, C.B. Schechter

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): H. In, J. Wylie-Rosett, M.M. Kememy, B.D. Rapkin

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): H. In, I. Solsky, P.E. Castle, C.B. Schechter, M. Parides, P. Friedmann, B.D. Rapkin

Writing, review, and/or revision of the manuscript: H. In, I. Solsky, P.E. Castle, C.B. Schechter, M. Parides, P. Friedmann, J. Wylie-Rosett, M.M. Kememy, B.D. Rapkin

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): H. In

Study supervision: H. In

Research reported in this article was supported by the NCI of the NIH under the award number UG1CA189823 (Alliance for Clinical Trials in Oncology NCORP grant). Partial support was also provided by the Montefiore Medical Center minority-based NCORP community site (UG1CA189859).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.