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. Author manuscript; available in PMC: 2016 May 4.
Published in final edited form as: Health Psychol. 2015 Feb;34(2):101–110. doi: 10.1037/hea0000177

Effects of racial and ethnic group and health literacy on responses to genomic risk information in a medically underserved population

Kimberly A Kaphingst 1, Jewel D Stafford 1, Lucy D’Agostino McGowan 1, Joann Seo 1, Christina R Lachance 2, Melody S Goodman 1
PMCID: PMC4856065  NIHMSID: NIHMS662728  PMID: 25622080

Abstract

Objective

Few studies have examined how individuals respond to genomic risk information for common, chronic diseases. This randomized study examined differences in responses by type of genomic information [genetic test/family history] and disease condition [diabetes/heart disease] and by race/ethnicity in a medically underserved population.

Methods

1057 English-speaking adults completed a survey containing one of four vignettes (two-by-two randomized design). Differences in dependent variables (i.e., interest in receiving genomic assessment, discussing with doctor or family, changing health habits) by experimental condition and race/ethnicity were examined using chi-squared tests and multivariable regression analysis.

Results

No significant differences were found in dependent variables by type of genomic information or disease condition. In multivariable models, Hispanics were more interested in receiving a genomic assessment than Whites (OR=1.93; p<0.0001); respondents with marginal (OR=1.54; p=0.005) or limited (OR=1.85; p=0.009) health literacy had greater interest than those with adequate health literacy. Blacks (OR=1.78; p=0.001) and Hispanics (OR=1.85; p=0.001) had greater interest in discussing information with family than Whites. Non-Hispanic Blacks (OR=1.45; p=0.04) had greater interest in discussing genomic information with a doctor than Whites. Blacks (β= −0.41; p<0.001) and Hispanics (β= −0.25; p=0.033) intended to change fewer health habits than Whites; health literacy was negatively associated with number of health habits participants intended to change.

Conclusions

Findings suggest that race/ethnicity may affect responses to genomic risk information. Additional research could examine how cognitive representations of this information differ across racial/ethnic groups. Health literacy is also critical to consider in developing approaches to communicating genomic information.

Keywords: genetic communication, family history, genetic susceptibility testing, cultural competence, health literacy

INTRODUCTION

Personal genomic information related to common, chronic diseases is likely to become an important part of health care (McBride, Bowen, et al., 2010), either through assessment of family history (Khoury, Feero, & Valdez, 2010; Valdez, Yoon, Qureshi, Green, & Khoury, 2010) or, increasingly, through genetic testing (McBride, Koehly, Sanderson, & Kaphingst, 2010). Although the clinical utility of these tools is still uncertain (Khoury et al., 2010; Marteau et al., 2010), genomic information may be used to personalize prevention and treatment and motivate changes in health habits (Cameron, Marteau, Brown, Klein, & Sherman, 2012; Feero, 2008; Khoury et al., 2009). Few data are available on differences in responses to risk information based on genetic testing compared with assessment of family history (Hicken & Tucker, 2002; Wijdenes-Pijl, Dondorp, Timmermans, Cornel, & Henneman, 2011). Such information is critical to translating genomic information into health care settings and disease prevention initiatives.

Responses to different types of genomic information

Prior research using hypothetical vignettes has shown some differences in responses to genetic test results and family history assessments. A randomized study found that parents were more concerned about their risk of a severe, adult-onset disease when the risk estimate was based on a family history assessment rather than genetic test results (Tarini, Singer, Clark, & Davis, 2008). In focus groups, some participants believed that genetic test results would be more motivational than family history assessments for changing health habits, and that the latter might affect family communication (Wijdenes-Pijl et al., 2011). However, in an analog study, no difference was found in intention to follow recommended behaviors between those who heard about genetic test results and those who heard about family history information (Hicken & Tucker, 2002).

With return of actual disease risk estimates, a randomized controlled trial found that participants who received genetic test results perceived a diagnosis of familial hypercholesteremia as more accurate than those who received family history and cholesterol results (GRAFT Study Group, 2004). Among adult children of patients with Alzheimer’s Disease (AD), those who received genotype results with family history judged their AD risk to be lower and were more certain about their AD risk than those who received family history only (LaRusse et al., 2005). Further investigation is needed regarding responses to different types of genomic information for common, chronic diseases and whether these responses depend upon the disease condition.

Possible mechanisms underlying different responses to genomic information

The Common Sense Model (CSM) of self-regulation of health and illness (Leventhal et al., 1997) suggests why individuals may react differently to genetic test results and family history assessments. Based on CSM, genomic risk information activates a cognitive representation of a threat which has five domains, identity, cause, timeline, consequences, and control, (Cameron et al., 2012; Marteau & Weinman, 2006), as well as risk perceptions (Marteau & Weinman, 2006). This representation predicts the procedures an individual will identify for controlling the threat (Marteau & Weinman, 2006). Differences in representations of genetic test results and family history assessments may therefore lead to different responses. Genetic test results may be seen as more definitive (Senior, Marteau, & Peters, 1999) or less controllable (Nelkin & Lindee, 1995) than other types of risk information (Hicken & Tucker, 2002), and may be less likely to activate behavior change as a coping procedure (Marteau & Weinman, 2006). However, family history information might be more salient based on experiences with family members (Tarini et al., 2008) or because it captures not only genetic risk, but shared behavioral and environmental risk (Yoon, Scheuner, & Khoury, 2003). More research is needed to investigate representations of and responses to genetic test results compared with family history assessments.

Differences in responses to genomic information by race and ethnicity

Another important gap is the limited data available on how race/ethnicity affects responses to genomic information, especially among medically underserved populations. Individuals from racial and ethnic minority groups have been underrepresented in genetic research (Akinleye et al., 2011; Alford et al., 2011). Studies have shown lower awareness of and knowledge about genetic testing among individuals from minority racial and ethnic groups compared with Whites (Armstrong, Weber, Ubel, Guerra, & Schwartz, 2002; Lerman et al., 1999; Pagan, Su, Li, Armstrong, & Asch, 2009; Ramirez, Aparicio-Ting, de Majors, & Miller, 2006; Vadaparampil, McIntyre, & Quinn, 2010; Wideroff, Vadaparampil, Breen, Croyle, & Freedman, 2003). Blacks and Hispanics have fewer positive and more negative attitudes towards genetic testing than Whites (Peters, Rose, & Armstrong, 2004; Singer, Antonucci, & Van Hoewyk, 2004). Individuals from racial and ethnic minority groups have less use of genetic testing than Whites (Alford et al., 2011; Chen et al., 2002; Hall & Olopade, 2005). These prior data suggest that it is critical to examine how individuals from different racial and ethnic groups respond to different types of genomic risk information.

METHODS

Study purpose

To address these research gaps, a two-by-two randomized experiment was conducted to examine the effects of type of genomic information and disease condition on responses among a medically underserved population. The study investigated three questions: (1) how responses to genomic information differed based on the experimental factors; (2) whether these associations were mediated by factors suggested by CSM (i.e., control perceptions, risk perceptions; (Cameron et al., 2012; Marteau & Weinman, 2006) and prior literature (i.e., worry, information characteristics); and (3) how responses varied across racial/ethnic groups and whether hypothesized mediating variables acted as mediators in the associations between race/ethnicity and dependent variables. Based on CSM and prior literature, individuals were hypothesized to be more interested in receiving genetic test results and in discussing these results with a doctor compared with family history assessments, but more interested in discussing family history assessments with family members and intending to change more health habits based on family history assessments compared with genetic test results. Dependent variables were selected based on prior literature as those related to uptake of genetic testing (i.e., interest in genomic assessment) or coping responses to genomic information (i.e., interest in discussing with doctor or family, intentions to change health habits) (McBride, Koehly, Sanderson, & Kaphingst, 2010; Persky, Kaphingst, Condit, & McBride, 2007; Wijdenes-Pijl et al., 2011). Responses were hypothesized to be mediated by risk perceptions, perceived control, predictive ability, and worry.

Study design

Data collectors administered waiting room surveys at 3 community health centers in a large metropolitan area in the eastern U.S. Participants were randomly assigned to complete one of four survey forms, each containing one vignette that asked them to imagine receiving genomic risk information. The vignettes varied across two factors: type of genomic information [genetic test results vs. family history assessment] and disease condition [heart disease vs. diabetes].

Heart disease and diabetes were selected because they have similar risk factors and would be familiar but may have differences in cognitive representations of the threat, such as, for example, that diabetes might be seen as less controllable (Thompson et al., 2013). Participants were not offered actual genomic information. All vignettes asked participants to imagine that they were at increased risk of a common, chronic disease as determined by a doctor after reviewing genomic information (i.e., lifetime risk of disease of 37 in 100 compared with 33 in 100 in the general population); the same risk estimates were used in all vignettes (Figure 1). The information in the vignettes was adapted from actual presentations of genomic risk information used in a prior population-based study (Kaphingst et al., 2010; McBride et al., 2009), and the risk estimates were based on the risk associated with carrying a risk-increasing variant in a single nucleotide polymorphism for a common, chronic disease like diabetes (Kaphingst et al., 2010; McBride et al., 2009). Vignettes were developed by a transdisciplinary team with expertise in health literacy, genetic communication, health education, and health disparities based on best practices in designing vignettes to examine interest in genetic testing (Persky, Kaphingst, Condit, & McBride, 2007) and in plain language and risk communication (Doak, Doak, & Root, 1996; Fagerlin, Zikmund-Fisher, & Ubel, 2011). Questions asked before the vignette (e.g., family history, disease diagnoses) were identical across survey forms. Questions asked after the vignette (i.e., dependent variables, hypothesized mediating variables) were also identical except that these items referenced the disease in the vignette. Specific items are described below.

Figure 1. Sample vignette for family history assessment and diabetes condition.

Figure 1

Figure 1

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Please imagine that during your last visit to your doctor, he or she asked you about and then wrote down your complete FAMILY HEALTH HISTORY. This family health history includes all of your immediate and extended family. The doctor looked at your family health history in order to tell you about your chance of getting diabetes in your lifetime.

Now you are back at your doctor’s office to hear the results. Your doctor tells you that he or she has looked over all of the information in your family health history and the results show that you have a HIGHER CHANCE of getting DIABETES in your lifetime.

Your doctor tells you that based on your family health history your chance of getting diabetes in your lifetime is 37 in 100.

The average chance of getting diabetes in a person’s lifetime is 33 in 100.

Participants

English-speaking visitors aged 18 years and older were eligible to participate. The community health centers in which recruitment was conducted provide comprehensive health care services to a diverse patient population. Uninsured and underinsured patients are billed on a sliding fee scale based on household income. Data collection occurred on a rotating schedule including different days of the week and at different times of the day over a six-month period in order to reach a representative sample of visitors. Trained data collectors approached all adult visitors in waiting rooms during their shifts and completed an in-person consent process. Of those approached, 62% agreed to complete the survey. Of the 1,970 that agreed to participate, 1,490 (75.6%) completed all survey components. This study was approved by the institutional review boards at the National Institutes of Health, university and county health department.

Measures

Dependent variables

Interest in genomic assessment

Participants were asked to what extent they would be interested in receiving the genomic assessment if it were actually available on a five-point Likert scale from “not at all” to “very” interested. Because of the skewed distribution and in order to predict strong interest in receiving the genomic assessment, this variable was dichotomized as “very interested” versus all other categories in analysis.

Discussion with doctor or family members

Participants were asked two questions to assess whether they would want to discuss the genomic information with their doctor or family members (i.e., “I would want to discuss the information with my doctor”). These items were adapted from a population-based study of responses to genomic information (Kaphingst et al., 2012) and were answered on five-point Likert scales from “strongly disagree” to “strongly agree.” Both items were dichotomized as “strongly agree” versus all other categories in order to identify variables associated with strong interest in these communication behaviors.

Intent to change health habits

Participants were asked an item from a population-based study of responses to genomic information (Kaphingst et al., 2012; McBride et al., 2009): “In thinking about the information from the doctor, are there any health habits that you would like to improve?” Participants were prompted to think about six health behaviors (i.e., diet, exercise, smoking, multivitamin use, taking a prescription, other). We created an index from 0–6 of the number of health habits they would intend to change, which was treated continuously in analysis.

Hypothesized mediating variables

Risk perception

Absolute risk perceptions were examined with one item (i.e., “Based on this information, do you think your chance of getting [disease] in your lifetime is…”), which was answered on a five-point Likert scale from “very low” to “very high”, and relative risk perceptions with the item “Based on this information, compared to most people your age and sex, would you say that you are…,” which was answered on a five-point scale from “a lot less likely” to “a lot more likely” to get the disease (Lipkus et al., 2000; Wertz, Sorenson, & Heeren, 1986). Risk perceptions were treated continuously in analysis.

Perceived control

Perceived control was assessed with one item “There is a lot I can do to prevent [disease],” with a five-point response scale from “strongly disagree” to “strongly agree” (Wang et al., 2009), which was treated continuously in analysis.

Worry

Disease worry was assessed with one item “After hearing the information from the doctor, how worried would you be about getting [disease] in your lifetime?”, which was answered on a five-point scale from “not at all” to “very” worried (Lipkus et al., 2000). Disease worry was treated continuously in analysis.

Predictive ability

The item “In general, how well do you think doctors can predict your chance of getting a disease based on your [genes/family history]?” was answered on a five-point scale from “not at all” to “completely” (Persky, Kaphingst, Allen Jr., & Senay, 2013). This item was treated continuously in analysis.

Participant characteristics

Gender, age, educational attainment, race, income, health insurance status, and family and personal history of diabetes and heart disease were assessed, and the Newest Vital Sign was administered to assess health literacy (Weiss et al., 2005).

Data analysis

In order to examine differences by race/ethnicity, analyses were limited to racial/ethnic groups with sufficient numbers of participants for analysis (i.e., White, Black, Hispanic), excluding 276 participants from other groups or who did not indicate their race/ethnicity. Participants who had a history of diabetes or heart disease and were randomized to a vignette based on that disease were also excluded (N=157), leaving an analytic sample of 1,057. We first examined descriptive statistics for all variables. To examine differences in dependent variables and hypothesized mediating variables across experimental groups and bivariate associations between race/ethnicity and dependent variables and hypothesized mediating variables, chi-squared tests were used for dichotomous variables and analysis of variance for continuous variables.

Multivariable logistic regression models (dichotomous outcomes) or linear regression models (continuous outcomes) were built to examine associations between race/ethnicity and dependent variables. Insurance status, income, education, age, gender, health literacy, and family history of diabetes and heart disease were tested for entry into the model and significant covariates were retained. We examined possible interactions between experimental condition and race/ethnicity in these models. To test mediation, the following associations were examined using regression analyses: (1) predictor and dependent variable; (2) predictor and hypothesized mediating variable; (3) hypothesized mediating variable and dependent variable, and (4) predictor and dependent variable controlling for hypothesized mediating variable (Baron & Kenny, 1986). Data were analyzed using SAS 9.4 (Cary, NC); statistical significance was assessed as p<0.05.

RESULTS

Characteristics of participants

As shown in Table 1, the majority of participants had no more than a high school degree (57.0%). Less than half (39.5%) had adequate health literacy. The mean age was 36.7 years. More than half (54.9%) reported having a family history of diabetes, and 35.1% a family history of heart disease. 35.3% identified as non-Hispanic White, 34.5% as non-Hispanic Black, and 30.2% as Hispanic. There were no significant differences across experimental groups by gender, education, health literacy, race/ethnicity, income, health insurance, family history or age.

Table 1.

Sociodemographic characteristics of respondents overall and by experimental vignette condition.

Characteristic Experimental condition
Overall Diabetes Heart Disease p-value
Family history Genetic test Family history Genetic test
N % N % N % N % N %
Female (N=1054) 735 69.7 185 72.3 180 68.7 188 68.6 182 69.5 0.782
Education level (N=1017) 0.385
 Less than high school 107 10.5 33 13.3 23 9.1 24 9.2 27 10.7
 High school degree/GED 473 46.5 110 44.2 114 44.9 136 51.9 113 44.8
 Some college 301 29.6 73 29.3 86 33.9 71 27.1 71 28.2
 College degree or higher 136 13.4 33 13.3 31 12.2 31 11.8 41 16.3
Health literacy (N=788) 0.151
 Inadequate 138 17.5 42 21.3 38 18.8 28 14.7 30 15.2
 Marginal 339 43.0 74 37.6 95 47.0 90 47.1 80 40.4
 Adequate 311 39.5 81 41.1 69 34.2 73 38.2 88 44.4
Race/Ethnicity (N=1057) 0.568
 Non-Hispanic Black 365 34.5 101 39.3 83 31.7 94 34.3 87 33.0
 Non-Hispanic White 373 35.3 88 34.2 99 37.8 94 34.3 92 34.9
 Hispanic 319 30.2 68 26.5 80 30.5 86 31.4 85 32.2
Income (N=903) 0.935
 <$20,000 358 39.7 93 42.7 94 40.2 88 38.4 83 37.4
 $20,000–$39,999 290 32.1 66 30.3 73 31.2 78 34.1 73 32.9
 ≥$40,000 255 28.2 59 27.1 67 28.6 63 27.5 66 29.7
Health insurance (N=1010) 0.564
 Have private insurance 166 16.4 41 16.7 32 12.7 44 17.2 49 19.1
 Have public insurance 377 37.3 95 38.8 100 39.5 94 36.7 88 34.4
 No insurance 467 46.2 109 44.5 121 47.8 118 46.1 119 46.5
Family history
 Diabetes (N=1029) 565 54.9 141 55.5 132 52.0 144 55.0 148 57.1 0.322
 Heart disease (N=1020) 358 35.1 85 33.9 98 38.9 88 34.1 87 33.6 0.543
M SD M SD M SD M SD M SD
Age (N=988) 36.7 13.6 36.7 14.2 35.8 12.9 37.8 14.3 36.4 12.7 0.424
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Effects of experimental factors

The effects of the experimental factors (i.e., type of genomic information, disease condition) were first examined. There were no significant differences across the four experimental groups for any of the dependent variables or for any of the variables hypothesized to act as mediators (Table 2). Because experimental factors were not associated with the dependent variables or hypothesized mediating variables, no further tests of mediation were conducted. No significant interactions were found between experimental condition and race/ethnicity (data not shown).

Table 2.

Responses to genomic risk information overall and by experimental vignette condition.

Experimental condition
Overall Diabetes Heart Disease p-value
Family history Genetic test Family history Genetic test
N % N % N % N % N %
Dependent variables 0.468
Interest in assessment (N=1044)
Very interested 468 44.8 124 49.0 115 44.4 115 42.6 114 43.5
Other responsesa 576 55.2 129 51.0 144 55.6 155 57.4 114 43.5
Discussion with doctor (N=1023) 0.918
Strongly agree 527 51.5 127 51.4 131 51.6 132 50.0 137 53.1
Other responsesb 496 48.5 120 48.6 123 48.4 132 50.0 121 46.9
Discussion with family member (N=1023)
Strongly agree 442 43.2 115 46.6 96 37.7 111 42.1 120 46.7 0.124
Other responsesb 581 56.8 132 53.4 159 62.4 153 58.0 137 53.3
M SD M SD M SD M SD M SD
Health habit sum (N=1041) 2.2 1.3 2.2 1.3 2.2 1.3 2.1 1.4 2.3 1.4 0.319
Hypothesized mediating variables
Absolute risk perception (N=1046) 2.9 1.0 3.0 1.0 2.8 1.0 2.9 1.0 3.0 1.0 0.119
Relative risk perception (N=1027) 2.7 1.0 2.7 1.0 2.7 1.0 2.8 1.1 2.6 1.0 0.302
Disease worry (N=1047) 2.9 1.3 3.1 1.3 3.1 1.3 3.0 1.3 3.0 1.3 0.852
Perceived control (N=1042) 3.9 1.2 3.9 1.2 3.8 1.3 3.8 1.3 4.0 1.2 0.234
Predictive ability (N=1041) 2.5 1.0 3.4 0.9 3.6 1.0 3.5 1.1 3.4 1.0 0.198
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a

Pretty/somewhat/a little bit/not at all interested.

b

Somewhat agree/neither agree nor disagree/somewhat disagree/strongly disagree.

Associations between race and ethnicity and dependent variables

Bivariate differences in the dependent variables by race/ethnicity were next examined. As shown in Table 3, being very interested in receiving the genomic assessment (χ2 =19.4, p<0.001), reporting a strong interest in discussing genomic information with family members (χ2 =7.3, p=0.026), and number of health habits respondents intended to change in response to genomic information differed significantly across racial/ethnic groups (F =13.5, p<0.001). Interest in discussing the genomic information with a doctor was not associated with race/ethnicity.

Table 3.

Responses to genomic risk information by racial and ethnic group.

Non-Hispanic Black Non-Hispanic White Hispanic p-value
N % N % N %
Dependent variables
Interest in assessment (N=1044) <0.001
Very interested 157 43.2 140 38.0 171 54.6
Other responsesa 206 56.8 228 62.0 142 45.4
Discussion with doctor (N=1023) 0.393
Strongly agree 192 54.2 182 49.2 153 51.2
Other responsesb 162 45.8 188 50.8 146 48.8
Discussion with family member (N=1023) 0.026
Strongly agree 163 45.9 139 37.7 140 46.8
Other responsesb 192 54.1 230 62.3 159 53.2
M SD M SD M SD
Health habit sum (N=1041) 2.0 1.3 2.5 1.4 2.1 1.2 <0.001
Hypothesized mediating variables
Absolute risk perception (N=1046) 3.0 1.3 3.0 1.4 2.9 1.1 0.106
Relative risk perception (N=1027) 2.7 1.0 2.8 0.9 2.7 1.0 0.146
Disease worry (N=1047) 3.1 1.3 2.8 1.2 3.3 1.3 <0.001
Perceived control (N=1042) 3.9 1.3 3.9 1.2 3.8 1.3 0.795
Predictive ability (N=1041) 3.4 1.0 3.4 0.9 3.6 1.0 0.001
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a

Pretty/somewhat/a little bit/not at all interested.

b

Somewhat agree/neither agree nor disagree/somewhat disagree/strongly disagree.

In multivariable models (see Table 4), Hispanics were more interested in receiving the genomic assessment than non-Hispanic Whites (OR=1.93; 95% CI: 1.34, 2.78) but there was no significant difference between non-Hispanic Blacks and non-Hispanic Whites. Respondents with marginal (OR=1.54; 95% CI: 1.12, 2.13) or limited (OR=1.85; 95% CI: 1.21, 2.83) health literacy had greater interest in receiving the assessment than those with adequate health literacy. Non-Hispanic Blacks (OR=1.78; 95% CI: 1.25, 2.52) and Hispanics (OR=1.85; 95% CI: 1.28, 2.67) had greater interest in discussing genomic information with family members than non-Hispanic Whites; health literacy was not significantly associated with this dependent variable. Non-Hispanic Blacks (OR=1.45; 95% CI: 1.03, 2.06) had greater interest in discussing the information with a doctor compared to non-Hispanic Whites, but there was no significant difference between Hispanics and non-Hispanic Whites or by health literacy. Both non-Hispanic Blacks (β= −0.41; 95% CI: −0.63, −0.19) and Hispanics (β= −0.25; 95% CI: −0.48, −0.02) intended to change fewer health habits compared with non-Hispanic Whites (see Table 5). Respondents with marginal (β= −0.47; p<0.001) or inadequate (β= −0.88; p<0.001) health literacy intended to change fewer health habits compared with those with adequate health literacy.

Table 4.

Associations between race/ethnicity and interest in genomic assessment, discussion with doctor and discussion with family members in multivariable logistic regression models.

Predictor variable Strong interest in assessment Discussion with doctor Discussion with family
OR 95% CI p-value OR 95% CI p-value OR 95% CI p-value
Base model n=781 n=764 n=765
Non-Hispanic Blacka 1.19 0.83 1.69 0.341 1.45 1.03 2.06 0.036 1.78 1.25 2.52 0.001
Hispanica 1.93 1.34 2.78 <0.001 1.26 0.88 1.82 0.207 1.85 1.28 2.67 0.001
Marginal health literacyb 1.54 1.12 2.13 0.005 0.95 0.69 1.31 0.765 0.94 0.68 1.30 0.711
Inadequate health literacyb 1.85 1.21 2.83 0.009 0.69 0.45 1.06 0.092 0.80 0.52 1.23 0.318
Disease worry added n=778 n=761 n=762
Non-Hispanic Blacka 1.10 0.76 1.58 0.623 1.38 0.96 1.97 0.079 1.70 1.19 2.44 0.004
Hispanica 1.67 1.14 2.44 0.008 1.12 0.77 1.63 0.557 1.67 1.14 2.43 0.008
Disease worry 1.58 1.39 1.78 <0.001 1.45 1.28 1.63 <0.001 1.44 1.27 1.62 <0.001
Marginal health literacyb 1.41 1.01 1.97 0.045 0.87 0.63 1.22 0.425 0.86 0.62 1.20 0.365
Inadequate health literacyb 1.58 1.01 2.47 0.044 0.60 0.38 0.93 0.022 0.70 0.45 1.09 0.111
Predictive ability added n=773 n=758 n=759
Non-Hispanic Blacka 1.25 0.85 1.82 0.257 1.52 1.07 2.18 0.021 1.87 1.28 2.62 0.001
Hispanica 1.76 1.19 2.61 0.005 1.19 0.82 1.73 0.365 1.73 1.18 2.52 0.005
Predictive ability 2.48 2.06 2.99 <0.001 1.50 1.28 1.75 <0.001 1.56 1.33 1.83 <0.001
Marginal health literacyb 1.33 0.94 1.88 0.111 0.87 0.62 1.21 0.401 0.85 0.61 1.19 0.351
Inadequate health literacyb 1.89 1.19 3.02 0.008 0.68 0.44 1.05 0.084 0.80 0.51 1.24 0.310
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a

Comparison category is non-Hispanic White.

b

Comparison category is adequate health literacy.

c

Very interested compared with pretty/somewhat/a little bit/not at all interested.

d

Strongly agree compared with somewhat agree/neither agree nor disagree/somewhat disagree/strongly disagree.

Table 5.

Association between race/ethnicity and number of health habits interested in changing in multivariable linear regression models.

Predictor variable β 95% CI p-value
Base model (n=779)
Non-Hispanic Blacka −0.41 −0.63 −0.19 <0.001
Hispanica −0.25 −0.48 −0.02 0.033
Marginal health literacyb −0.47 −0.67 −0.26 <0.001
Inadequate health literacyb −0.88 −1.15 −0.62 <0.001
Disease worry added (n=776)
Non-Hispanic Blacka −0.43 −0.66 −0.21 <0.001
Hispanica −0.31 −0.54 −0.07 0.010
Disease worry 0.13 0.05 0.20 <0.001
Marginal health literacyb −0.50 −0.71 −0.30 <0.001
Inadequate health literacyb −0.93 −1.20 −0.66 <0.001
Predictive ability added (n=770)
Non-Hispanic Blacka −0.40 −0.62 −0.17 <0.001
Hispanica −0.26 −0.50 −0.03 0.027
Predictive ability 0.09 0.00 0.19 0.049
Marginal health literacyb −0.51 −0.71 −0.30 <0.001
Inadequate health literacyb −0.89 −1.16 −0.62 <0.001
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a

Comparison category is non-Hispanic White.

b

Comparison category is adequate health literacy.

Effects of hypothesized mediating variables in associations with race/ethnicity

Disease worry (F=13.8, p<0.001) and predictive ability (F=7.0, p=0.001) were significantly associated with race/ethnicity (see Table 3). These variables were tested as potential mediators of the associations between race/ethnicity and dependent variables. As shown in Table 4, adding disease worry somewhat attenuated the difference between Hispanics and non-Hispanic Whites in interest in the genomic assessment, although Hispanics still had greater interest than Whites (OR=1.67; 95% CI: 1.14, 2.44). After adding disease worry, the difference between non-Hispanic Blacks and non-Hispanic Whites in interest in discussing the information with a doctor was no longer significant; disease worry was strongly associated with this dependent variable (OR=1.45; 95% CI: 1.28, 1.63). With increasing disease worry, respondents were more interested in the genomic assessment (OR= 1.58; 95% CI: 1.39, 1.78). Although disease worry had significant positive associations with discussing genomic information with family members (OR = 1.44; 95% CI: 1.27, 1.62) and with intended number of health habits to change (β= 0.13; 95% CI: 0.05, 0.20), the addition of this variable only slightly changed the magnitude and significance of the associations between race/ethnicity and the dependent variables.

Adding predictive ability somewhat attenuated the difference between Hispanics and non-Hispanic Whites in interest in the assessment, although Hispanics still had greater interest (OR=1.76; 95% CI: 1.19, 2.61). With increasing predictive ability, respondents were more interested in the assessment (OR= 2.48; 95% CI: 2.06, 2.99). After adding predictive ability, non-Hispanic Blacks (OR = 1.87; 95% CI: 1.28, 2.62) and Hispanics (OR = 1.73; 95% CI: 1.18, 2.52) still had greater interest in discussing genomic information with family members than non-Hispanic Whites. Predictive ability was positively associated with this dependent variable (OR = 1.56; 95% CI: 1.33, 1.83). Predictive ability was significantly associated with interest in discussing the information with a doctor (OR = 1.50; 95% CI: 1.28, 1.75) and intended number of health habits to change (β= 0.09; 95% CI: 0.00, 0.19), but adding this variable only slightly changed the magnitude of the associations between race/ethnicity and these dependent variables.

DISCUSSION

In this medically underserved population, no significant differences in responses to genomic risk information for common, chronic disease were observed across experimental groups defined by type of genomic information and disease condition. The study hypotheses of differences in responses were therefore not supported. Because the associations between experimental group and dependent variables were not significant, variables selected based on the CSM and prior literature were not tested as possible mediators. Additional research is needed to investigate how different types of genomic information are understood and represented among medically underserved populations. Individuals in underserved populations may perceive few differences between genetic test results and family history assessments due to a lack of familiarity (Hall & Olopade, 2005; Hughes, Fasaye, LaSalle, & Finch, 2003). Other domains of cognitive representations of the different types of genomic risk information suggested by CSM (i.e., identity, causes, consequences, timeline) (Marteau & Weinman, 2006) should be investigated. Alternatively, patients in medically underserved populations may focus less on how risk estimates are determined as much as their interpretation and implications. Significant differences by race/ethnicity in responses to genomic information were observed. In multivariable models, race/ethnicity was associated with strong interest in receiving a genomic assessment, discussing genomic information with family members and with a doctor, and intentions to change health habits in response to genomic information. The study findings suggest, therefore, that individuals’ race/ethnicity might be critical to consider in predicting responses to genomic risk information, even more than the type of genomic information.

Although data on responses to genomic information among racial and ethnic minority groups are limited (Alford et al., 2011; Kaphingst & McBride, 2010), research has shown that Black women perceive fewer health benefits to genetic testing than White women (Peters et al., 2004). Health care-related distrust has been shown to be higher among Blacks than Whites (Corbie-Smith, Thomas, & Geroge, 2002; Institute of Medicine, 2002), and researchers have suggested that this may extend to distrust of genetic information (Armstrong, Micco, Carney, Stopfer, & Putt, 2005), and concerns about misuse of this information (Singer et al., 2004; Sterling, Henderson, & Corbie-Smith, 2006) and racial discrimination based on genetic testing (Armstrong et al., 2005; Peters et al., 2004; Thompson, Valdimarsdottir, Jandorf, & Redd, 2003). Interestingly, in the present study, Hispanic ethnicity was not associated with interest in discussing genomic information with a doctor, and non-Hispanic Blacks and non-Hispanic Whites did not differ on interest in the genomic assessment. It is possible that this might relate to similar levels of trust in the community health centers across racial and ethnic groups. Further research is needed to examine differences across racial and ethnic groups in factors such as trust that might affect responses to genomic information. Other healthcare system barriers, such as insurance barriers, might also impact familiarity with and access and responses to genomic information. Some researchers have suggested that cultural differences in factors such as temporal orientation, spiritual beliefs, and beliefs about prevention might affect responses to genetic information for Blacks compared with Whites (Gregg & Curry, 1994; Peters et al., 2004), and studies are needed to investigate how these beliefs might affect responses to genomic risk information.

Among Hispanics, higher levels of acculturation have been associated with increased awareness of and more perceived benefits from genetic testing (Heck, Franco, Jurkowski, & Gorin Sheinfeld, 2008; Pagan et al., 2009; Sussner, Thompson, Valdimarsdottir, Redd, & Jandorf, 2009). Medical mistrust has been associated with attitudes about genetic testing (Thompson et al., 2003) and Hispanics have been shown to be more concerned about possible misuses of genetic information compared with Whites (Singer et al., 2004). In the present study, however, Hispanics were found to have greater interest in receiving a genomic assessment than non-Hispanic Whites. Other research has also found high levels of interest in genetic testing among Hispanics (Ramirez et al., 2006), although Hispanics may have less knowledge about genetic testing and may hold beliefs and attitudes conflicting with testing (Singer et al., 2004). The finding of high levels of interest in a genomic assessment therefore suggests the need for more research on attitudes toward, knowledge of, and uptake of genomic information to ensure that individuals are able to make informed decisions regarding the receipt of genomic information.

The findings from the present study add to the existing literature in a number of other ways. Researchers have suggested the importance of family coping with genomic risk information (Koehly et al., 2009; Koehly et al., 2008). In this study, non-Hispanic Blacks and Hispanics had greater interest in discussing genomic information with family members than non-Hispanic Whites, findings that support the potential for family-based disease prevention approaches with racial/ethnic minority groups (Kaphingst, Lachance, Gepp, D’Anna, & Rios-Ellis, 2011; Koehly et al., 2009). In addition, in this study, perceived predictive ability of the genomic information partially mediated the association between race/ethnicity and interest in the genomic assessment. Little research has examined the perceptions of individuals from different racial and ethnic groups about the characteristics of different types of genomic information; this may be an important line of future investigation in understanding differences in responses across groups.

The development of communication approaches for genomic information is a top priority for translational research in genomics (McBride, Bowen, et al., 2010). Researchers have previously highlighted the importance of developing culturally competent approaches (Hughes et al., 1997; Kinney et al., 2005; Pagan et al., 2009; Sussner et al., 2009), which is supported by the findings from this study. In addition, the greater interest in genomic assessments among those with limited health literacy indicates that health literacy will be critical to communicating about genomic risk and ensuring informed decision making about receiving genomic risk information. Although education has been associated with interest in genetic susceptibility testing (Alford et al., 2011), little prior research has examined how health literacy or numeracy impact responses to genomic risk information (Hurle et al., 2013; Lea, Kaphingst, Bowen, Lipkus, & Hadley, 2011). This is a critical research gap as genomic risk information reaches more diverse populations.

This study had a number of limitations that should be considered in interpreting the findings. Interest in genomic assessments may overpredict actual uptake. Intention measures may also differ from actual changes in health behaviors or communication behaviors. Vignettes were used rather than actual results from genetic testing or a family history assessment. Although the vignette methodology allowed the investigation of experimental factors with a randomized design (Cameron et al., 2012), responses to actual genomic information may differ. Because this study focused on risk of common, chronic disease, the increase in risk described in the vignettes was modest. Participants may have responded differently to a larger increase in risk. In addition, participants may have responded differently to risk information for a disease that was less familiar or had different risk factors. The examination of hypothesized mediating variables was limited by the cross-sectional design and measurement of only some aspects of individuals’ cognitive representations of the genomic risk information. Genomic knowledge was not assessed, and could be explored in future studies. Comprehension of genomic risk information by health literacy or numeracy could also be assessed in future studies, as could differences in responses by acculturation or language of preference.

Despite these limitations, the findings from the present study add to the understanding of responses to genomic information among medically underserved populations and indicate that the type of genomic information may be less important than factors associated with race/ethnicity. The roles of awareness, knowledge, attitudes and beliefs toward different types of genomic information will be critical to examine, and it will be important to explore what mechanisms underlie the relationships between race/ethnicity and responses to genomic information. Finally, the findings highlight the importance of developing communication strategies for genomic risk information that are effective for individuals from different racial and ethnic groups and with varying levels of health literacy.

Acknowledgments

The authors would like to thank the participants and team of data collectors. This work was supported by the Intramural Research Program of the National Human Genome Research Institute at the National Institutes of Health, the Alvin J. Siteman Cancer Center (P30CA91842), and the Barnes Jewish Hospital Foundation.

Footnotes

The authors have no conflicts of interest to disclose.

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