Cultural Diversity and Ethnic Minority Psychology 2016, Vol. 22, No. 1, 104 –113

© 2015 American Psychological Association 1099-9809/16/$12.00 http://dx.doi.org/10.1037/cdp0000034

Culture and Risk Assessments: Why Latino Americans Perceive Greater Risk for Diabetes Camille D. Basilio, Virginia S. Y. Kwan, and Michelle J. Towers

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Arizona State University Objective: Large ethnic disparities exist in health outcomes, yet little is known about the psychological mechanisms that underlie these differences. We propose that a key to understanding ethnic minority health is to recognize the cultural factors that influence perceived vulnerability to disease (PVD), specifically ethnicity and ethnic identification. In 3 studies, we examined how these cultural factors were associated with PVD to Type II diabetes, a highly prevalent disease among Latino Americans. We had 3 specific aims. The first was to examine ethnic group differences in PVD between European Americans and Latino Americans. The second was to examine potential psychological mechanisms that account for ethnic differences in PVD. The third was to examine the relationship between ethnic identification and PVD among Latino Americans. Method: Participants in all studies were young European American and Latino American adults and were from independent samples. In all 3 studies, participants completed the questionnaires online. Results: Study 1 found that Latino Americans as compared with European Americans have higher PVD to diabetes. Study 2 showed that perceived similarity to the typical person who gets diabetes and the number of reported family members with diabetes predicted the degree of PVD to diabetes. However, we found that the nature of the associations between these mechanisms and perceived risk differed by ethnic group. Study 3 examined what may be influencing perceived similarity for Latino Americans; we found ethnic identification is a significant factor. Discussion: Together, the present findings have broad implications for diabetes communication, education, and health campaigns. Keywords: culture, diabetes, ethnic identification, Latino Americans, perceived risk

ventative programs designed to reduce the occurrence of diabetes among Latino Americans (e.g., American Diabetes Association, CDC), research on psychological and cultural factors that may help prevent diabetes within this population is still limited. In this article, we begin with a brief review of the current state of diabetes among Latino Americans. Second, we provide an overview of the literature on PVD, ethnicity, ethnic identification, and psychological factors. Finally, we report three studies to address our research questions.

The Office of Minority Health at the Centers for Disease Control and Prevention (CDC) outlines dramatic health disparities across ethnic groups, including the disproportionate prevalence of Type II diabetes within minority populations (CDC, 2011). Previous studies show that cultural factors influence important outcomes for ethnic minorities, such as health-related behaviors and disease prevention (e.g., Bagley, Angel, Dilworth-Anderson, Liu, & Shincke, 1995; Betancourt & Flynn, 2009; Campos et al., 2008; Castro, Shaibi, & Boehm-Smith, 2010; Kiang et al., 2006; Yip, 2009). However, few studies have examined the dynamic influences of psychological and cultural factors on perceived vulnerability to disease (PVD). For example, do members of different ethnic groups perceive their risks differently? Within an ethnic group, what factors account for variability in perceived risks? In this research, we aim to answer these questions by exploring how cultural factors (i.e., ethnicity and ethnic identification) are associated with PVD. Specifically, the focus of this research is on the Latino American population, a group that is disproportionately affected by diabetes (CDC, 2011). Although there are many pre-

Diabetes Diabetes mellitus is the seventh leading cause of death overall and the fifth leading cause of death for Latino Americans in the United States (CDC, 2011, 2014). Diabetes can result in a number of serious health complications. Furthermore, those living with the disease need to monitor and maintain their blood glucose levels daily, and for some, diabetes is a lifelong disease. Here, we focus on Type II diabetes, which accounts for 90% to 95% of all cases (CDC, 2011). Although the exact causes and triggers of the disease are still unclear, scientists have identified several risk factors that may predispose certain individuals for acquiring the disease such as family history, lifestyle, and ethnicity (CDC, 2011; National Diabetes Education Program, 2013).

This article was published Online First March 16, 2015. Camille D. Basilio, Virginia S. Y. Kwan, and Michelle J. Towers, Department of Psychology, Arizona State University. Camille D. Basilio is now at the County of Orange Health Care Agency, Santa Ana, California. Michelle J. Towers is now at The Turkish Fulbright Commission, Sivas, Turkey. Correspondence concerning this article should be addressed to Camille D. Basilio. E-mail: [email protected]

Diabetes Among Latino Americans There are clear and dramatic ethnic disparities in diabetes prevalence. According to the CDC (2014), 12.8% of Latino Americans aged 20 and older have diabetes, as compared with 7.6% of 104

PVD TO DIABETES

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European Americans. In addition, an even greater percentage (32%) of the Latino American population has prediabetes (Goran, Lane, Toledo-Corral, & Weigensberg, 2008). Despite these daunting numbers, Type II diabetes can often be prevented or delayed by engaging in health behaviors that can reduce modifiable risk factors (e.g., diet and exercise), though there is some evidence to suggest that Latino Americans are less likely to engage in behaviors that can reduce these risks (e.g., Coronado, Thompson, Tejeda, Godina, & Chen, 2007). Nevertheless, a key component and important first step that may promote engaging in health preventative behaviors is awareness of one’s own risk.

Perceived Vulnerability to Disease (PVD) PVD is an individual’s perception of the likelihood of acquiring a particular disease (Duncan, Schaller, & Park, 2009). It is a subjective assessment of one’s own susceptibility and is an important construct in health-related research (Glanz, Rimer, & Su, 2005; Rosenstock, 1974, 1990). For example, the Health Belief Model posits that individuals will engage in health-protective behavior if they recognize that they are at risk for a disease (Brooks, Lee, Stover, & Barkley, 2011; Rosenstock, 1974; Ross, Ross, Rahman, & Cataldo, 2010). Previous research has shown that PVD can lead individuals to engage in health protective behaviors to prevent the disease (Aiken, Gerend, & Jackson, 2001; Brooks, Lee, Stover, & Barkley, 2009; Wilson, Lavelle, Greenspan, & Wilson, 1991). For example, people who express higher PVD to HIV/AIDS are more likely to take precautionary steps in their sexual behavior (e.g., Gerrard, Gibbons, & Bushman, 1996). Because of the critical role PVD plays in disease prevention, it is necessary to identify the factors that influence PVD assessments, particularly among young adults when they still have time to prevent or delay the onset of the disease. A number of personal factors may influence PVD. Previous research has shown that family history of a disease influences perceptions of one’s own risk (Aiken, Fenaughty, West, Johnson, & Luckett, 1995; Dickerson, Smith, Sosa, McKyer, & Ory, 2012). Disease characteristics, such as its perceived prevalence in the population, may also influence perceived risk (Cree, Lynch, Au, & Myers, 2009; Hiraki, Chen, Roberts, Cupples, & Green, 2009; Kasperson et al., 1988). In addition to these risk factors, ethnic group membership may be a salient consideration in PVD assessments for ethnic minorities (Arar et al., 2003). Though all these factors may be considered in PVD assessments simultaneously, it is possible that more weight is given to some factors than others.

Proposed Mediators In this research, we hypothesized that there are significant between-groups differences in PVD to diabetes reflecting diabetes prevalence disparities between European American and Latino American populations. More importantly, we aimed to examine potential psychological factors that link ethnicity to PVD. Psychological factors, such as cognitive heuristics, have been shown to influence PVD (Kershaw, Ethier, Niccolai, Lewis, & Ickovics, 2003; Weinstein, 1980, 1982; Weinstein & Klein, 1995). The heuristics of interest in this research were the representativeness and availability heuristics (Kahneman & Tversky, 1973). The availability heuristic is a cognitive shortcut used when making

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judgments of the likelihood of an event, based on the ease with which examples of the event come to mind (Kahneman & Tversky, 1973). Vicarious experiences can increase the perception of the likelihood of an event occurring to oneself. For instance, knowing someone who has diabetes will increase the availability of the disease, and in turn increase the perceived likelihood of acquiring the disease. There may be ethnic differences in the use of the availability heuristic. The high prevalence of diabetes within the Latino American population may be more likely to trigger the use of this heuristic. Latino Americans may encounter someone with diabetes more frequently than European Americans because of the prevalence of the disease within their population. The representativeness heuristic is a cognitive shortcut used in likelihood judgments of a particular event by comparing it with other events with comparable features (Kahneman & Tversky, 1973). For instance, perceiving oneself as similar to the typical person who gets a certain disease increases PVD (Lek & Bishop, 1995). Ethnic identification may be particularly relevant here. Latino Americans who identify strongly with their ethnic group may perceive other Latino Americans as more similar to them. Therefore, when they encounter other Latino Americans with diabetes, their perceived similarity may also increase their PVD. On the other hand, Latino Americans who do not identify or only weakly identify with their ethnic group may perceive themselves as quite different from Latino Americans in general. Thus comparing themselves with Latino Americans who have diabetes may not be associated with their PVD.

Ethnic Identification Ethnicity and ethnic identification are related constructs, though there are important distinctions between the two. A person’s ethnicity is derived from his or her cultural group of origin. Ethnicity is a categorical variable and is a person-background distinction of group membership. However, ethnic group membership alone is not indicative of one’s psychological functioning. According to Betancourt’s Model of Culture and Behavior (Betancourt & Flynn, 2009), cultural factors most relevant to the individual as well as related psychological processes are more direct determinants of behavior. An individual’s personal attachment to his or her culture may therefore be an important consideration in determining behavior. Ethnic identification reflects the individual’s attachment to one’s ethnic group, which defines his or her social identity (Phinney, 1990, 1992). More specifically, the construct of ethnic identification is comprised of, but not limited to, self-identification, engagement in culturally relevant behaviors (e.g., traditions and customs), affirmation, and belongingness (Phinney, 1992; UmañaTaylor, Gonzales-Backen, & Guimond, 2009). Affirmation and belongingness refer to a sense of pride and belonging to one’s group (Phinney, 1992). Ethnic identity achievement is another component researchers have studied in the past. This construct refers to the development of one’s ethnic identity via the exploration of the meaning of belonging to one’s group (Phinney, 1992). The exploration involved in developing a strong ethnic identity provides a strong foundation of self-knowledge from which personal decisions can be made with confidence (Smith & Silva, 2011).

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Ethnic identification is central to psychological functioning (Phinney, 1990). The degree to which one identifies with his or her ethnic group can be a fundamental part of one’s everyday life and can color perceptions, beliefs, and behavior. Strong ethnic identification has been demonstrated as important for overall psychological well-being among ethnic minorities (Kiang, Yip, GonzalesBacken, Witkow, & Fuligni, 2006; Smith & Silva, 2011). However, it is important to note that members of one ethnicity do not form a single homogenous group and will have varying degrees of identification with their group. As such, two individuals can have the same ethnicity, yet have different degrees of ethnic identification. It is also worth noting that though ethnicity and ethnic identification are related to the concept of culture, these are separable constructs. Culture refers to socially agreed upon information that is transmitted via members of the social group (Heine, 2008). In contrast, ethnicity and ethnic identification refer to the characteristics that distinguish groups from one another and the degree to which these characteristics hold meaning for the individual’s identity, respectively. In this research, we propose that one’s ethnicity and ethnic identification may influence perceived disease risk. It is essential to highlight that ethnic-related explanations for disparities are insufficient without specifying the pathways with which cultural factors exert their influences on outcomes (Hruschka, 2009). Thus, we focus on examining prospective underlying mechanisms by which ethnicity and ethnic identification may be systematically associated with PVD. It is likely that individuals may consider their ethnic group membership when assessing their risk for diabetes. These considerations may account for between-ethnic group differences in PVD. For example, ethnicity may serve as a cue for increased risk for Latino Americans if they are aware of the prevalence of the disease within their group. The variability in degrees of ethnic identification within an ethnic group may also be a key factor in explaining within group variability in PVD assessments. To recapitulate, this research has three specific goals. The first is to examine ethnic group differences in PVD assessments. The second is to examine potential psychological factors that account for ethnic differences in PVD. The third is to examine the association of ethnic identification to PVD among Latino Americans. To test these goals, we conducted three studies with three independent samples.

Study 1: Group Level Differences in PVD In Study 1, we evaluated PVD judgments of Latino and European American participants to diabetes. We also measured their PVD to two control diseases, breast cancer and influenza. Breast cancer for female participants was included because this disease has similar risk factors associated with it as diabetes, such as ethnicity, family history of the disease, and lifestyle choices. Furthermore, like diabetes, breast cancer is a severe disease. However, unlike diabetes, it has a higher prevalence in European Americans as compared to Latino Americans (National Cancer Institute, 2012). Influenza was included because it represents a different type of illness: a less severe and contagious disease. The risk factors for acquiring influenza also do not include ethnicity (CDC, 2013). The goals of this study were to (a) examine whether Latino Americans report higher PVD to diabetes than European

Americans and (b) examine whether Latino Americans have higher PVD to diseases in general than European Americans, or whether ethnic differences in PVD are disease-specific. We hypothesized that Latino participants would report higher PVD to diabetes than European Americans, and this higher level of PVD would be specific to diabetes but not to the other two diseases.

Method Participants. Three hundred six undergraduate students from a large Southwestern university participated in the study in exchange for course credit. Participants were recruited from an introductory psychology course. They were excluded from the study if they took less than 3 minutes or more than 60 minutes to complete the study (25 participants excluded). These exclusion criteria were established because pilot testing of the questionnaires suggested that participants should take approximately 15 to 20 minutes to complete the entire study. The data showed that participants took an average of 15 minutes. In addition, participants were excluded in the analyses if they did not self-identify as either European or Latino American (46 participants excluded). Because of the heterogeneity of ethnicity within this excluded sample, there was insufficient power to detect any between group differences among them, and thus we only report results from Latino and European American participants. For the following analyses, we had a total of 180 European American (87 men and 93 women) and 55 Latino American (24 men and 31 women) participants with a median age of 19.28 years. Sex of participants did not differ by ethnicity, ␹2(1, n ⫽ 235) ⫽ 0.37, p ⫽ .54, and it did not influence the significant patterns of results. The majority of our participants identified as either upper-middle class (41% of European Americans, 26% of Latino Americans) or middle-class (38% of European Americans, 52% of Latino Americans). The reported results remain unchanged if participants who were excluded based on the time duration criteria were included in the analyses. Materials and procedures. The study was conducted online and was approved by IRB. Participants provided their informed consent and completed a set of PVD questionnaires related to diabetes, breast cancer (female participants only), and influenza. Participants’ PVD to each disease was assessed by the following questions adapted from existing PVD scales (Aiken et al., 1995; Dolan, Lee, & McDermott, 1997; Gerend, Aiken, West, & Erchull, 2004): “What do you believe is the chance that you will develop [disease] in your lifetime (1 ⫽ very low chance to 6 ⫽ very high chance)?” and “How susceptible do you feel you are to [disease] in your lifetime (1 ⫽ not at all susceptible to 6 ⫽ very susceptible)?” These two items were highly correlated, r(230) ⫽ .92, p ⬍ .001, for diabetes (␣ ⫽ .95), r(120) ⫽ .88, p ⬍ .001, for breast cancer (␣ ⫽ .94), and r(231) ⫽ .72, p ⬍ .001 for influenza (␣ ⫽ .83). Therefore, we created a composite PVD score for each disease by averaging the responses to these two items as was done in previous studies. Participants also indicated their ethnicity as part of a larger demographic questionnaire. Analysis plan. To examine whether there were mean differences in PVD between Latino Americans and European Americans to diabetes, breast cancer, and influenza, we conducted a series of independent t tests.

PVD TO DIABETES

Results The results of the study showed that Latino Americans (M ⫽ 2.74, SD ⫽ 1.30) reported significantly higher PVD to diabetes than European Americans (M ⫽ 2.04, SD ⫽ 1.11), t(232) ⫽ ⫺3.91, p ⬍ .001, Cohen’s d ⫽ .58. However, PVD to breast cancer among Latino American (M ⫽ 2.80, SD ⫽ 1.10) and European American (M ⫽ 2.97, SD ⫽ 1.21) women did not differ, t(121) ⫽ .70, p ⫽ .49. European Americans (M ⫽ 3.65, SD ⫽ 1.36) reported higher PVD to influenza than Latino Americans (M ⫽ 3.05, SD ⫽ 1.33), t(233) ⫽ 2.90, p ⫽ .004, Cohen’s d ⫽ .45.

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Discussion These findings suggest that Latino Americans do not have higher PVD than European Americans in general, but that they make disease-specific judgments when evaluating their risks. These disease-specific evaluations may reflect some knowledge of actual disease prevalence of diabetes within their ethnic group. These findings are consistent with our hypothesis.

Study 2: PVD and Psychological Mechanisms Study 1 revealed that there are ethnic differences in PVD to diabetes. Study 2 aimed to examine potential mechanisms that may mediate the relationship between ethnicity and PVD assessments. Ethnic differences in PVD assessments found in Study 1 may be attributable to differential information used when making PVD assessments. We hypothesized that the availability and representativeness heuristics will account for the association between ethnicity and PVD.

Method Participants. A total of 125 undergraduate students from a large Southwestern university participated in the study in exchange for course credit. We used the same exclusion criteria as Study 1. In total, 58 European Americans (29 men and 29 women) and 36 Latino Americans (20 men and 16 women) with a median age of 19.50 years met these criteria. The majority of our participants identified as either upper-middle class (40% of European Americans, 25% of Latino Americans) or middle class (36% of European Americans, 36% of Latino Americans), The reported results remain unchanged if participants who were excluded based on the time duration criteria were included in the analyses. Materials and procedure. The study was conducted online and was approved by IRB. Participants provided their informed consent and took the PVD questionnaire for diabetes (␣ ⫽ .96) as was used in Study 1. To measure the availability heuristic, participants responded to the following questions: “Do you have any family members who have diabetes? If so, how many?” and “Do you have any friends who have diabetes? If so how many?” with a response scale of 0 ⫽ none, 1 ⫽ 1–2 people, 2 ⫽ 3–5 people, 3 ⫽ 6 – 8 people, 4 ⫽ more than 8 people. These questions were adapted from previous studies as measures of the availability heuristic (Weinstein, 1980; Gerend et al., 2004) and were analyzed separately. To measure the representativeness heuristic, participants responded to the question “How similar do you believe you are to the typical man (woman) who gets diabetes?” on a 1 (not at all) to 6 (very) scale (Gerend et al., 2004; Weinstein, 1980, 1982).

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Participants also indicated their ethnicity, sex, and SES as part of a larger demographic questionnaire. Analysis plan. Independent t tests were conducted to examine whether the mean differences in PVD to diabetes between Latino Americans and European Americans were replicated. Chi-square (i.e., ordinal data) and t tests were conducted to examine whether there were ethnic group differences in number of family members with diabetes and perceived similarity. Correlational analyses were conducted to examine the association between our proposed mediators, PVD, and ethnicity. Our proposed mediation model was examined with structural equation modeling (SEM) using Mplus 7.11 software (Muthén & Muthén, 2007) with maximum likelihood estimation with robust standard errors to handle our ordinal variable mediator variable (i.e., number of family members with diabetes). This approach takes into account non-normal distributions (Muthén & Muthén, 2007). We examined our proposed mediation model using bootstrapping procedures using 3000 resamples (MacKinnon, Fairchild, & Fritz, 2007). For the exogenous variable of ethnicity, European Americans were coded as “0,” and Latino Americans were coded as “1.” Both mediators were simultaneously entered in the model and were allowed to correlate. Conventional standards indicate that model fit is considered good if the Comparative Fit Index (CFI) is greater than or equal to .95 (greater than or equal to .90 for adequate fit), the Root Mean Square Error of Approximation (RMSEA) is less than or equal to .06 (less than or equal to .08 for adequate fit), and the Standardized Root Mean Square Residual (SRMR) is less than or equal to .08 (less than or equal to .10 for adequate fit; Hu & Bentler, 1999). Regression analyses were also conducted to examine how each of the psychological factors predicted PVD within each ethnic group.

Results First, we examined whether Latino and European Americans showed differences in their PVD assessments. Latino Americans (M ⫽ 2.58, SD ⫽ 1.34) as compared with European Americans (M ⫽ 2.00, SD ⫽ 1.20) had significantly higher PVD to diabetes, t(91) ⫽ ⫺2.19, p ⫽ .03, Cohen’s d ⫽ .46. These findings replicated the results of Study 1. Additionally, Latino Americans reported having significantly more family members with diabetes, ␹2 (4, n ⫽ 93) ⫽ 10.89, p ⬍ .05. The following is the distribution: 44.8% of European Americans and 22.2% of Latino Americans reported having no family members with diabetes; 44.8% of European Americans and 50% of Latino Americans reported having 1 or 2 family members with diabetes; 8.6% of European Americans and 16.7% of Latino Americans reported having 3 to 5 family members with diabetes; 0% of European Americans and 5.6% of Latino Americans reported having 6 to 8 family members with diabetes; 0% of European Americans and 5.6% of Latino Americans reported having more than 8 family members with diabetes. One European American did not respond to this question. In addition, Latino Americans (M ⫽ 2.71, SD ⫽ 1.23), as compared with European Americans, (M ⫽ 2.28, SD ⫽ .98), reported marginally higher perceived similarity to the typical person who gets diabetes, t(90) ⫽ ⫺1.87, p ⫽ .064.

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Second, we examined whether the participants’ responses on the heuristics questions were associated with PVD to diabetes. The results showed that the number of family members with diabetes, r (93) ⫽ .48, p ⬍ .001, and perceived similarity, r(92) ⫽ .50, p ⬍ .001, were significantly correlated with PVD. However, the number of friends with diabetes was not significantly correlated with PVD, r(92) ⫽ ⫺.03, p ⫽ .80 (see Table 1 for intercorrelations of these variables by ethnicity). This nonsignificant finding may be attributable to our younger sample not reporting having many friends with diabetes (50% reporting no friends with diabetes and 41.3% reporting having only 1 or 2 friends with diabetes), and as a consequence, their vicarious experience of diabetes through friends is quite limited. This is an important consideration that we will further discuss in the discussion section. As such, subsequent analyses will only include participants’ reports of number of family members who have diabetes and perceived similarity to the typical person who gets diabetes. We first tested our model controlling for gender and SES. However, results showed that these demographic variables had no significant effects, and as such were subsequently removed from our final model to make the model more parsimonious. The results of our final model showed that our proposed mediation model fit the data well, ␹2(4) ⫽ 4.705, p ⫽ .32, CFI ⫽ .99, RMSEA ⫽ .04 (.00, .17), and SRMR ⫽ .05. The results also supported our hypothesis. We reported the unstandardized coefficients (see Figure 1 for standardized coefficients). Ethnicity was significantly associated with the number of family members with diabetes (a1 ⫽ .20, p ⬍ .01, Sa1 ⫽ .07), and was marginally associated with perceived similarity to the typical person who gets diabetes (a2 ⫽ .16, p ⫽ .06, Sa2 ⫽ .08). The mediators of number of family members with diabetes (b1 ⫽ .47, p ⬍ .01, Sb1 ⫽ .16) and perceived similarity to the typical person who gets diabetes (b2 ⫽ .42, p ⬍ .01, Sb2 ⫽ .13) were significantly associated with PVD. Next, we examined the total, direct, and indirect paths with 95% bias-corrected bootstrap CI. The total effect of ethnicity on PVD (c ⫽ .19, p ⬍ .05, Sc ⫽ .09, CI [.01, .37]) was reduced to zero (c’ ⫽ .04, p ⫽ .65, Sc’ ⫽ .08, CI [⫺.12, .21]). The total indirect effect was also significant (a1b1 ⫹ a2b2 ⫽ .16, p ⬍ .01, S a1b1 ⫹ a2b2 ⫽ .06, CI [.06, .27]). The specific indirect effect via number of family members was significant (a1b1 ⫽ .09, p ⬍ .05, S a1b1 ⫽ .04, CI [.02, .19]). However, the specific indirect effect via perceived similarity was only marginally significant (a2b2 ⫽ .07, p ⫽ .09, Sa2b2 ⫽ .04, CI [⫺.00, .15]). This finding supports our hypothesis that the ethnic differences in PVD are accounted for by the mediators in the model. The next step was to examine how the mediators from the model above function within each ethnic

group. We conducted multiple regression analyses using the perceived similarity and number of family members with diabetes as predictors of PVD separately for each ethnic group. The results showed that for European American participants, the number of family members with diabetes was a significant predictor of PVD (b1 ⫽ .84, p ⬍ .001, Sb1 ⫽ .23) but perceived similarity was not (b2 ⫽ .24, p ⫽ .11, Sb2 ⫽ .15). The opposite pattern was observed for Latino American participants. The number of family members was not a significant predictor of PVD (b1 ⫽ .32, p ⬍ .12, Sb1 ⫽ .20), but perceived similarity was (b2 ⫽ .54, p ⬍ .01, Sb2 ⫽ .16).

Discussion The results of Study 2 supported our hypothesis and suggest that the differences in PVD assessments between the two ethnic groups is mediated by the number of family members with diabetes and perceived similarity. It is important to note that the availability heuristic was operationalized using the number of family members with diabetes. However, this measure of the heuristic is confounded with one’s biological risk for diabetes. Thus, it is not clear whether it is indeed the availability heuristic influencing PVD judgments, whether participants were simply considering their biological risk, or whether it is a combination of both. What is clear, however, is that the number of family members with diabetes seems to be a more important factor in PVD judgments for our European American participants than Latino American participants. The results of Study 2 suggest that European American and Latino American participants place differential emphasis on each of these factors and this accounts for differences in PVD assessments. European American participants place greater emphasis on the number of family members with diabetes, perhaps focusing more on one’s biological risk factor or the salience of the disease. Whereas Latino American participants may place greater emphasis on how similar they feel they are to the prototypical person with diabetes, perhaps placing a greater emphasis on a perceived shared culture with the prototypical person with diabetes. We realize that it is ideal to run multigroup analyses to examine how this model varies by ethnicity. However, because of our small sample size, we were unable to do so in our study. Future studies should further investigate these ethnic differences with larger sample sizes.

Study 3: The Role of Ethnic Identification Study 2 revealed that between-groups ethnic differences in PVD to diabetes may be attributable in part to the differential informa-

Table 1 Correlation of PVD to Number of Family Members and Friends With Diabetes, and Perceived Similarity by Ethnicity Variable PVD to diabetes Number of family members with diabetes Number of friends with diabetes Perceived similarity

PVD to diabetes 1

Number of family members with diabetes ⴱ

.47ⴱⴱⴱ .12 .41ⴱⴱ

.34 1 .17 .45ⴱⴱⴱ

Number of friends with diabetes

Perceived similarity

⫺.18 ⫺.19 1 .18

.55ⴱⴱ .17 ⫺.14 1

Note. Spearman’s rho correlation was used to correlate the ordinal variables of number of family members and friends with diabetes with other variables. Upper diagonal are correlations for Latinos, lower diagonal are correlations for European Americans. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01. ⴱⴱⴱ p ⬍ .01.

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Number of Family Members with Diabetes .20**(.33)

.47**(.32)

Total: .19* (.22)/ Direct: .04 (.04) Ethnicity

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.16+(.21)

PVD to Diabetes

.42**(.36)

Perceived Similarity to the Typical Person Who Gets Diabetes

Figure 1. Mediation model showing the association of ethnicity with PVD to diabetes as mediated by number of family members with diabetes and perceived similarity to the typical person who gets diabetes. Parameter estimates reported are unstandardized (standardized) coefficients. Significant paths are indicated with asterisks (⫹ p ⬍ .10, ⴱ p ⬍ .05, ⴱⴱ p ⬍ .01).

tion used by Latino and European Americans in risk assessments. Given that there is a greater prevalence of diabetes among Latino Americans, the prototypical person with diabetes may conjure up the image of a Latino American person. This may be attributable to the prevalent health campaigns from health resources (e.g., NIH, CDC, ADA) that have emphasized the prevalence of diabetes among Latino Americans. Diabetes has become associated with the Latino ethnicity (Montoya, 2007; Montoya, 2011), perhaps solidifying the perception that the prototypical diabetic is Latino American. Exposure to health information and campaigns may explain the observed ethnic differences in perceived disease risk. Nevertheless, the present study aimed to explore whether there are other factors that influence within-group differences in PVD among Latino Americans. We expected that ethnic identification with the Latino American culture would be associated with PVD. Ethnic identification can potentially influence PVD in multiple ways. First, the more one identifies with his or her ethnic group, the more likely he or she is to follow cultural norms of that ethnic group, which include dietary and exercise practices. Second, ethnic identification may influence perceived similarity to members of one’s ingroup. The more strongly individuals identify with the Latino American culture, the more similar they will perceive themselves to be to the typical Latino American. We hypothesized that as ethnic identification increases, so would PVD to diabetes. In addition, we hypothesized that this association would be mediated by perceived similarity to the typical person who gets diabetes.

Method Participants. Fifty-two self-identified Latino American undergraduate students from a large Southwestern university participated in the study in exchange for course credit. The same exclusion criteria from Studies 1 and 2 were used for this study. A total of 45 (22 men and 23 women) participants with a median age of 18.43 years met these criteria and were included in the analyses. The reported results remain unchanged if the seven Latino American participants who were excluded based on the time duration criteria were included in the analyses.

Materials and procedure. The study was conducted online and was approved by IRB. Participants provided their informed consent and completed the PVD and the perceived similarity questionnaires as described in Studies 1 and 2. In addition, participants also completed the 14-item Multigroup Ethnic Identification Questionnaire (MEIM; Phinney, 1992) to assess their degree of ethnic identification. This is a widely used measure of ethnic identification with three subscales. The first is affirmation and belonging (5 items; ␣ ⫽ .82), which assesses one’s sense of belonging to, attitudes toward, and pride in one’s ethnic group (e.g., “I am happy that I am a member of the group that I belong to.”). The second subscale is ethnic identity achievement (7 items; ␣ ⫽ .81), which assesses the degree of exploration of the meaning of one’s ethnicity (e.g., “I have a clear sense of my ethnic background and what it means for me.”). The third subscale is ethnic behaviors (2 items; ␣ ⫽ .33), which assesses involvement in cultural traditions and activities with other members of one’s ethnic group (e.g., “I participate in practices of my own group, such as special food, music, or customs”). It is worth noting that Phinney (1992) stated the reliability of the ethnic behavior subscale may not be accurately calculated given that it only contains two items. The response scale used for all subscales ranged from 1 ⫽ strongly disagree to 5 ⫽ strongly agree, with higher scores indicating a higher degree of ethnic identification. The affirmation and belonging subscale was significantly correlated with the ethnic identity achievement subscale, r(45) ⫽ .61, p ⬍ .001, but not with the ethnic behaviors subscale, r(45) ⫽ .24, p ⫽ .12. The ethnic behaviors subscale was significantly correlated with the ethnic identity achievement subscale, r(45) ⫽ .33, p ⫽ .03. Because we had no a priori hypotheses for how each subscale will be associated with PVD and perceived similarity, we used the total ethnic identification score (␣ ⫽ .86), which was the composite of all three subscales. The pattern of results does not change when each subscale was examined separately. Participants also indicated their ethnicity and gender as part of a larger demographic questionnaire.

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Analysis plan. Correlational analyses were conducted to examine how our variables of interest were associated with each other. The same procedures as Study 2 were implemented to examine the mediation model. We also conducted exploratory analyses examining the mediation model for the ethnic behaviors, achievement, and affirmation subscales separately.

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Results The results of the study showed that participants had moderate levels of ethnic identification (M ⫽ 3.53, SD ⫽ .55), and scored below the midpoint on PVD (M ⫽ 2.53, SD ⫽ 1.45), and perceived similarity (M ⫽ 2.54, SD ⫽ 1.14). Correlational analyses revealed that ethnic identity was significantly correlated with perceived similarity, r(45) ⫽ .45, p ⫽ .002, and PVD to diabetes, r(45) ⫽ .40, p ⫽ .007. Perceived similarity was also significantly correlated to PVD, r(45) ⫽ .71, p ⬍ .001. We first tested our model controlling for gender and found that it had no significant effects. As such, we removed it from our final model. The final model results supported our hypothesis (See Figure 2). We were unable to test model fit because the model was fully saturated. Ethnic identification was significantly associated with perceived similarity to the typical person who gets diabetes, (a ⫽ .93, p ⬍ .001, Sa ⫽ .27). In addition, perceived similarity to the typical person who gets diabetes was significantly associated with PVD, (b ⫽ .84, p ⬍ .001, Sb ⫽ .16). Next, we examined the total, direct, and indirect paths with bootstrapping 95% CI. The total effect of ethnic identification on PVD (c ⫽ 1.04, p ⬍ .01, Sc ⫽ .31, CI [.41, 1.66]) was reduced to zero (c’ ⫽ .26, p ⫽ .36, Sc’ ⫽ .28, CI [⫺.24, .82]). The indirect effect was also significant (ab ⫽ .79, p ⬍ .001, Sab ⫽ .27, CI [.23, 1.37]). This finding supports our hypothesis that the association between the degree of ethnic identification and PVD was accounted for by perceived similarity to the typical person who gets diabetes. The exploratory analyses, which tested the mediation model using each individual subscale showed that the ethnic behaviors, achievement, and affirmation subscales all had positive indirect effects on PVD via perceived similarity. However, only two of the three factors— ethnic behaviors and achievement— had significant total effects, that is, the combined direct and indirect effects, (.36, p ⬍ .01 and .38, p ⬍ .001, respectively). Affirmation/belonging did not have a significant total effect (.23, p ⫽ .10). Similarly, ethnic behaviors and achievement also had significant indirect effects, that is, the mediated effect via perceived similarity, (.18,

p ⫽ .07 and .31, p ⬍ .001, respectively) and affirmation had a marginally significant indirect effect (.20, p ⫽ .07). The nonsignificant effects of affirmation may be in part due to our small sample size.

Discussion The results of Study 3 suggest that ethnic identification plays an important role in PVD assessments for Latino Americans: The more Latino Americans identified with their ethnic group, the more similar they felt they are to the typical person who gets diabetes. This may be attributable to the general knowledge Latino Americans have about the higher incidence rates of diabetes within their group. This knowledge may lead to the perception that the typical person who gets diabetes is Latino American. However, only those individuals with high degree of ethnic identification may perceive similarity between themselves and the typical person who has diabetes.

General Discussion The results of our three studies showed that cultural factors, such as ethnicity and ethnic identification, are associated with PVD assessments. In addition, we found that these associations can be explained by psychological factors. For European Americans, the number of family members with diabetes was critical in their PVD assessments. For Latino Americans, their perceived similarity to the typical person who gets diabetes was more critical in their PVD assessments. Furthermore, within the Latino American population, the degree of ethnic identification is strongly related to PVD via perceived similarity. The results of these studies have implications for both theory and application. The present research highlights the need for a closer examination of how cultural factors influence disease risk assessments. In particular, cultural factors are closely linked to between- and within-group differences in PVD assessments for ethnic minorities. Particularly for younger individuals who can still prevent or delay the onset of diabetes, understanding how they form their judgments is very important in promoting health related behaviors. This research can inform these individuals and their health care providers on how they are determining their risk—and subsequent protective health behaviors—as well as identify at-risk populations. There may also be vulnerable populations within the Latino American population—those who do not identify with their

Perceived Similarity to the Typical Person Who Gets Diabetes .84 (.67)***

.93 (.45)***

Ethnic Identification

Total: 1.04 (.40)** / Direct: .26(.10) PVD to Diabetes

Figure 2. Mediation model showing the association of ethnic identification on PVD to diabetes as mediated by perceived similarity to the typical person who gets diabetes. Parameter estimates reported are unstandardized (standardized) coefficients. Significant paths are indicated with asterisks (ⴱⴱ p ⬍ .01, ⴱⴱⴱ p ⬍ .001).

PVD TO DIABETES

ethnic group. They may underestimate their actual risk for diabetes and may potentially minimize risk factors associated with an ethnic group with which they do not identify. Intervention programs and communication campaigns designed to target these specific individuals can promote awareness of their potential heightened risk and consequently encourage engagement in health protective behaviors. Among young Latino Americans, this research has the added benefit of helping members of this population recognize their risk and develop these health protective behaviors earlier in their lifetime.

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Limitations and Future Directions This research focuses on the young adult population because a better understanding of how this population perceived their disease risk may help prevent or delay the onset of the disease. However, the psychological processes underlying PVD may differ across age groups. Future research should examine whether the findings of these studies can be replicated in an older sample. An older sample can also provide a more effective test of the availability heuristic through asking about the number of friends with diabetes. We predict that this population would report having more friends with diabetes because age is one of the risk factors associated with diabetes. Examining the association of the number of friends with diabetes with PVD can help tease apart whether it is the salience of the disease or the biological risk factor that is being considered in PVD judgments. Examination of how these considerations are made among family members can provide insight to another potential source of heightened PVD as well as a point to consider for intervention beyond just young individuals. Future research should also replicate our findings related to ethnic identification. Our findings suggest that all three factors of ethnic identification have positive relationships with perceived similarity with people with diabetes. Future research could ascertain whether specific elements of ethnic identity are most associated with perceptions of similarity as well as PVD. For example, the affirmation/belonging subscale could be more related to one’s perceived similarity to someone with diabetes as it assesses one’s pride and attachment to the group, which may increase perceptions of similarity with members of that group. However, the ethnic behaviors subscale may be associated with one’s perceived similarity if they perceive that they engage in similar behaviors (e.g., same diet and exercise habits) to the person with diabetes. Similarly, ethnic identity achievement may influence perceived similarity because this measures one’s exploration and commitment to one’s ingroup. As such, more knowledge of what it means to be part of one’s ingroup, in this case, of being Latino, may also lead to the knowledge that they are more likely to get diabetes than their European American counterparts. Therefore, it will be fruitful to examine how each of these elements of ethnic identity may differentially be associated with perceived similarity and PVD.

Conclusions A key component in trying to reduce health disparities in ethnic minority populations is to understand how factors related to one’s ethnicity and ethnic identity influence PVD assessments. The studies presented highlighted that ethnic membership and identification are important considerations in health-related research and

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may be instrumental in PVD assessments for ethnic minority populations. Furthermore, our studies examined how these processes manifest in young Latino American adults—a population that is at risk for acquiring diabetes, but still have opportunities to modify their behavior to reduce risk. Together, the present findings have broad implications for diabetes communication, education, and health campaigns. Health campaigns and intervention programs should consider the degree to which individuals identify with their ethnic group when educating individuals about their risk. Furthermore, those who do not have a high degree of identification with their ethnic group may underestimate their risk and may miss opportunities to engage in health protective behaviors. Identification of these individuals could therefore alert health care providers to make these individuals aware of their risk and engage in more health protective behaviors. Additionally for young Latino Americans, those who still have opportunities to prevent or delay the onset of diabetes, it may be helpful to go beyond assessing their perceived risk to the disease. Health care providers should educate them early on about how the lifestyle choices they make now (e.g., diet and exercise) can significantly reduce their risk for the disease in the future.

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