Perceived Risk of Developing Diabetes in Early Adulthood: Beliefs about Inherited and Behavioral Risk Factors across the Life Course

Abstract

This study aimed to determine how college students perceive their risk of developing diabetes over their life course, with specific emphasis on their beliefs about the influence of inherited versus behavioral risk factors. A bivariate ordered probit regression model was used to simultaneously predict perceived risk for 10-year absolute risk of diabetes and lifetime absolute risk of diabetes. Ten-year and lifetime absolute risk were both increased when the respondent self-identified with a race/ethnicity other than non-Hispanic white (β = 0.42, p < .001 and β = 0.33, p = .004, respectively), and when the respondent had an increasing number of family members with diabetes (β = 0.33, p < .001 and β = 0.45, p < .001, respectively). Beliefs linking behavioral risk factors to perceived risk of developing diabetes across the life course were not statistically significant. The absence of significant association between perceptions of behavioral risk as factors for developing diabetes and perceived risk for diabetes over the life course supports the need for educational interventions about behavioral and genetic causes of diabetes among the college-aged population.

Keywords

diabetes late adolescence risk factors risk perception

Introduction

Diabetes is a major health concern in the United States and throughout the world (Bruno and Landi, 2011). In the United States, diabetes is the seventh leading cause of death contributing to over 230,000 deaths each year (Molinaro, 2011). It is therefore concerning that diabetes incidence is projected to grow from eight cases per 100,000 in 2008 to 15 cases per 100,000 by 2050 (Boyle et al., 2010). If current incidence and mortality trends continue, diabetes prevalence will reach 33 percent by 2050 (Boyle et al., 2010). Diabetes also has a high financial cost to health systems throughout the world (Clarke et al., 2010). In the United States, these costs are predicted to exceed $190 billion by 2020 (Molinaro, 2011).

Trends in the rate of diabetes prevalence continue to exhibit racial and ethnic disparities. The age and sex adjusted prevalence rate of diagnosed diabetes among African-Americans (11.0%) and Hispanics (11.0%) is higher than among non-Hispanic whites (5.5%) (Zhang et al., 2009). In addition, the trend of earlier onset of diabetes is becoming a worldwide concern (Bruno and Landi, 2011). As such, disparities in diabetes prevalence are likely to persist into the foreseeable future.

Risk factors for diabetes include: high blood pressure; physical inactivity; being overweight or obese; race/ethnicity; and having a family history of diabetes (Heikes et al., 2008). While family history is a strong predictor for developing Type 2 diabetes, this is only the case within American and European cultures (American Diabetes Association, 2011). In non-western cultures, family history does not share the same predictive relationship for development of Type 2 diabetes (American Diabetes Association, 2011). The likely reason for this divergence is the difference in lifestyle behaviors such as diet and physical activity habits (American Diabetes Association, 2011) which are known as controllable behavioral risk factors. Such extant research suggests that family history of diabetes among Americans has more to do with shared health-risk behaviors than genetics.

Understanding perceptions of behavioral and genetic risk is important for guiding diabetes prevention among high-risk populations; however, there is much to be learned about how the high-risk early adulthood population perceives their risk and the factors that influence those perceptions. While there is limited research directly on this topic, related studies have provided a foundation upon which to build research hypotheses. The work of Aoun and colleagues (2002) demonstrates the utility of the Health Belief Model (Rosenstock, 1966) in assessing perceived risk of developing diabetes and resulting cues to act among middle-aged men. Similarly, Soo and Lam (2009) point to the connection between physical and mental manifestations of diabetes by examining the role of stress management.

This article examines risk perceptions among college students for developing diabetes during the life course. Given our focus on early adult-onset diabetes, our study uses the term ‘diabetes’ to imply the clinical condition known as Type 2 diabetes mellitus, which accounts for up to 95 percent of diagnosed cases of diabetes mellitus (Molinaro, 2011). Specifically, this article examines what factors influence college students’ risk perceptions for developing diabetes – beliefs that diabetes is due to controllable behavioral risk factors or non-controllable risk factors, such as genetic causes of diabetes; their personal characteristics such as race/ethnicity and sex; and family history of diabetes. This article also examines these factors on a temporal basis across both proximal and distal time periods. Our a priori hypothesis was that college students lacked an appreciation for the behavioral causes of diabetes, and as a result, may have an optimistic bias about their risk for developing diabetes during their lifetimes. This assertion was based in large part on prior studies of college students and heart disease risk perception (Collins et al., 2004; Green et al., 2003), although these studies did not attempt to explain the temporal nature of risk perception. Since the college environment represents a unique opportunity to establish healthy lifestyle behaviors (Nelson et al., 2008), this is a critical population to study.

Methods

Participants

A total of 703 college students voluntarily responded to an Internet-based survey, with the option to discontinue their participation at any time. Ninety-one respondents were excluded from the study because they reported being personally diagnosed with one of three chronic diseases being studied: cancer; heart disease; and diabetes. The resulting sample contained 612 respondents.

Measures

Dependent variable selection

The dependent variables in the study were respondents’ perceived risk of developing diabetes during the next 10 years (‘10-year absolute risk’) or at some point in their lifetime (‘lifetime absolute risk’). These risks were characterized as ‘absolute’ because they were not based on a relative or peer comparison. These two questions asked respondents to score their perceived risk of developing diabetes across each of the two risk periods. Each question had five ordinal response choices ranging from ‘no chance’ to ‘certain to occur’. The verbatim text of the 10-year absolute risk question was, ‘How likely are you to get the following disease/health condition within the next 10 years?’ The verbatim text of the lifetime absolute risk question was, ‘How likely are you to get the following disease/health condition at any point in your lifetime?’ Based on the work of Hevey and colleagues (2009), our asking relative risk questions prior to the two questions stated above may indicate our approach was best reflective of a type of indirect elicitation, which may minimize unrealistic optimism of respondents in self-rating their susceptibility for developing diabetes (Hevey et al., 2009). Further, these questions were located on page nine of the 13-page instrument. Ideally, Lister and colleagues (2002) suggest such questions be located closer to the front of the instrument to minimize the chance anxiety about health risk questions would influence responses.

Independent variable selection

Nine variables were selected as possible predictors of individual risk assessment for developing diabetes. Sex of the respondent was determined to be important based on previous findings relating to sex-based differences in chronic disease risk perception (Hart, 2005). Considering the known relationships between diabetes and racial/ethnic minorities, a variable was created to indicate which respondents self-identified with a race/ethnicity other than non-Hispanic white. Based on prior studies examining the link between risk perception and family health history (Hart, 2005; Montgomery et al., 2003), the number of immediate family members (i.e. biological and non-biological parents, grandparents, siblings, aunts, uncles, cousins, and children) with diabetes were aggregated into a composite variable, ‘number of family members with diabetes’. Similarly, the number of non-family members (i.e. friends, friends’ family members, classmates, and co-workers) with diabetes was aggregated into a composite variable, ‘number of non-family members with diabetes’.

In developing the research question, we were intrigued by the beliefs and communication about family health history between family members as studied by Hastrup and colleagues (1985). To build upon this literature, we wanted to examine the role parents play in their children’s discussions of family health history, relative to their children’s risk perceptions for developing chronic diseases. As such, a variable describing these relationships was included in our analysis, ‘have discussed family health history with parents’.

Finally, we believed important differences in perceived risk of developing diabetes may exist based on respondents’ perceptions about the causes of diabetes (Sousa et al., 2010). To examine these perceptions, we incorporated four variables which asked the respondent to score how likely their chance was of developing diabetes due to: ‘behavioral causes’; ‘social causes’; ‘genetic causes’; and ‘environmental causes’. Respondents were provided with 10 ordinal response choices ranging from ‘not at all likely’ to ‘absolutely likely’.

Analytical approach – bivariate ordered probit regression

Our study involved an examination of two dependent variables that were significantly related to one another (i.e. Rho = 0.81, X² = 346.78, p < .001). Estimating two separate ordered categorical models (presumably two ordered probit regression models) would have resulted in biased estimators as discovered by researchers using similar analytic methods (Oshio and Kobayashi, 2010). To account for this potential bias, we utilized a bivariate ordered probit regression model to jointly estimate both outcomes simultaneously (i.e. ‘10-year absolute risk’ and ‘lifetime absolute risk’). This approach has been used in several similar situations (de Lapparent, 2008; Weiss, 1993; Yamamoto and Shankar, 2004) where two ordinal response variables were correlated and required joint estimation (see Kim (1995) who utilized this method when studying diabetic neuropathy and found it unreasonable to assume two eyes in the same pair behaved independently). Our specific utilization of bivariate ordered probit regression was accomplished through the ‘bioprobit’ command in Stata Release 11 (StataCorpLP, 2011) developed by Sajaia (2006), and based on maximum likelihood estimation.

Analytical approach – bivariate ordered probit regression – marginal probability

In addition to reporting coefficients, robust standard errors, and p-values; we report the results of a detailed sensitivity analysis conducted on all variables and response choices called ‘marginal probability’ analysis. This metric was calculated using techniques described by Sajaia (2006). Marginal probability represents the anticipated change in the dependent variable when a single independent variable takes a different value, while all other variables remain constant. To determine the full effect of the model results, we calculated the marginal impact of a change in value of each predictor on the response choice for each dependent variable. For example, if the respondent self-identified as being part of a race/ethnicity other than non-Hispanic white, and all other variables remained constant, the marginal change in the percentage of respondents predicted to answer ‘not at all likely’ when asked to assess their perceived 10-year absolute risk perception of developing diabetes would be –35.0 percent (see Table 1). Given a baseline for this response choice of 27.5 percent, this means the model would expect the new predicted percentage of respondents in this category (‘not at all likely’) to be 17.9 percent (27.5% * (1–0.35)). Since changes in marginal probabilities are based on the coefficients of the bivariate ordered probit regression model predictors, statistical significance of changes in probabilities are evaluated by examining the model coefficients and overall model adequacy (Sajaia, 2006).

Table 1.

Descriptive Statistics of Sample (n = 612).

	Male (N = 227)	Female (N = 385)	Total (N = 612)	X²	p
Age				5.242	0.630
18	23 (10.1%)	37 (9.6%)	60 (9.8%)
19	52 (22.9%)	70 (18.2%)	122 (19.9%)
20	61 (26.9%)	125 (32.5%)	186 (30.4%)
21-24	85 (37.4%)	138 (35.8%)	223 (36.4%)
25+	6 (2.6%)	15 (3.9%)	21 (3.4%)
Member of a Race/Ethnicity other than Non-Hispanic White				4.689	0.030
Yes	62 (27.3%)	76 (19.7%)	138 (22.5%)
No	165 (72.7%)	309 (80.3%)	474 (77.5%)
Have Discussed Family Health History with Parents				15.835	0.001
Yes	193 (85.0%)	364 (94.5%)	557 (91.0%)
No	34 (15.0%)	21 (5.5%)	55 (9.0%)
Number of Family Members with Diabetes				9.027	0.108
0	123 (54.2%)	174 (45.2%)	297 (48.5%)
1	62 (27.3%)	146 (37.9%)	208 (34.0%)
1+	42 (18.5%)	65 (16.9%)	107 (17.5%)
Number of Non-Family Members with Diabetes				4.353	0.500
0	149 (65.6%)	229 (59.5%)	378 (61.8%)
1	39 (17.2%)	86 (22.3%)	125 (20.4%)
1+	39 (17.2%)	70 (18.2%)	109 (17.8%)
	Male (N = 227)	Female (N = 385)	Total (N = 612)	t	p
Perceived Cause of Diabetes Susceptibility (1 = “Not at All Likely”, 10 = “Absolutely Likely”)
Behavioral	7.51 (± 2.872)	8.01 (± 2.643)	7.82 (± 2.739)	−2.193	0.029
Social	3.58 (± 2.890)	3.84 (± 3.094)	3.74 (± 3.020)	−1.008	0.314
Genetic	7.34 (± 2.706)	7.59 (± 2.475)	7.50 (± 2.564)	−1.167	0.244
Environmental	3.82 (± 2.920)	4.08 (± 3.045)	3.98 (± 3.000)	−1.020	0.308

Calculating marginal probabilities was thought to be important to facilitate interpretation of the data for researchers and practitioners alike. Reporting statistics yielded from the marginal probability analysis complements the raw coefficients from the bivariate ordered probit regression, which does not allow for the same ease of interpretation as other statistical methods such as ordinary least squares regression.

Procedures

Focus groups consisting of college students were used to create the ‘Finding Roots: Exploring Your Family History’ study (Smith et al., 2011). The purpose of the study was to assess the knowledge, beliefs, and perceptions of family history and chronic diseases. In addition to an initial set of focus groups, one round of follow-up meetings with the same focus group participants was held to review the resulting questionnaire. The final instrument contained 60 items organized according to the constructs of the Health Belief Model (Rosenstock, 1966) (i.e. perceived susceptibility, severity, barriers, and benefits of health behaviors). The survey items contained Likert-type scales, checklists, and closed-end response formats. Likert-type questions utilized a bipolar scale of response choices. Likert-type questions were scored on an ordinal basis with no weighting. Other question types generally yielded indicator variables, and as such, were scored as zero or one. No weighting was used for scoring any item in the instrument.

The questionnaire was then converted to a web-based survey instrument and field tested (n = 77). The web-link to the survey instrument was then provided through electronic mail to academic advisers and department heads across the university to distribute to their students at their discretion. This process was used because it best enabled the researchers to deliver the survey to as many college students as possible. The survey took respondents approximately 15 minutes to complete. This study was approved by the Texas A&M University Institutional Review Board.

Results

Sample characteristics

Table 1 reports the descriptive statistics of the sample. The majority of the sample was under the age of 21 (60.1%) and female (62.9%). Nearly a quarter of the sample self-identified with a race/ethnicity other than non-Hispanic white (22.5%).

The predictors exhibited a wide degree of variability. More than half the sample identified at least one family member with diabetes (51.5%). A higher percentage of female respondents (54.8%) than male respondents (45.8%) identified at least one family member with diabetes. Approximately 38 percent of respondents also identified a non-family member with diabetes. The majority of respondents (91.0%) had discussed family health history with their parents. More females reported having such discussion (94.5%) than males (85.0%). This difference was statistically significant (X² = 15.83, p < .001).

Using a one (‘not at all likely’) to 10 (‘absolutely likely’) scale of susceptibility to diabetes, respondents attributed their highest risk of developing diabetes to behavioral causes (7.82 ± 2.73) and genetic causes (7.50 ± 2.56). Males attributed a lower perceived risk of developing diabetes due to behavioral causes (7.51 ± 2.87) than females (8.01 ± 2.64). This difference was statistically significant (t = −2.19, p = .029). Similar to the findings of Park and colleagues (2010), we do not believe the way the susceptibility questions were framed (i.e. focused on the negative aspects of disease susceptibility versus a positive focus on the benefits of disease avoidance) impacted the results.

Perceived 10-year absolute risk of developing diabetes

Table 2 reports the results of the bivariate ordered probit regression model where the dependent variables of perceived 10-year and lifetime absolute risk of diabetes were jointly estimated. Respondents who self-identified as being part of a race/ethnicity other than non-Hispanic white (β = 0.42, p < .001) and reported increased numbers of family members with diabetes (β = 0.33, p < .001) were more likely to report higher perceived levels of absolute 10-year risk of developing diabetes relative to their counterparts.

Table 2.

Bivariate Ordered Probit Regression Model.

	Ten-Year Risk			Lifetime Risk
	Coefficient	Robust Standard Error	P-Value	Coefficient	Robust Standard Error	P-Value
Dependent Variable Correlation
Wald Test of Rho = Chi2(1) = 346.78, p = 0.000 Rho = 0.81
Independent Variables
Sex	0.019	0.095	0.839	−0.042	0.091	0.647
Member of a Race/Ethnicity other than Non-Hispanic White	0.422	0.116	0.001	0.334	0.115	0.004
Have Discussed Family Health History with Parents	−0.311	0.182	0.087	−0.211	0.177	0.233
Number of Family Members with Diabetes	0.330	0.050	0.001	0.450	0.052	0.001
Number of Non-Family Members with Diabetes	0.034	0.046	0.459	0.038	0.046	0.418
Perceived Cause of Diabetes Susceptibility - Behavioral	0.037	0.027	0.174	0.015	0.023	0.504
Perceived Cause of Diabetes Susceptibility - Social	0.007	0.019	0.721	0.024	0.019	0.204
Perceived Cause of Diabetes Susceptibility - Genetics	0.019	0.028	0.482	0.047	0.025	0.064
Perceived Cause of Diabetes Susceptibility - Environmental	0.024	0.020	0.233	−0.006	0.020	0.743

Perceived Lifetime Absolute Risk of Developing Diabetes

Respondents who self-identified as being part of a race/ethnicity other than non-Hispanic white (β = 0.33, p = .004) and reported increased numbers of family members with diabetes (β = 0.45, p < .001) were more likely to report higher perceived levels of lifetime absolute risk of developing diabetes relative to their counterparts.

Sensitivity analysis

Table 3 illustrates the sensitivity analysis of the bivariate ordered probit regression model. To determine the most relevant predictors when determining changes in 10-year and lifetime absolute risk perception, it is important to (1) consider the statistical significance of the predictor, and (2) examine the magnitude of the change to the original predicted values. Using this criterion, we identified the most significant predictor; whether a respondent self-identified with a race/ethnicity other than non-Hispanic white.

Table 3.

Bivariate Ordered Probit Regression Model Sensitivity Analysis - Marginal Probabilities.

Percent of Sample by Self-Reported Risk Perception	Marginal Change in Risk Perception Given a Change in a Single Independent Variable Value*
	Baseline Ten-Year Risk Perception Scores					Baseline Lifetime Risk Perception Scores
	Not at All Likely				Absolutely Likely	Not at All Likely				Absolutely Likely
	1	2	3	4	5	1	2	3	4	5
	27.5%	43.3%	22.1%	5.7%	1.4%	14.3%	36.1%	29.8%	14.8%	5.0%
Marginal Change in Risk Perception Score Based on a Change in…….
	Marginal Percentage Change in Ten-Year RiskPerception Scores					Marginal Percentage Change in Lifetime RiskPerception Scores
	Not at All Likely				Absolutely Likely	Not at All Likely				Absolutely Likely
1	2	3	4	5	1	2	3	4	5
Sex
Male	1.4%	0.0%	−1.1%	−1.9%	−2.6%	3.9%	1.1%	−1.0%	−2.7%	−4.4%
Female	−0.8%	0.0%	0.6%	1.1%	1.7%	2.3%	0.6%	−0.6%	−1.6%	−2.7%
Member of a Race/Ethnicity other than Non-Hispanic White
Yes	−35.0%	−2.9%	29.6%	57.9%	78.3%	−35.0%	−12.3%	9.4%	29.1%	46.9%
No	9.3%	1.5%	−7.8%	−18.7%	−32.5%	8.7%	3.7%	−1.7%	−8.0%	−18.2%
Have Discussed Family Health History with Parents
Yes	3.0%	0.2%	−2.4%	−4.8%	−7.1%	2.7%	0.8%	−0.7%	−2.1%	−3.3%
No	−29.3%	−3.0%	24.2%	51.4%	81.8%	−25.4%	−8.9%	6.3%	20.8%	38.2%
Number of Family Members with Diabetes
+10% Change in Number of Family Members	−2.0%	−0.8%	1.7%	5.8%	12.8%	−2.5%	−1.9%	−0.2%	3.4%	12.2%
Number of Non-Family Members with Diabetes
+10% Change in Number of Family Members	−0.2%	0.0%	0.2%	0.4%	0.6%	−0.3%	−0.1%	0.1%	0.3%	0.5%
Perceived Cause of Diabetes Susceptibility - Behavioral
+10% Change in Score	−3.1%	−0.2%	2.5%	4.9%	7.3%	−1.6%	−0.5%	0.4%	1.3%	2.2%
Perceived Cause of Diabetes Susceptibility - Social
+10% Change in Score	−0.3%	0.0%	0.2%	0.5%	0.7%	−1.2%	−0.4%	0.3%	1.0%	1.9%
Perceived Cause of Diabetes Susceptibility - Genetics
+10% Change in Score	−1.6%	−0.1%	1.3%	2.5%	3.7%	−4.7%	−1.7%	1.2%	3.9%	7.0%
Perceived Cause of Diabetes Susceptibility - Environmental
+10% Change in Score	−1.0%	−0.1%	0.8%	1.7%	2.7%	0.3%	0.1%	−0.1%	−0.3%	−0.5%

Calculations are mutually exclusive. Assumes values for other independent variables remain the same.

Note - Marginal changes do not sum to 1 because of the nature of simultaneous model specification with the variables.

Respondents self-identifying with a race/ethnicity other than non-Hispanic white experienced a higher level of perceived risk for developing diabetes over their life course. In the 10-year risk period, sensitivity analysis indicated only 17.9 percent of these respondents were predicted to fall into the first response choice category (‘not likely at all’) versus 27.5 percent when race/ethnicity was not a factor. Conversely, 2.5 percent of those self-identifying with races/ethnicities other than non-Hispanic white were predicted to fall into the fifth response choice category (‘absolutely likely’) versus 1.4 percent when race/ethnicity was not a factor. Results were similar in the lifetime risk period. Only 9.3 percent of those self-identifying with a race/ethnicity other than non-Hispanic white were predicted to fall into the first response choice (‘not likely at all’) versus 14.3 percent when race/ethnicity was not a factor. Conversely, 7.3 percent of those self-identifying with a race/ethnicity other than non-Hispanic white were predicted to fall into the fifth response choice category (‘absolutely likely’) versus 5.0 percent when race/ethnicity was not a factor. While the variable measuring the number of family members with diabetes was statistically significant in both risk periods, Table 1 illustrates how its impact on predicted risk perception was negligible.

Discussion

Perceived risk of developing diabetes accurately reflects race/ethnicity trends

Our results indicate respondents identifying with a race/ethnicity other than non-Hispanic white were more likely to perceive their risk of developing diabetes in both the 10-year and lifetime absolute risk periods as greater than their non-Hispanic white counterparts. By examining the marginal probabilities in Table 3 for perceived 10-year risk, it can be determined that approximately 60 percent of those identifying with a race/ethnicity other than non-Hispanic white were predicted to fall into the two lowest risk response categories. Conversely, 74 percent of non-Hispanic white respondents were predicted to fall into the two lowest risk response categories. Despite the literature critiquing the ability of college students to accurately perceive their risk for chronic diseases such as heart disease (Collins et al., 2004; Green et al., 2003), the perceptions of respondents in our study reflected congruence with prevalence data along race/ethnicity lines (Zhang et al., 2009). Although our outcome variables were different than Ho and colleagues (2007) who studied perceived health status among chronic disease and diabetes patients as young as age 25, our research was not able to establish the same relevance of age and sex to our research questions. This could be due to the geographic differences in the studies (i.e. our study was conducted in the United States while the study performed by Ho and colleagues was conducted in Hong Kong).

College students’ perceived risk of diabetes is driven by non-controllable factors

The most alarming finding in this study is the lack of a statistically significant relationship between perceived risk of developing diabetes and the contribution of behavioral risk factors. While college students report the importance of behavioral risk factors in developing diabetes (see Table 1), that belief was not associated with their own personal risk assessment for developing diabetes. This finding is no different than other recent studies (Copeland et al., 2009; Schwartz et al., 2010) which have tried to identify the sources of unfounded optimism of college students with respect to the consequences of their often high-risk health behaviors. In addition, this finding is consistent with much of the established social psychology and medical literature regarding optimistic biases and perceived health risks (Allen and Blumenthal, 1998; Azzarello, 2007; Weinstein, 1984, 1987) In this study, not only were behavioral causes of diabetes susceptibility not statistically significant in predicting levels of perceived risk of developing diabetes, but instead, the two factors that were statistically significant (i.e. identifying with a race/ethnicity other than non-Hispanic white, and having an increased number of family member with diabetes) were both considered to be non-controllable factors. This is worth noting because not only does it suggest college students have minimized their perceived diabetes risk as attributed to behavioral factors, it also suggests they base their risk assessment primarily on non-controllable risk factors, lessening their likelihood of engaging in preventive behaviors. As discussed in this study, such a view is not only without support in the clinical data, it is also a view with substantial societal implications in terms of economics, morbidity, and ultimately mortality.

Study limitations

This study is unique in its examination of how risk perceptions are influenced across different points of time in the life course. However, one notable shortcoming is the study does not link perceptions to health status. This is an opportunity for a future study. Second, while the bivariate ordered probit regression model is an appropriate and unique tool for this data set, it does not report a metric such as the coefficient of determination to allow us to see how much of the variation in risk perceptions is explained with the independent variables. Next, the methods for administering the survey instrument did not allow for a calculation of response rate. Finally, the study was focused on a single college campus which may or may not be representative of the wider college population.

Conclusion

As researchers, administrators, and policymakers continue to examine ways to improve the health of adolescent and young adult populations, it is important to study cognitive perceptions alongside clinical factors. This study is among a handful of efforts to illustrate how a population of college students perceived their risk for developing a specific disease along a racial/ethnic dimension. This finding is different than the prevailing view that college students rarely understand their personal health risks. Future research is needed to better understand why differences exist in the accuracy of the perceived risk of developing diabetes, and how such differences can be overcome. The work of Eiser and colleagues (2002) reminds us how risk assessment and changes based on behavioral motivations are multifaceted, not only in terms of clinical issues such as the differentiation between Type 1 and Type 2 diabetes patients, but also in terms of the complex interactions that occur between patients and clinicians. Issues such as these should be further studied among early adult populations.

Footnotes

None declared.

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