Abstract
Using data from the Indiana Black Men’s Health Study (N = 455), a community-based sample of adult Black men, the primary aim of this study was to explore factors of health care discrimination, and to examine if such reports differed by age and the frequency of race thoughts. Approximately one in four men reported experiencing discrimination in the health care setting. Results from the multivariable logistic regression models suggested that frequent race thoughts (odds ratio [OR]: 1.89, p < .05), not having health insurance (OR: 1.80, p < .05), and increased depressive symptomology (OR: 1.06, p < .01) were positively associated with reports of health care discrimination. A multiplicative interaction coefficient of age and frequency of race thoughts was included to determine if health care discrimination differed by age and frequency of race thoughts (OR: 1.03, p = .08). Results from the predicted probability plot suggested that the likelihood of experiencing health care discrimination decreases with age (OR: 0.97, p < .05). In particular, results suggested that between the ages of 33 and 53 years, Black men who experienced frequent race thoughts were more likely to report experiences of discrimination in the health care setting than men of the same age that did not experience frequent race thoughts. These results highlight the need for empirical work to better understand the experiences of Black men, a group less likely to utilize health care services than most adult groups within the health care setting.
Black males have significantly higher rates of morbidity and mortality from many medically amendable conditions compared with White males (Haiman et al., 2006; Mozaffarian et al., 2015; Schiller, Lucas, Ward, & Peregoy, 2012; Siegel, Naishadham, & Jemal, 2012; Thorpe et al., 2013; Thorpe et al., 2014). More troubling is the fact that Black males are less likely to seek medical care regardless of the severity of their health problems (Neighbors & Howard, 1987), and are less likely to seek routine health care screening when compared with White men (U.S. Department of Health & Human Service, 2006). Some researchers suggest that Black males perceived poor treatment in the health care setting in general or specifically by one or more medical professional (hereafter referred to as health care discrimination), may in part explain their reluctance to engage with the health care system and as such remains an important area of focus (Williams, 2003).
Health care discrimination has been reported to be associated with less optimal chronic disease screenings (Hausmann, Jeong, Bost, & Ibrahim, 2008b; Peek, Wagner, Tang, Baker, & Chin, 2011; Trivedi & Ayanian, 2006), delays in prescription filling (Van Houtven et al., 2005), and poor self-rated health (Hausmann, Jeong, Bost, & Ibrahim, 2008a; Lee, Ayers, & Kronenfeld, 2009; Perez, Sribney, & Rodríguez, 2009). Previous studies suggested that socioeconomic factors like education and income, the lack of health insurance, being African American, and being male are statistically associated with health care discrimination (Hausmann et al., 2008a; Lauderdale, Wen, Jacobs, & Kandula, 2006; LaVeist, Nickerson, & Bowie, 2000; Shavers et al., 2012). For example, Hausmann et al. using a national sample of adults, showed that adults younger than 65 years and adults who frequently think about their race have an increased likelihood of reporting differential treatment while seeking health care (Hausmann et al., 2008a). Although previous studies have examined health care discrimination, little is known about Black adults males’ experiences with health care discrimination.
Interestingly, interpersonal discrimination outside of the health care setting, which has been shown to be statistically associated with various health outcomes (Paradies, 2006; Pascoe & Richman, 2009; Williams, Neighbors, & Jackson, 2003), is believed to have a carry-over effect into the health care system (Smedley, Stith, & Nelson, 2009). For example, Hammond (2010) found that interpersonal discrimination outside of the heath care setting was positively associated with health care discrimination among Black males.
While little is known about Black men’s specific experiences with health care discrimination, less is known about the impact of age on the reports of health care discrimination. Age is of particular interest because previous studies have suggested that the perception of non–health care does differ by age and, more important, the risk of illness and mortality does also increase by age. In particular, previous findings based on a sample of African American adults living in Detroit, Michigan have suggested that the non–health care discrimination decreases with age (Broman, Mavaddat, & Hsu, 2000; Gary, 1995). However, other findings imply that older Black males may be more apt to report health care discrimination attributed to mistrust in the health system due to historical events like the Tuskegee Study of Untreated Syphilis in the Negro Male (Barnes et al., 2004; Gamble, 1997; Hammond, 2010).
In addition to age, there is growing interest in how the salience of one’s race may affect the perception of discrimination. Some suggest that frequent thoughts of one’s own race may be associated with perception of discrimination (Hausmann et al., 2008a; Sellers & Shelton, 2003; Sellers, Smith, Shelton, Rowley, & Chavous, 1998; Shelton & Sellers, 2000). That is, those who think frequently about their race, compared with those who do not, may be more apt to evaluate an ambiguous situation as discriminatory (Sellers et al., 1998; Shelton & Sellers, 2000). It is important to determine if racial salience is influential to reports of health care discrimination, as the consequence of such perceptions may result in disengagement with the health care system.
Given the current dearth of evidence about the factors associated with the experiences of health care discrimination among Black males, the goal of this study was twofold. First, the primary goal was to examine factors associated with health care discrimination among a relatively large sample of Black males. Our second goal was to determine whether age and frequency of race thoughts differentially predict health care discrimination.
Method
Sample
Data from the Indiana Black Men’s Health Study (BMHS) were used for the study. The BMHS was designed to identify social determinants that affect Black men’s health, opportunities and initiatives to prevent chronic health conditions, and gaps in awareness, access, and utilization of health services. The BMHS relied on convenience sampling to recruit participants from 11 counties known to have relatively high proportions of Black male residents compared with other counties in Indiana. The data, collected between July and August of 2011, came from members of targeted focus groups who self-administered paper-pencil surveys. Participants had the opportunity to complete two surveys that consisted mainly of questions from the Behavioral Risk Factor and Surveillance System (BRFSS). Men who self-identified as African American or Black, 18 years or older, and an Indiana resident were eligible to participate in the study. The participants were given a $15 gift card for each completed survey.
The first survey (Survey A) consisted of questions related to general health and use of health services, and the second survey (Survey B) consisted of questions related to perceived discrimination and help-seeking behaviors. Although potential respondents were encouraged to complete both surveys, some elected to complete only one (Survey A’s n =1,444; Survey B’s n = 1,151). For this study, we used measures from both surveys. However, because the respondents did not complete both surveys and each survey was anonymous, we employed an automated matching process using the following demographic variables that were assessed on both surveys: respondent’s age, education, income, employment, race, and marital status. Surveys that tied on two or more of the variables were not retained. Only those surveys that matched 1:1 were retained for this study (n = 836).
Of the initial 836 respondents who completed both surveys, 381 respondents were missing one or more of the variables used in the analysis. Overall, health care discrimination (15.8%) and frequency of race thoughts (9.2%) had the most missing responses, with the other variables used in the analysis having less than 1% missing. We conducted an analysis of the missingness pattern between the group that was missing on one or more variables (n = 381) versus those the group that were missing none of the variables (n = 455). Based on one-way analysis of variance and chi-square test, respondents in the non–missing group (n = 455) did not significantly differ in reports of health care discrimination, race consciousness, or most of the other variables used in the analysis. Respondents with one or more missing variables tended to have more chronic health conditions and to be older than the respondents with no missing variables. As such, we chose to use the non–missing group as our analytic sample for this study. Listwise deletion was used in all of the analyses conducted in this study.
Study Measures
Outcome Variables
The outcome of interest for the study is the perception of unfair treatment in the health care setting (i.e., health care discrimination). Health care discrimination was assessed by respondents’ responses to the following question: “Within the past 12 months when seeking health care, do you feel you were treated worse than, the same as, or better than people of other races?” The possible responses were (1) worse than other races, (2) the same as other races, (3) better than other races, (4) worse than some races better than other races, and (5) only encountered people of the same race. Similar to a previous study, the response “worse than other races” was coded as experiencing health care discrimination, and the second and third responses were coded as not experiencing health care discrimination in the health care setting (Crawford, Jones, & Richardson, 2010; Hausmann et al., 2008a). The responses to the latter two options were treated as missing because they did not indicate a strong sense of favorable or unfavorable treatment (Crawford et al., 2010; Hausmann et al., 2008a).
Independent Variables
Several independent variables such as health insurance, affordability of medical care, frequency of race thoughts, perceived everyday discrimination, depressive symptomology, and having one or more chronic health condition might be associated with health care discrimination. Health insurance status was assessed by asking, “Do you have any kind of health care coverage, including health insurance, prepaid plans such as HMOs, or government plans such as Medicare?” (“yes” or “no”). Affordability of medical care was assessed using the question “Was there a time in the past 12 months when you needed to see a doctor, nurse, or health care provider but could not because of cost?” (“yes” or “no”). Frequency of race thoughts was assessed using the question “How often do you think about your race? (never, once a year, once a month, once a week, once a day, once an hour, constantly).” Responses were coded into two categories: nonfrequent (“never,” “once a year,” “once a month”), and frequently (“once a week,” “once a day,” “once an hour,” and “constantly”).
Everyday discrimination was assessed using the Everyday Discrimination Scale (Williams, Yan, Jackson, & Anderson, 1997). The scale is a 10-item measure of perceived everyday interpersonal discrimination that assesses the occurrence and frequency with which the respondents endure routine and relatively minor experiences of unfair treatment (Williams et al., 1997). More specifically, respondents were asked how often (1) they were treated with less courtesy, (2) treated with less respect than others, (3) they received poorer service than others, (4) they believed others acted as if they were not smart, (5) others acted as if they were afraid of them, (6) others felt that they were dishonest, (7) people acted like they were better than them, (8) people called them names or insulted them, (9) they felt threatened or harassed, or (10) they were followed in stores. The responses ranged from 1 to 6, with 1 being “almost everyday” and 6 being “never.” The scale was created by reverse coding each of the response category (1 = 5, 2 = 4, 3 = 3, 4 = 2, 5 = 1, 6 = 0) and then summing the responses of 10 items, which resulted in a scale that ranged from 0 to 50. Higher scores on the scale represent higher frequency of experiences with discrimination.
Depressive symptomology was assessed using a Rasch-derived short form 10-item version of the Center for Epidemiologic Studies-Depression (CES-D) Scale (Cole, Rabin, Smith, & Kaufman, 2004). Respondents were asked, “during the past week how often did you feel: (1) bothered by things that do not usually bother you, (2) unable to shake off the blues even with help from family and friends, (3) just as good as other people, (4) like you had trouble keeping your mind on what you were doing, (5) that everything you did was an effort, (6) hopeful about the future, (7) life had been a failure, (8) fearful, (9) lonely, and (10) people were unfriendly.” Items “3 and 6” were reverse coded in order for the item response to reflect the theoretical construct of the scale. The response scale for each item ranged from 0 to 3, with 0 indicating “rarely,” “less than one a day,” and 3 indicating “most of the time, 5 to 7 days.” The response options were then summed across the 10 items to create a scale with a range of 1 to 10. The chronic health conditions variable was assessed, based on the response to the following question: “Have you ever been told by a doctor, nurse, or health care professional that you have: arthritis, asthma, diabetes, heart problems, hypertension, high cholesterol, HIV/AIDS, or a stroke?” (“yes” or “no”). A binary variable was created to indicate whether they had one or more chronic health conditions.
Covariates
Age, education, (three-level categorical: ≤high school, some college or trade school, and college graduate or more), income (three-level categorical: ≤35,000; >35,000; and missing), and marital status (married vs. all others) were included in the analysis as potential confounders. Since income is a sensitive response item, a missing category was used for income, as a means to not drop too many respondents from the analysis.
Data Analysis
Sample characteristics were summarized for the analytic sample and additionally summarized and compared using chi-square statistics between those who experienced health care discrimination compared with those who did not (see Table 1). In Table 2, we presented results from the bivariate logistic regression analyses between health care discrimination and each of the independent variables and also results from the multivariable regression analyses with all of the independent variables included in the model. To determine whether experiences of health care discrimination differed by age and frequency of race thoughts, we included a multiplicative interaction term for age and frequency of race thoughts in the full model shown in Table 2 (results not shown). Figure 1 depicts the probability plot of experiencing health care discrimination by age and frequency of race thoughts derived from the multivariable logistic regression model with the “age × race thoughts” multiplicative interaction term. All of the analyses for the study were performed in STATA version 13 (StataCorp LP, College Station, Texas). The data collection process was approved by the institutional review boards at Purdue University and Indiana University.
Sample Characteristics of the Indiana Black Men’s Health Study.
Discrimination refers to discrimination when seeking health care. bCenter for Epidemiologic Studies-Depression. cCount of having at least one of the following chronic health conditions: arthritis, asthma, diabetes, heart attack, hypertension, high cholesterol, HIV/AIDS, and stroke.
~p < .10. **p < .05. ***p < .01.
Logistic Regression Depicting Perceived Discrimination While Seeking Health Care and Study Characteristics Using BMHS (n = 455).
Note. BMHS = Black Men’s Health Study; CES-D = Center for Epidemiologic Studies-Depression; OR = odds ratio; CI = confidence interval.
Bivariate association between health care discrimination and each respective variable. bAdjusted odds ratio reflecting the association between health care discrimination, controlling for all of the variables. cPresence of having at least one of the following chronic health conditions: arthritis, asthma, diabetes, heart attack, hypertension, high cholesterol, HIV/AIDS, and stroke.
Predictive probability of experiencing health care discrimination by age and frequency of race thoughts controlling for study characteristics.
Results
The characteristics of the analytic sample are reported in Table 1. About 23% of the sample reported experience with discrimination in the health care setting. The average number of chronic health conditions was over 1.33. Slightly over half of the sample reported that they frequently thought about their race (52.31%). Of those, only 28.57% reported experiencing health care discrimination (p < .01). The mean score for everyday discrimination in the entire sample was 24.92, and there were significant differences (p < .01) in the everyday discrimination by reports of experiencing health care discrimination (26.38) compared with those who did not (24.52). About 30% of the sample did not have health insurance, and among those who did not, 35.11% reported health care discrimination (p < .01). This pattern was similar for those who could not receive care due to cost, 34.51% of the total population could not get care; however, only about 30% of those who could not get care reported health care discrimination (p < .05). The CES-D score for the entire sample was 12.93, with significant difference (p < .01) between those who experienced health care discrimination (14.55) and those who do not (12.45).
The results from the bivariate and multivariable logistic regression predicting discrimination in the health care setting are shown in Table 2. In the unadjusted models, Black males who thought about their race on a weekly basis or more frequently were twice as likely to report experiencing discrimination in the health care setting (odds ratio [OR]: 2.01, p < .05) compared with men who thought about their race on a less frequent basis. Similarly, men who did not have health insurance were more likely to report discrimination in the health care setting (OR: 2.48, p < .01) compared with those males who had health insurance. Males who had financial barriers to care were more likely to report health care discrimination compared with males who did not have such barriers (OR: 1.63, p < .05). Those males with increased levels of depressive symptomology were more likely to experience health care discrimination (OR: 1.09, p < .01). On the other hand, in the unadjusted models, being older (OR: 0.98, p < .05), having a trade school/some college education (OR: 0.58, p < .05), and household income greater than $35,000 (OR: 0.44, p < .01) were associated with a lowered likelihood of reporting health care discrimination.
Similar to the unadjusted model, in the multivariable logistic analysis, males who thought frequently about their race were more likely to experience health care discrimination (OR: 1.89, p < .05) when compared with men that did not think of their race as frequently. Although not statistically significant, reports of everyday discrimination were associated with an increased likelihood of experiencing health care discrimination (OR: 1.01). The likelihood for males without health care coverage to experience health care discrimination attenuated in the adjusted models (OR: 1.80, p < .05). Controlling for all the variables, financial barriers to health care was no longer a statistically significant predictor of health care discrimination (OR: 1.42, p > .05). An increase in depressive symptomology was associated with an increased likelihood of experiencing health care discrimination (OR: 1.06, p < .01). There was a marginal association between having a trade school/some college education (OR: 0.56, p = .054) and experiencing health care discrimination.
The “age × race thoughts” multiplicative interaction term, testing whether the probability of health care discrimination differed by age and frequency of race thoughts, was marginally significant (OR: 1.03, p = .08, data not shown). The predicted probability of experiencing health care discrimination by age and stratified by frequency of race thoughts is depicted in Figure 1, controlling for the variables in Table 1. As shown, the probability of experiencing discrimination in the health care setting decreased with age. However, as noted in Figure 1, this pattern was not consistent throughout all of the age groups with respect to the frequency of race thoughts. As shown, between the ages 38 and 48 years, men who thought about their race on a weekly basis or greater were more likely to experience health care discrimination than those that thought about their race less frequently.
Discussion
Although all of coefficients were not statistically significant (p < .05), findings from this study suggested that increased frequency of race thoughts, having no health insurance, lower income, lower education, and increased depressive symptomatology were all positively associated with a higher likelihood of experiencing health care discrimination. The findings are consistent with previous research, which suggests African Americans vulnerability to experiencing health care discrimination.
Similar to previous findings, in the current study, Black males without health insurance were almost twice as likely to report health care discrimination compared with those with health insurance (Burgess, Ding, Hargreaves, van Ryn, & Phelan, 2008; Burgess et al., 2009). This may result from the stigma associated with the structural barriers in accessing health care, whereby males without health insurance may perceive additional barriers in accessing public health services (e.g., clinics) as unfair treatment. Previous studies have reported similar results (LaVeist, Rolley, & Diala, 2003; Trivedi & Ayanian, 2006), and have offered that patients may perceive more discriminatory treatment in clinic settings as opposed to private offices (LaVeist et al., 2003).
Males who frequently thought about their race were almost twice as likely to report discrimination compared with men who thought about their race less frequently. This finding supports the hypothesis suggested by others that individuals with high racial identity were more likely to attribute unfair treatment to their race, and were more apt to report racial discrimination (Sellers et al., 1998; Shelton & Sellers, 2000). Findings from the current study are also similar to Hausmann et al. (2008a), which determine that frequent race thoughts were associated with health care discrimination among a national sample of racially diverse adults. Given the role that racial salience has on perceptions of unfair treatment in the health care setting, future studies should explore the relationship between racial saliency and health behaviors among Black males. Such studies can offer insight on Black males at risk not engaging in the health care system, in efforts to develop targeted interventions to encourage their participation with the system.
The current findings are also similar to other studies that have demonstrated that depressive symptomology, as well as other mental health disorders, are positively associated with perceived discrimination (Burgess et al., 2008; Gee, Ro, Gavin, & Takeuchi, 2008; Noh & Kaspar, 2003; Pascoe & Richman, 2009; Schulz et al., 2006). For example, findings from Brown et al. (2000), using three waves of longitudinal data, demonstrated that reports of discrimination at Wave 2 were associated with psychological distress at Wave 3. Yet psychological distress and depression at Wave 2 were not associated with reports of discrimination at Wave 3 (Brown et al., 2000). This suggests that mental health may not be predictive of perceptions of discrimination. Thus, it is assumed in the current study that depressive symptomology is not influential to perceptions of discrimination among Black males.
Interestingly, in light of Smedley et al.’s (2009) proposition that interpersonal discrimination might have carry-over effects in the health system, in the current study daily discrimination was not a significant predictor of health care discrimination. The lack of statistical significance in the current study is also contrary to findings from Hammond (2010), which suggests that the effects of Black males’ experiences with interpersonal discrimination may extend to their interaction within the health care system. It should be noted that the current study used the Everyday Discrimination Scale to measure discrimination, whereas Hammond (2010) used the Daily Life Experience subscale of Racism and Life Experiences Scales (Harrell, Merchant, & Young, 1997). Furthermore, in the current study, health care discrimination was measured using the item from BRFSS “Reaction to Race Module”; Hammond (2010) used an adapted version of the Perception of Racism Scale (Green, 1995). The differences in scales might contribute to the lack of significance found in the current study and Hammond (2010). The contrasting finding highlights the need for a more thorough understanding of health care discrimination, which should be assessed through a nationally based study on Black males’ health.
Study results offer new insight on the association between experiences of health care discrimination and age among Black men. Similar to previous findings, younger Black males are most likely to perceive discrimination compared with older Black men (Broman et al., 2000). Our findings suggest that perception of health care discrimination attenuates over the life course. The older men in the study may feel that current slights are not worth noting given the past history of more blatant mistreatment of racial/ethnic minorities (Broman et al., 2000). Future studies should further explore age differences in the perception of health care discrimination in efforts to determine if these perceptions are associated with the use of preventative health services among Black males. Furthermore, the results may represent an unconscious bias by health professionals, in that they may deem older Black males less intimating than younger Black males, thus treating them with better bedside manner. Previous studies have demonstrated that such biases influence health professionals’ decision making and interactions, which may contribute to health disparities (Dovidio & Fiske, 2012; van Ryn et al., 2011). To examine this phenomenon, future studies may consider using vignettes and/or standardized patients studies to further evaluate any potential biases by health care personnel. Findings from such studies can be used to raise the awareness of health professionals regarding their potential biases toward Black males.
Limitations
Although the results from this study can offer some insights into factors that may be related to discrimination in the health care setting among Black men, it is not without limitations. One limitation to the current study is the use of a single-item measure for discrimination in the health care setting. Although the validity and reliability of this item to measure health care discrimination was assessed in a pilot testing in 2002 for the BRFSS, additional studies are warranted. As mentioned by Hausmann et al. (2008a), the wording of experiences “while seeking health care” might be taken too literally, and respondents might only recall unfair practices while accessing the health care system, for instance, while scheduling an appointment, not the actual experiences while being seen by a health care professional. If indeed it was the case, the relationship reported here and elsewhere may be biased downward given that some events may not have been reported.
Another potential limitation to the current study concerns its cross-sectional nature. It is unclear whether experiences of discrimination in the health care setting causes an individual to think more about his or her race or whether individuals who think about their race more frequently are more likely to overreport experiences of discrimination. Although some existing studies do suggest that rumination about one’s race can potentially result from interpersonal discrimination (Pascoe & Richman, 2009; Williams & Mohammed, 2009), future studies must examine this particular issue as it relates to health care–seeking behavior. The last limitation concerns the generalizability of the findings. The current study used convenience sampling; therefore, the findings might not be generalizable to all Black men and their experiences in the health care setting. The participants in the study were not selected at random, and the experiences of Black men living in Indiana might be different from those of Black men in other regions.
Despite its limitation, the findings from this study are consistent with national studies that suggest that African Americans experience discrimination in the health care setting (Hausmann et al., 2008a). The BMHS sample size might affect the findings from the study, by limiting the effect sizes in the analysis. However, the community sampling strategy employed for the current study has value in that it provides a large number of Black males, which is useful in exploring their health needs, a group that is typically hard to reach in traditional random sampling studies. The BMHS is valuable in that it offers insight onto the health needs and barriers to accessing health care from a large sample of Black men.
Conclusion
Findings from the study illustrate the need to further explore social determinants that influence health care–seeking behaviors and subsequent health outcomes among Black men. The findings from the study offer new insight on factors associated with health care discrimination among Black males. Our findings that some differences in health care discrimination may exist among men ages 38 to 53 years. Given that many regular preventive health screenings for men begin at 40 years, future studies should explore factors that may decrease potential health care discrimination for this particular group of men. Future research should examine how institutional practices, policies, and regulations may affect how individual providers may unknowingly discriminate against patients.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support or the research, authorship, and/or publication of this article: The Indiana Black Men’s Health Study was funded by the Racial and Ethnic Minority Epidemiology Center at the Indiana Minority Health Coalition, Inc.