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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Am J Public Health. Author manuscript; available in PMC Dec 18, 2013.
Published in final edited form as:
PMCID: PMC3518332
NIHMSID: NIHMS535822
An Assessment of Biases Against Latinos and African Americans Among Primary Care Providers and Community Members
Irene V. Blair, PhD, Edward P. Havranek, MD, David W. Price, MD, Rebecca Hanratty, MD, Diane L. Fairclough, DrPH, Tillman Farley, MD, Holen E. Katz, MA, and John F. Steiner, MD, MPH
Irene V. Blair, Department of Psychology and Neuroscience, University of Colorado Boulder;
Address correspondence to Irene V. Blair, University of Colorado Boulder, Department of Psychology and Neuroscience, UCB 345, Boulder, CO 80309-0345, Irene.Blair/at/Colorado.edu
Objectives
This study assessed implicit and explicit bias against both Latinos and African Americans, among experienced primary care providers (PCPs) and community members (CMs) in the same geographic area.
Methods
210 PCPs and 190 CMs from three health care organizations in the Denver metro area completed Implicit Association Tests and self-report measures of implicit and explicit bias, respectively.
Results
With a 60% participation rate, the PCPs demonstrated substantial implicit bias against both Latinos and African Americans, but this was no different from CMs. Explicit bias was largely absent in both groups. Adjustment for background characteristics showed the PCPs to have slightly weaker ethnic/racial bias than CMs.
Conclusions
This research provides the first evidence of implicit bias against Latinos in health care, as well as confirming prior findings of implicit bias against African Americans. The lack of substantive differences in bias between the experienced PCPs and CMs suggests a wider societal problem. At the same time, the wide range of implicit bias suggests that bias in healthcare is neither uniform nor inevitable, and important lessons may be learned from providers who do not exhibit bias.
Significant ethnic/racial disparities in health care and health outcomes show remarkable consistency across illnesses and health care services in the United States.1,2 Reduction of these disparities and their associated excess morbidity and mortality is a major goal for quality improvement.36 A 2003 report by the Institute of Medicine (IOM) crystallized long-standing concerns that provider attitudes are part of the problem, concluding that “bias, stereotyping, prejudice, and clinical uncertainty on the part of health care providers” likely play a role in the continuation of health disparities.7
For present purposes, bias can be defined as the negative evaluation of one group and its members relative to another. Such an evaluation can be expressed explicitly (e.g., “I don’t want to work with Latinos.”) or more implicitly (e.g., negative nonverbal behavior). Explicit bias also differs from implicit bias in terms of underlying process. Explicit bias requires that the person is aware of the evaluation, believes that evaluation to be correct in some manner, and has the time and motivation to act on it in the current situation.810 Accordingly, explicit bias is measured by asking individuals to report on their own feelings and beliefs. Such measures show that explicit bias against ethnic/racial groups has declined significantly over the past 50 years and is now unacceptable in general society.11
Implicit bias, in contrast, operates in an unintentional and even unconscious manner.810,12 Activated by situational cues (e.g., a person’s skin color) implicit bias can quickly and unknowingly exert its influence on perception, memory and behavior.10,1317 Self–report is therefore not a good measure of implicit bias. This form of bias is instead measured by sophisticated instruments that have been developed for this purpose, the most common being the Implicit Association Test (IAT).18,19 These instruments reveal that unlike the decline in explicit bias, implicit bias appears to be common and persistent.2022
To better understand how implicit bias may affect clinical outcomes, consider the example of an implicitly biased physician who wrongly perceives that an African American patient with uncontrolled hypertension is uncooperative and unlikely to adhere to a more intensive treatment regimen. Unaware of the distortions introduced by bias, the physician may not intensify treatment appropriately. Furthermore, the physician may demonstrate bias in unconsciously negative behavior (e.g., in facial expression, body language and voice tone), making the patient uncomfortable and hesitant to engage in honest dialogue. In this manner, implicit bias may hamper the flow of information and weaken the patient’s resolve to follow treatment recommendations.2328
Six studies have directly measured ethnic/racial biases among health care providers, all focused on bias against African Americans.2933,34 Five of these studies found evidence that providers have implicit bias against African Americans to varying degrees, whereas explicit bias against the same group is low to non-existent.35
Thus, although the number of studies is not high, the evidence is generally consistent in suggesting that implicit but not explicit ethnic/racial bias exists in health care settings. This conclusion is circumscribed, however, by limitations of the research.35 First, ethnic/racial bias in health care has not yet been assessed with regard to groups other than African Americans. Of particular concern in this regard is the lack of research on bias against Latinos, who constitute the largest and fastest growing minority group in the U.S.36 and also experience a disproportionate burden of poor health outcomes.1,2 Second, all but one of the six studies were conducted with relatively young and inexperienced providers (residents and students). It is therefore unknown how experienced providers might respond. Third and finally, nearly all of the studies have suffered from very low (e.g., 26%) or unknown response rates, again calling into question the representativeness of the results.35 In this study, we hypothesized that primary care providers (PCPs) would demonstrate, on average, a substantial level of implicit bias (Cohen’s d ≥ .50) against Latinos and against African Americans; that PCPs would demonstrate little explicit bias (Cohen’s d ≤ .50) against either group; and that PCPs and community members would not differ in implicit or explicit ethnic/racial bias.
This study measured the implicit and explicit ethnic/racial biases of PCPs in the metropolitan Denver area. Several steps were taken to address the limitations of prior research. First, implicit and explicit biases against both Latinos and African Americans were measured. Second, only experienced, practicing PCPs with established patient panels were eligible to participate. Third, the PCP sample was recruited from Family Medicine and Internal Medicine providers working in three different health care settings that broadly reflect the organization of primary care practices in the U.S. Fourth, we attempted to recruit all of the eligible providers in these organizations, setting a pre-determined participant denominator to calculate a firm response rate. Fifth, the study measures were concurrently administered to a sample of community members who visited the health care clinics of the participating PCPs, allowing for a geographically and temporally close comparison group. Such a comparison permits additional conclusions about the degree to which providers have biases that are similar to or different from other members of the community. No research to date has provided such a comparison.
Setting
Participants were drawn from three different clinical settings: Denver Health (DH), Kaiser Permanente Colorado (KPCO), and the State Network of Colorado Ambulatory Practices & Partners (SNOCAP). DH is a public institution nationally recognized for its model of care to underserved, indigent and minority patients. DH provides approximately 42% of the indigent care in the Denver area, and over 25% of Denver residents use DH. KPCO is a private, group-model non-profit HMO providing integrated health care services in Colorado, serving approximately 16% of Denver metro residents. SNOCAP is an association of practice-based research networks, including traditional private medical practices and federally qualified health centers.
Participants and Procedure
Across the three settings, 351 Family Medicine and General Internal Medicine PCPs in 34 offices in the Denver metro area were eligible to participate in this study. The study PI (Blair) and a physician co-investigator from each of the organizations gave presentations at each clinic and invited every PCP, individually, to participate via secure website with assigned passcodes. The study team and participating organizations were blinded to PCP participation by, (a) the administration of anonymous informed consent on the website, (b) all PCPs, regardless of participation, were given the study incentives ($10 giftcard and the book Blink by Malcolm Gladwell), and (c) all PCPs were sent two personalized reminders following the initial invitation to participate.
Concurrently, community members were recruited from the waiting areas of clinics in the same organizations. Community members completed the study measures on laptop computers and received a $20 giftcard. The study was approved by the Colorado Multiple Institutional Review Board and the KPCO IRB.
Measures
Implicit Bias
The IAT measures the strength with which concepts (e.g., Black and White people) are associated with attributes (e.g., good and bad).18,19,22 Stimulus items from four categories appear on a computer screen and participants are asked to categorize them, one at a time, by pressing the computer key that corresponds to the correct category. During one critical block of trials, for example, the “f” key must be pressed when either a Black face or a “good” word appears on the screen, while the “j” key must be pressed for a White face or a “bad” word. In another critical block of trials, the response pairings are reversed such that participants must categorize Black faces and bad words using the same key, and White faces and good words using the other key. If the concepts sharing a response key are associated, participants ought to be faster to categorize the stimulus items compared to when the concepts sharing a response key are not associated. The majority of White respondents, for example, are significantly faster when Black faces and bad words require the same response while White faces and good words require another response, compared to the reversed pairing.2022 The larger this performance difference, the stronger the implicit bias for a particular person. Demonstrations of this test can be found online at https://implicit.harvard.edu.
The IAT has been used in more than 700 studies across a wide array of disciplines, including psychology, health, education, political science, and market research.15,19 The IAT has greater documented reliability and validity than other implicit measures, and its methodological strengths and limitations have been extensively reviewed.15,19,22,37
Two IATs were administered in this study, one to measure implicit bias against Latinos compared to Whites and another to measure implicit bias against African Americans compared to Whites. These IATs were developed and validated using a separate community sample.38 Each IAT required participants to categorize faces as Latino vs. White (first IAT) or as Black vs. White (second IAT) while also categorizing words as positive vs. negative. The order of the two critical blocks within each IAT (e.g., Latino+positive and Latino+negative) was randomized across participants. Community participants had the option to complete the IATs in either English or Spanish.
Explicit Bias
Participants were asked to indicate their explicit attitudes toward African Americans, Latinos and Whites on two standard measures39,40: the Feeling Thermometer (0 – 100 for “cool” to “warm” feelings), and a set of semantic differential scales (7-point trait-ratings of “hard-working – lazy,” “wise – foolish,” and “cooperative – hostile”).
Demographic Characteristics
PCPs were asked to indicate their gender, age, ethnic/racial identification, Spanish fluency, medical specialty and how many years post-residency they had been practicing medicine. Community participants were asked the same questions, except instead of medical specialty and years of medical practice, they were asked about their household income and level of education. Due to the sensitivity of the research, results were aggregated across the three participating organizations to prevent the identification of data from any one.
Statistical Analysis
Of primary interest were, (a) the mean and heterogeneity (variance) of PCPs’ implicit and explicit ethnic/racial biases, (b) the degree to which biases differ between PCPs and community members, overall and within the majority ethnic/racial group (Whites), and (c) the relations among implicit and explicit attitudes for both samples.
All of the bias measures were scored such that higher numbers indicate greater bias against the minority group compared to Whites, with a score of 0 indicating no bias. IAT scores were calculated following the recommendations of Greenwald and colleagues,41 with separate IAT scores for Latino:White implicit bias and Black:White bias for each participant. Comparable explicit bias scores were created for the thermometer measure by subtracting the warmth rating for each minority group from the rating for Whites. Explicit bias scores were created for the trait rating measure by first averaging the three trait ratings for each group, and then subtracting the average rating for Whites from the average rating for each minority group.
The primary analyses examined each of the six bias scores for significant departures from zero within each sample (PCP or community members) and for differences between the two samples. These single degree-of-freedom tests were conducted using general linear models. Tests of between-sample differences were repeated in multivariate models that adjusted for participants’ background characteristics (e.g., race/ethnicity). To avoid over-fitting the models, only characteristics that revealed consistent relations to bias – potential confounders – were included.
Bivariate correlations between continuous variables were estimated using Spearman’s statistic; correlations involving non-continuous variables were estimated with the Kendall Tau coefficient; chi-square tests of association were used for categorical characteristics. All statistical tests were 2-sided and were considered significant at alpha=.05. Effect sizes are reported in terms of Cohen’s d with “small,” “medium” and “large” effects defined as d= .20, .50 and .80, respectively. All statistical analyses were performed using SAS version 9.1 (SAS Institute Inc, Cary, NC).
Participants
Of the 351 eligible PCPs, 210 (60%) participated. Of the 375 community members approached, 205 were consented and 190 provided useable data (final response rate of 51%). The PCP and community samples differed in several ways (see Table 1): PCPs had higher socio-economic status, reported greater fluency in speaking Spanish, and were more likely to be White and 36–55 yrs old.
TABLE 1
TABLE 1
Social-Demographic Characteristics of Participating PCPs and Community Members
Implicit Bias
Table 2 provides the mean, standard deviation and size of effect for each IAT. Figure 1 provides a visual display of the range of scores obtained on the IATs, by PCPs and community members. As this figure shows, there was a wide range of implicit biases among both the PCPs and the community members, including greater positivity toward each minority group than toward Whites. However, as hypothesized, the more frequent and stronger response was greater positivity toward Whites (i.e., bias against the minority groups): the average Latino:White IAT score indicated strong bias against Latinos compared to Whites for both PCPs (Cohen’s d=.87) and community members (Cohen’s d=.69); the average Black:White IAT score indicated strong bias against African Americans compared to Whites, again for both PCPs (Cohen’s d=.79) and community members (Cohen’s d=.72). Scores on the Latino:White IAT and scores on the Black:White IAT were correlated to a moderate extent for both PCPs (r=.44) and community members (r=.49).
TABLE 2
TABLE 2
Levels of Implicit and Explicit Ethnic/Racial Bias Among PCPs and Community Members
FIGURE 1
FIGURE 1
Percentage of providers and community members in each scoring category of the Latino:White IAT and the Black:White IAT
Table 3 provides the model estimates from tests of differences in bias between the PCPs and community members, both unadjusted and adjusted for relevant background characteristics (i.e., those showing bivariate associations with bias scores). In the unadjusted analysis, the PCPs and community members did not differ in implicit bias on the Latino:White IAT, t(396)=0.93, p=.35, or the Black:White IAT, t(395)=0.38, p=.70. In the adjusted analysis, the PCPs were found to have somewhat less implicit bias than community members on both the Latino:White IAT, t(371)=−1.93, p=.05, and the Black:White IAT, t(371)=−2.64, p<.01.
TABLE 3
TABLE 3
Unadjusted and Adjusted Models Testing for Differences Between PCPs and Community Members on Latino:White and Black:White Bias Measures
Explicit Bias
As expected, explicit bias was weak to non-existent (MCohen’s d =.04) among both PCPs and community members. In the unadjusted analysis comparing PCPs and community members, PCPs reported somewhat greater positivity toward Whites on the Black:White thermometer measure but not on the Black:White trait measure, the Latino:White thermometer measure or the Latino:White trait measure (Table 3). Multivariate adjustment for background characteristics eliminated the difference between PCPs and community members in thermometer ratings of African Americans (Table 3).
Across participants, implicit and explicit biases were only modestly related, with correlations somewhat stronger for the thermometer measures of explicit bias (r= .27 and .28, for Latino:White and Black:White, respectively) than the trait-rating measures (r= .13 and .12, for Latino:White and Black:White, respectively).
Subgroup Analysis of White Participants
Comparisons between PCPs and community members were repeated with just the White participants; there were too few Latino or Black PCPs to analyze those groups separately. In this analysis, White PCPs showed less bias against minorities than did White community members on five of the six measures: Latino:White IAT (p<.02), Black:White IAT (p < .01), Latino:White Thermometer (p<.05), Latino:White trait ratings (p<.0001), and Black:White trait ratings (p<.05). The sixth measure, the Black:White thermometer, showed no difference between the subgroups (p=.23).
This study confirms prior findings of implicit bias against African Americans with a sample of more experienced providers working in three different health care settings and with a higher response rate than obtained in prior work35. More importantly, however, is the new finding of substantial implicit bias against Latinos, a target group that has been neglected in research on ethnic/racial bias. Approximately two thirds of the providers in this sample demonstrated implicit bias against Latinos, even as they explicitly reported egalitarian attitudes toward the group. Neither implicit nor explicit bias against Latinos was related to the providers’ age, gender, medical specialty or years practicing medicine.
Comparisons between the providers and community members using the same clinics revealed no substantial differences in ethnic/racial biases. These null results suggest that the implicit biases observed are not a problem particular to health care professionals, but reflect broader community or societal issues. The remarkable similarity between providers and community members raises the question of how those similarities are perceived. Is it enough for patients that no more bias is likely to appear within the health care setting than outside, or are health care providers held to a higher standard? What is the standard to which providers hold themselves?
Although it is common practice to focus on the central tendencies of a group, it is important not to lose sight of the differences that appear among individuals. In this study, approximately 18% of the providers showed no implicit bias when considering Latinos and 28% showed no implicit bias when considering African Americans. These are not insubstantial numbers and they suggest a somewhat different approach to the problem of health disparities. That is, instead of focusing on what biased providers might be doing wrong, it may in fact be more productive to consider what this select group of providers is doing right. Do they have an approach that allows them to work more effectively with diverse patients? Do patients seek out these providers as a means to work within a system that otherwise seems biased? What allows these providers to have attitudes that are both implicitly and explicitly egalitarian? Can it be taught to others?
Although research is just beginning on the conditions under which implicit bias may or may not affect health care,29,31,33,34 one may wonder whether anything can be done to combat an unintentional or even unconscious process. Laboratory research in social psychology shows that implicit bias is potentially malleable and does respond to changes in situational cues and social norms.42 These laboratory methods have yet to be adapted and tested in clinical setting, but the findings nonetheless suggest the real possibility of change. Additional interventions may also be developed for other points of contact, for example by bolstering patients’ defenses against bias or altering care delivery systems to mitigate the effects of bias.
The general lack of explicit bias against both African Americans and Latinos (i.e., generally egalitarian explicit attitudes) is noteworthy because it points to the types of judgments and behaviors that may contribute to ethnic/racial disparities in health care and the situational factors that are likely to exacerbate the problem. In particular, explicit egalitarian attitudes are more likely to produce egalitarian outcomes when (a) a person is thinking more deeply about what he or she is doing, (b) the situation contains fewer competing demands on the person’s time and attention, and (c) the relevant evidence is clear and consistent.8,9,10,13,14 Health care encounters that lack one or more of those conditions would be less likely to gain the benefits of providers’ explicit egalitarian attitudes, and simultaneously more likely to suffer from implicit biases.43 The lack of explicit bias among PCPs also suggests that widely practiced efforts to combat this form of bias (i.e., rational arguments about the importance of cultural sensitivity) may be ineffective.
The strengths of this study include the assessment of bias against both Latinos and African Americans, the sampling of experienced PCPs across three clinical settings that represent different models of health care delivery in the U.S., and the comparison of PCPs with other members of the community. Our 60% participation rate is also higher than most prior studies on this topic.35
Most research on implicit bias has occurred in laboratory settings with narrow populations (college students) or with un-denominated volunteers. Moving the research into actual health care settings permits stronger conclusions about the potential effects of implicit bias in health care, specifically, and at the same time it also validates the laboratory work in the consistency of results.
The limitations of the research begin with the possibility that response bias affected the results. The study is also limited in its focus on primary care providers and community members within a clinical setting. Because our provider sample is predominantly White, we have inadequate power for detailed sub-analyses of other ethnic/racial groups of PCPs. Our study also does not address the link between providers’ (implicit) ethnic/racial biases and actual health disparities, an important next step.
The patient-provider relationship remains at the center of health care, increasing the stakes for assessing and addressing ethnic/racial biases among providers. The findings of the present study contribute to an understanding of provider bias in several ways. We have added new evidence of implicit ethnic/racial biases among experienced primary care providers particularly with regard to bias against Latinos. At the same time, we have shown that a number of providers do not appear to have such biases, and nearly all providers have generally egalitarian attitudes explicitly. Such evidence is important as a guide for future research on the pathways through which bias may operate and the types of interventions most likely to be effective in eliminating ethnic/racial disparities.
Acknowledgments
We thank Leslie Wright, Elhum Karimkhani and Lee Eakin for their assistance in collecting the data and managing the study. Funding for this work was provided by the National Heart Lung and Blood Institute, National Institutes of Health, grants HL088198 and HL089623.
Footnotes
Contributors
All authors contributed to the design and conduct of the study, interpretation of the findings and edited drafts of the article. I. V. Blair wrote the initial draft of the article. D. L. Fairclough conducted the data analysis. Both I. V. Blair and D. L. Fairclough had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Human Participant Protection
The research protocol, including informed consent procedures, received full ethical review and approval from the Colorado Multiple Institutional Review Board and the Institutional Review Board of Kaiser Permanente Colorado.
Contributor Information
Irene V. Blair, Department of Psychology and Neuroscience, University of Colorado Boulder.
Edward P. Havranek, Division of Cardiology, Denver Health.
David W. Price, Institute for Health Research, Kaiser Permanente Colorado.
Rebecca Hanratty, Department of Internal Medicine, Denver Health.
Diane L. Fairclough, Colorado Health Outcomes Program, University of Colorado Denver.
Tillman Farley, Salud Family Health Centers and the State Networks of Colorado Ambulatory Practices & Partners.
Holen E. Katz, Department of Psychology and Neuroscience, University of Colorado Boulder.
John F. Steiner, Institute for Health Research, Kaiser Permanente Colorado.
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