Objectives. To improve measurement of discrimination for health research, we sought to address the concern that explicit self-reports of racial discrimination may not capture unconscious cognition.

Methods. We used 2 assessment tools in our Web-based study: a new application of the Implicit Association Test, a computer-based reaction-time test that measures the strength of association between an individual's self or group and being a victim or perpetrator of racial discrimination, and a validated explicit self-report measure of racial discrimination.

Results. Among the 442 US-born non-Hispanic Black participants, the explicit and implicit measures, as hypothesized, were weakly correlated and tended to be independently associated with risk of hypertension among persons with less than a college degree. Adjustments for both measures eliminated the significantly greater risk for Blacks than for Whites (odds ratio = 1.4), reducing it to 1.1 (95% confidence interval = 0.7, 1.7).

Conclusions. Our results suggest that the scientific rigor of research on racism and health will be improved by investigating how both unconscious and conscious mental awareness of having experienced discrimination matter for somatic and mental health.

A small but fast-growing body of public health research is investigating the association between self-reported experiences of racial discrimination and population health.15 To date, the strongest positive and generally linear associations have been observed for what are also the most commonly studied outcomes: self-reported psychological distress and self-reported health behaviors (e.g., smoking, alcohol use, and other drug use).15 By contrast, evidence for somatic health outcomes, chiefly cardiovascular health, has been more mixed, with studies variously reporting linear, nonlinear, and no associations between self-reported experiences of discrimination and health status.15

The largest published study on racial discrimination and risk of elevated blood pressure reported a positive linear (dose–response) relationship among professional Blacks but a curvilinear association among working-class Blacks, whose data revealed a J-shaped curve (i.e., blood pressure was higher among respondents reporting no discrimination than among those reporting moderate discrimination and highest among those reporting the most discrimination).6 This phenomenon of a linear relationship among persons with more socioeconomic resources but a J-shaped curve among those with fewer has been replicated in other studies on self-reported discrimination and health.7,8

One hypothesis that accounts for these results concerns what people are willing or able to say.1,6 For persons with more socioeconomic resources, a response of no discrimination may more accurately capture the lack of personally experienced discrimination, whereas among persons with fewer socioeconomic resources, the same response may reflect an accurate report of no discrimination; a positive illusion, denial, or internalized oppression that is not consciously perceived; or a conscious decision not to report the discrimination because it is uncomfortable or dangerous to do so.1,6 The latter 2 scenarios imply that a health risk may exist, via pathways involving physiological and behavioral responses to racial discrimination as a psychosocial stressor,15 with bodies revealing health effects of exposures that can be subject to distortions in perceptions, memory, and rationalizations.1,9 If so, reliance solely on self-report measures of racial discrimination, which has been standard to date,15 may be problematic.1

The need to critically assess measurement of exposure to discrimination is demonstrated by what psychologists term person–group discrimination discrepancy (PGDD).1015 It is well-documented that people typically report more discrimination for their group than for themselves personally, even though, statistically, the group level cannot be high if every individual's is low. PGDD is postulated to arise from an automatic protective mechanism,16 reflecting a larger psychological tendency to view and present oneself positively, even when such denial may not be in one's ultimate self-interest.1719

During the past decade, social psychologists, borrowing basic methods of cognitive psychology, have developed a new approach—the Implicit Association Test (IAT), a computer-based reaction-time measure—to study phenomena for which self-report data might not fully capture what people think and feel.2023 An outgrowth of several decades of experimental research in the social, cognitive, clinical, and neuropsychological sciences concerned with the general architecture of the human mind, human learning, and unconscious mental processes,20,2428 the IAT is now one of the most robust and widely employed measures in social, cognitive, and even clinical psychology; it is used to assess the ease with which the mind makes associations. Building on the central finding that learning involves changes in neural function of different neurons that are active at the same time, the underlying cognitive principle is that concepts in the mind that are more closely associated with each other are more closely linked. These associations can occur both for conscious cognitive processes and for unconscious mental processes that lie beyond the reach of introspective access, with evidence indicating that implicit (unconscious) associations can form about many different phenomena—the self, other people, places, animals, memories, fantasies, inanimate objects, and nontangible ideas.20,27

For example, an IAT could measure how much a person prefers flowers to bugs by contrasting the time it takes to make associations between the word pairs flowers and good and bugs and bad and then comparing what happens when participants are asked to pair flower with bad and bugs with good. A difference in average matching speed for opposite pairings determines the IAT score, a measure of strength of association. Participants are typically aware that they are making these connections but are unable to control them because of the rapid response times and the structure of the test. IAT methods are well-described in the social psychology literature,21,22 and programming resources to develop IATs are available online.29

To address extant questions about measuring experiences of discrimination,15,30,31 we employed a novel application of the IAT to assess unconscious cognition about discrimination and consider the implications of using both explicit (self-report) and implicit measures for research on racism and health. Our work builds on and extends research that used the IAT to study racial prejudice and stereotypes.3235 Recent health-related studies found that the IAT for racial prejudice can predict physicians' clinical decisions.36,37 Focusing on the somatic health of Black Americans, we hypothesized that the explicit and implicit measures would be weakly associated with each other (because of their varying abilities to measure unconscious processes) and independently associated with risk of hypertension (as modified by socioeconomic position) and with the greater hypertension risk among Blacks than Whites in the United States.

We recruited our study population in 2007 and 2008 from the Project Implicit Web site, the leading public Web site for online IAT research.23 We restricted study participants to US-born non-Hispanic persons aged 25 to 70 years who self-identified as being either (1) Black or African American or (2) White, chosen because these are the 2 groups most often studied in research on racism and cardiovascular health. We imposed the nativity and Hispanic restrictions because evidence indicates that US-born and immigrant populations may differently understand, experience, and respond to US racial discrimination.14,30

In a Web-based study, it is not possible to determine the response rate (because we cannot know the number of eligible persons who viewed the Web site but did not take the test). A total of 442 Black and 1018 White persons reported that they met the study eligibility criteria and participated in the study. Average completion time for the survey was 15 minutes, of which the IAT required 10 minutes.

Measures
Sociodemographic questions.

We asked about participants' age, race/ethnicity, nativity, gender, and educational level. We also asked about the educational level of their mother and father, as a measure of childhood socioeconomic position.38,39

Explicit measures of exposure to racial discrimination and response to unfair treatment.

We used the validated 9-item self-report Experiences of Discrimination (EOD) instrument (available without charge at http://www.hsph.harvard.edu/faculty/NancyKrieger.html).6,4042 This measure asks participants whether they have ever “experienced discrimination, been prevented from doing something, or been hassled or made to feel inferior in any of the following situations on account of your race, ethnicity or color,” and about the frequency of occurrence within each situation. The 9 situations are at school; getting hired or getting a job; at work; getting housing; getting medical care; getting service in a store or restaurant; getting credit, bank loans, or a mortgage; on the street or in a public setting; or from the police or in the courts. Each “yes” reply was scored as 1; the total EOD situation score range was 0 to 9. Previous research,6,4042 including on blood pressure, informed our categorization of the EOD situation measure as 0 = no discrimination, 1 to 2 = moderate discrimination, or 3–9 = high discrimination.

Two single-item global measures assessed how frequently (never, rarely, sometimes, or often) the respondents felt they had been discriminated against because of their “race, ethnicity, or color” (personal) and how often they felt that racial/ethnic groups who are not White are discriminated against (group); these questions were previously used in the EOD validation study.41 Two items asked about response to unfair treatment, with the following categories: (1) “accept it as a fact of life” versus “try to do something about it,” and (2) “talk to other people about it” versus “keep it to yourself.”6,4042 To control for how self-presentation might affect the self-report measures,41,42 we used a 5-item validated social desirability scale.43

Implicit measures of exposure to racial discrimination.

As shown in Figure 1a, the IAT was introduced by anchoring language explicitly addressing whether the participant had been a target of discriminatory behavior. It employed 2 sets of targets, (1) self (referred to as IAT-person) and (2) group (IAT-group), and 2 sets of attribute categorization terms: (1) “abuser,” “racist,” and “bigot,” and (2) “target,” “victim,” and “oppressed.” These attribute terms were derived from pilot studies we conducted with Boston-based participants who were college students, persons recruited on the street, or members of a community health center.44 As shown in Figure 1, the IAT's core contrast concerned how quickly participants linked words or images that pertained to self or to their group to words or images that pertained to being a victim or perpetrator of racial discrimination. The difference in speed (in milliseconds) for the 2 associations produced the raw IAT score, which we then normalized by following a standard IAT protocol.22 A score of zero indicated that a participant felt equally like a victim and a bigot, whereas a high score indicated that the participant felt like more of a victim than a bigot, and a low negative score the reverse.

Health data.

We used questions on self-reported health status from the US National Health Interview Survey45 for hypertension and other health outcomes, such as smoking, diabetes, and psychological distress (measured with the K6 scale: range = 0–24; scores ≥ 13 categorized as high psychological distress46,47). We classified participants as being hypertensive if they responded affirmatively to any of the following questions: “Have you ever been told by a doctor or other health professional that you had hypertension, also called high blood pressure?” “Because of your high blood pressure/hypertension, have you ever been told to take prescription medicine?” “Are you now taking prescribed medicine?” To address potential confounding, we obtained data on body mass index (BMI; defined as weight in kilograms divided by height in meters squared), computed from self-reported height and weight.

Statistical Analyses

Our statistical analyses, performed in SAS,48 involved 3 steps. First, we described the distribution of the selected exposure and outcome measures plus related covariates. To put these data in context, we obtained US national data on the corresponding variables.49,50 Second, we quantified the correlations between the different measures of racial discrimination and computed the PGDD for both the explicit and implicit measures. Third, we used logistic regression models to examine risk of hypertension in relation to the explicit and implicit measures of exposure to racial discrimination, with controls for relevant covariates.

Table 1 shows the distribution of the sociodemographic, discrimination, and health characteristics of the 442 Black participants. The average age of the participants ranged from 36 years (women) to 38 years (men), and they were highly educated and had better health than the US Black population overall; they also were much less likely to report no experiences of discrimination than were working-class and less-educated samples of Black Americans.6,44,49,50 For example, 60% of study participants had a bachelor's degree or higher (versus 17% among the US Black population aged 25 years and older in 200650), 23% were hypertensive (versus 40% among the US Black population aged 20–74 years in 2001–200449), and only 3.6% reported never having experienced racial discrimination (versus 33% among the Black participants in the United for Health cohort of low-income, working-class employees in the greater Boston area in 2003 to 200441,42).

Table

TABLE 1 Sociodemographic Characteristics, Explicit and Implicit Measures of Racial Discrimination, and Health Status Among US-Born Black Participants: 2007–2008

TABLE 1 Sociodemographic Characteristics, Explicit and Implicit Measures of Racial Discrimination, and Health Status Among US-Born Black Participants: 2007–2008

Black Participants
External Comparisons
WomenMenTotal PopulationWomenMen
Age,a y, mean (SD)36 (9)38 (10)
Education,b %
    < High school1.00.0US = 19.5US = 21.8
    ≥ High school41.134.8US = 62.4US = 64.7
    ≥ Bachelor's degree57.865.2US = 18.1US = 15.4
Mother's education, %
    < Bachelor's degree68.672.1
    ≥ Bachelor's degree31.428.0
Father's education, %
    < Bachelor's degree77.068.4
    ≥ Bachelor's degree23.031.6
Explicit measures of racial discrimination
EOD situationsc
    0, %4.32.2UH = 33 C = 20UH = 32 C = 23UH = 33 C = 16
    1–2, %18.815.9UH = 22 C = 28UH = 30 C = 29UH = 18 C = 27
    ≥ 3, %77.081.9UH = 45 C = 52UH = 37 C = 48UH = 48 C = 57
    Score, mean (SD)4.5 (2.1)4.2 (2.2)
Global-person (range = 1–4), mean (SD)2.8 (0.6)2.7 (0.6)
Global-group (range = 1–4), mean (SD)3.7 (0.5)3.6 (0.5)
Global PGDDd Wilcoxon signed rank test (P value)−12 327.5 (<.001)−2758.5 (<.001)
Implicit measures of racial discrimination
IAT-person0.37 (0.35)0.33 (0.38)
IAT-group0.40 (0.41)0.40 (0.41)
IAT PGDDe paired t test (P value)−0.25 (.802)−1.08 (.28)
Response to unfair treatmentc
    Talk to others/take action61.160.3UH = 49 C = 75UH = 42 C = 69
    Talk to others/accept as fact of life31.523.5UH = 27 C = 19UH = 25 C = 18
    Keep to self/take action1.75.2UH = 8 C = 3UH = 10 C = 5
    keep to self/accept as fact of life5.711.0UH = 12 C = 4UH = 20 C = 8
Social desirability3.8 (0.6)3.7 (0.6)
Hypertensionf21.326.7US = 40US = 38
High psychological distressg0.00.0US = 4.2
Current smokerh18.014.6US = 19US = 25
Diabetesi6.35.8US = 11.6

Note. C = Coronary Artery Risk Development in Young Adults (CARDIA) study; EOD = Experiences of Discrimination instrument; IAT = Implicit Association Test; PGDD = person-group discrimination discrepancy; UH = United for Health study; US = US population. Sample size for women was n = 304; for men, n = 138. Missing data < 1% for all variables other than smoking (< 5%); all values derived from observed (nonmissing) data.

aRange = 25–70 years

bComparison data are for US population, 2006 (aged ≥ 25 years).50

cComparison data are from United for Health study, 2003 to 2004,41,42 and CARDIA study, 1992 to 1993.6

dComparison data are from Web-based study internal comparison of PGDD (global-person minus global-group).

eComparison data are from Web-based study internal comparison of PGDD (IAT-person minus IAT-global).

fComparison data are for US population, 2001 to 2004 (aged 20–74 years, age-adjusted).49

gComparison data are for US population, 2005 to 2006 (aged ≥ 18 years, age-adjusted, not stratified by gender).49

hComparison data are for US population, 2006 (aged ≥ 25 years, age-adjusted).49

iComparison data are for US population, 2001 to 2004 (aged ≥ 20 years, age-adjusted, not stratified by gender).49

The nonequivalence of the explicit and implicit measures is also shown by our divergent PGDD results (Table 1). The explicit measure produced the expected gap: participants reported more discrimination for their group than for themselves personally (P < .001). By contrast, the implicit measure produced no such gap (women, P > .80; men, P > .28). Moreover, as shown in Table 2, the explicit EOD situation score and the 2 implicit measures, the IAT-person and the IAT-group, were, as expected, weakly correlated (Pearson correlation coefficients < 0.17).

Table

TABLE 2 Correlations Between Explicit and Implicit Measures of Racial Discrimination Among US-Born Male and Female Black Participants, Overall and Stratified by Educational Level: 2007–2008

TABLE 2 Correlations Between Explicit and Implicit Measures of Racial Discrimination Among US-Born Male and Female Black Participants, Overall and Stratified by Educational Level: 2007–2008

Women
Men
IAT-Person, Pearson Correlation Coefficient (P Value)IAT-Group, Pearson Correlation Coefficient (P Value)EOD, Pearson Correlation Coefficient (P Value)IAT-Person, Pearson Correlation Coefficient (P Value)IAT-Group, Pearson Correlation Coefficient (P Value)EOD, Pearson Correlation Coefficient (P Value)
Total populationa
    IAT-person1.0001.000
    IAT-group0.175 (.002)1.0000.102 (.224)1.000
    EOD score0.061 (.293)0.152 (.008)1.000−0.034 (.693)0.075 (.381)1.000
Less than a Bachelor's degreeb
    IAT-person1.0001.000
    IAT-group0.152 (.087)1.0000.189 (.120)1.000
    EOD score0.063 (.481)0.168 (.059)1.0000.067 (.650)0.068 (.647)1.000
Bachelor's degree or higherc
    IAT-person1.0001.000
    IAT-group0.210 (.006)1.0000.054 (.612)1.000
    EOD score0.056 (.466)0.128 (.093)1.000−0.106 (.322)0.067 (.528)1.000

Note. EOD = Experiences of Discrimination instrument; IAT = Implicit Association Test.

aWomen, n = 302; men, n = 183.

bWomen, n = 127; men, n = 48.

cWomen, n = 174; men, n = 90.

Table 3 first provides results for analyses, stratified by educational level, that assess the association between the explicit and implicit measures with risk of hypertension among the Black participants (crude and adjusted for relevant covariates). It then shows the effect of adjusting for these measures on Black–White comparisons of hypertension risk. The first set of these analyses revealed different associations by educational level. Among the Black participants with less than a bachelor's degree, the EOD and IAT-person in the fully adjusted model (model 4) tended to be independently associated with hypertension. For the EOD, the odds of having hypertension were 2.3 times as high among participants reporting no discrimination as they were among those reporting moderate discrimination (albeit with wide confidence intervals [CIs]; 95% CI = 0.1, 39.7) and 4.1 times as high (95% CI = 0.8, 20.5) among persons reporting high as they were among those reporting moderate discrimination, suggestive of a J-shaped curve. The odds of being hypertensive likewise tended to increase positively with the IAT-person score (odds ration [OR] = 2.2; 95% CI = 0.7, 6.8). By contrast, we found no associations between risk of hypertension and either the IAT or EOD score among participants with a bachelor's degree or higher.

Table

TABLE 3 Explicit and Implicit Measures of Racial Discrimination and Their Association With Risk of Hypertension Among US-Born Black Participants, Stratified by Educational Level and Effect of Discrimination on Black Versus White Risk of Hypertension: 2007–2008

TABLE 3 Explicit and Implicit Measures of Racial Discrimination and Their Association With Risk of Hypertension Among US-Born Black Participants, Stratified by Educational Level and Effect of Discrimination on Black Versus White Risk of Hypertension: 2007–2008

Discrimination MeasureModel 1, OR (95% CI)Model 2, OR (95% CI)Model 3, OR (95% CI)Model 4, OR (95% CI)
Less than a Bachelor's degree (Blank participants only; n = 154)
EOD score
    02.9 (0.2, 39.7)2.1 (0.1, 34.9)2.3 (0.1, 39.7)
    1–2 (Ref)1.001.001.00
    > 33.8 (0.9, 17.2)4.6 (0.9, 23.0)4.1 (0.8, 20.5)
IAT-person2.1 (0.7, 5.9)2.5 (0.8, 7.6)2.2 (0.7, 6.8)
IAT-group1.4 (0.6, 3.5)1.0 (0.4, 2.6)1.0 (0.4, 2.8)
Bachelor's degree or higher (Blank participants only; n = 243)
EOD score
    01.5 (0.2, 8.8)1.2 (0.2, 9.4)1.2 (0.2, 9.3)
    1–2 (Ref)1.001.001.00
    ≥ 31.1 (0.5, 2.4)1.2 (0.5, 2.7)1.2 (0.5, 2.9)
IAT-person0.6 (0.3, 1.4)0.8 (0.3, 1.9)0.7 (0.3, 1.9)
IAT-group0.7 (0.3, 1.5)0.7 (0.3, 1.7)0.7 (0.3, 1.7)
Effects of adjusting for implicit and explicit discrimination on Black versus White risk
Blacks vs Whitesa1.4 (1.1, 1.96)1.4 (1.0, 1.9)1.1 (0.7, 1.7)

Note. OR = odds ratio; CI = confidence interval; EOD = Experiences of Discrimination instrument; IAT = Implicit Association Test. EOD scores are explicit measures, and IAT values are implicit measures. Model 1 used bivariate regression. Models 2, 3, and 4 used multivariate regression and adjusted for gender, age, body mass index, social desirability, response to unfair treatment, mother's education, and father's education.

aFor Blacks, n = 397; for Whites, n = 1018. For this comparison, model 1 used bivariate regression; model 2 used multivariate regression and adjusted for age, gender, body mass index, and education (respondent's, mother's, and father's); and model 3 used multivariate regressions and adjusted for model 2 factors plus implicit and explicit measures of racial discrimination, response to unfair treatment, and social desirability.

Finally, even within our healthy study population, the odds of being hypertensive were 1.4 times as high among the Black as they were among the White participants in both the bivariate analysis (model 1; 95% CI = 1.1, 1.9) and the analyses adjusted for sociodemographic factors and BMI (model 2; 95% CI = 1.0, 1.9).

Contributions from both conscious and unconscious awareness of racial discrimination to these observed disparities were suggested by analyses that compared the Black–White risk after further taking into account the EOD and IAT data. The distribution among the 1077 White participants of EOD scores was 48% for 0; 36% for 1 or 2; and 16% for 3 or higher. Among Whites, the IAT-person scores had a mean ±SD = 0.11 ±0.35, and the IAT-group scores had a mean ±SD = 0.07 ±0.40. Additional controls for the explicit and implicit measures eliminated the excess risk among Black respondents and reduced the OR to a nonsignificant 1.1 (model 3; 95% CI = 0.7, 1.7).

Our study makes 2 useful contributions to improving the science of studying racial discrimination and health. First, we demonstrated the feasibility of adapting the IAT for population research on experiences of discrimination. Second, we provided evidence that explicit and implicit measures of racial discrimination are not only distinct but also may both matter for health, albeit in ways that, as predicted, vary by socioeconomic position. Specifically, we found that among Black participants, regardless of educational level, the explicit and implicit measures of racial discrimination were, as hypothesized, weakly correlated, and the implicit measures did not exhibit the personal–group discrimination discrepancy of the explicit measures. Both the explicit and implicit measures tended to be independently associated with risk of hypertension among less-educated Black participants, and jointly controlling for both the explicit and implicit measures eliminated the observed significantly greater risk for hypertension among Black than among White respondents.

Limitations

Our results are subject to several caveats. First, the data were cross sectional, limiting causal inference; the cross-sectional weak correlations between the explicit and implicit measures nevertheless provided important evidence that they assessed different experiences. Second, our Black participants had a much higher level of education and better health status than does the larger US Black population, a reflection of who uses the Internet (lower-income persons less than higher-income persons and Blacks and Hispanics less than Whites, even at the same income level51). This constrained socioeconomic heterogeneity in our sample reduced the likelihood of observing exposure–outcome associations and hence narrowed the range of variation in the study covariates.42,52 It also contributed to a very low prevalence of explicit self-reports of no discrimination, so that we could not test hypotheses about whether IAT scores varied by sociodemographic characteristics or health status among persons explicitly reporting no discrimination.

Third, we relied on self-report data for hypertension. Validation studies, however, show that the US National Health Interview Survey45 questions we used yield estimates of hypertension prevalence highly correlated with those based on blood pressure measurement, among both Black and White Americans.53 The validity of our hypertension analyses was also bolstered because we not only stratified by the participant's educational level but also controlled for several key covariates (i.e., age, gender, and BMI, as well as the educational level of the participants' mother and father, thereby capturing important aspects of both early life and adult socioeconomic position, both of which are related to increased risk of adult hypertension54).

A further concern is whether the IAT accurately measures exposure to racial discrimination, as directed at the self (IAT-person) and group (IAT-group). The test explicitly referred to discriminatory behavior, used target terms that explicitly referred to discrimination (e.g., “victim of discrimination,” “feels effects of prejudice”). It empirically assessed whether people were more likely to think that they themselves and the specified group were more likely to be the target than perpetrator of discrimination, with the detected difference in association times posited to reflect the existence of neural pathways shaped by previous experience with racial discrimination. Whether this previous experience pertains to actual experience, as specified in the anchoring language, rather than a more general fear, however, remains unknown.

A related and now resolved controversy concerned whether the IAT actually measures people's own attitudes rather than larger cultural attitudes.55 This criticism has been put to the test in the empirical literature, with the evidence indicating that the IAT in fact measures people's own attitudes. Specifically, a new comprehensive meta-analytic review of the IAT and its correlates unequivocally found, especially in the domain of racial prejudice and stereotyping, that the IAT systematically predicts real, personal, important, and clinically significant outcomes.56

Implications

The preliminary nature of our findings notwithstanding, we believe they have several implications for research on racial discrimination and health. First, they suggest the need for a robust research agenda investigating the properties of IATs designed to measure different types of experiences of discrimination. Topics warranting research include (1) whether these IATs capture individuals' actual experiences instead of fears of discrimination, including specific types of discrimination rather than unfair treatment more generally; (2) their correlations (or lack thereof) with explicit measures of discrimination; and (3) their associations with diverse health outcomes. Such research should also take into account that mental awareness of racial discrimination is only 1 component of how racism can harm health, because of the evidence that discrimination can increase the risk of adverse health behaviors (e.g., cigarette smoking, excessive alcohol consumption, adverse drug use, and harmful “comfort” eating) that can harm health independent of any physiological stress response15 and can involve additional pathways, such as social and economic deprivation; adverse exposure to toxins, pollutants, and pathogens; targeted marketing of licit and illicit harmful substances (e.g., tobacco and alcohol); and inadequate and degrading medical care.1

Second, our findings confirm that the term “perceived discrimination” (frequently used in the literature on racism and health25,31) should not be conflated with self-reports of racial discrimination. Instead, as suggested by the weak correlations between our IAT and EOD scores, these constructs are distinct, not synonymous, and should not be used interchangeably.1,6,41 Self-reports are what people are willing or able to report, and this may not be identical to what they consciously, let alone unconsciously, perceive.1,41,42

Third, our results underscore the importance of triangulating evidence derived from the stories that bodies tell and what people say.1,9,57 The more straightforward linear associations observed between self-reported experiences of racial discrimination and self-reported mental health and health behaviors versus those observed between self-reports and measured somatic health status might, for example, reflect (1) how the former associations depend on shared mental processes underlying self-reports and (2) how current conditions, regardless of previous conditions, can affect current mental state and health behaviors.5860 By contrast, the measurement of somatic health outcomes (many of which, other than acute infections, involve extended etiologic periods), is not affected by self-report cognitive mechanisms and is therefore able to detect nonlinear associations, if extant. Other research has likewise found discordant results for associations of psychosocial stressors with self-reported versus measured health status,61 with reasons for these discrepancies a topic of active debate and investigation.62,63

Our study provides provocative evidence that the scientific rigor of research on racism and health will be improved by investigating how both unconscious and conscious mental awareness of having experienced discrimination matter for somatic and mental health. Research on the validity and utility of jointly using explicit and implicit measures of racial discrimination—such as the EOD along with the IATs we have developed—should be conducted in more representative and larger study populations and in relation to a range of mental health, behavioral, and somatic health outcomes.

Acknowledgments

This study was funded by the Harvard/Robert Wood Johnson Foundation Health & Society Program.

Human Participant Protection

This study was approved by the institutional review board of Harvard University, and all participants gave their informed consent.

References

1. Krieger N. Embodying inequality: a review of concepts, measures, and methods for studying health consequences of discrimination. Int J Health Services. 1999;29:295352. (Republished and updated: Krieger N. Discrimination and health. In: , Berkman L, Kawachi I, eds. Social Epidemiology. Oxford, UK: Oxford University Press; 2000:36–75.) CrossrefGoogle Scholar
2. Williams DR, Neighbors HW, Jackson JS. Racial/ethnic discrimination and health: findings from community studies. Am J Public Health. 2003;93:200208. LinkGoogle Scholar
3. Paradies Y. A systematic review of empirical research on self-reported racism and health. Int J Epidemiol. 2006;35:888901. Crossref, MedlineGoogle Scholar
4. Mays VM, Cochran SD, Barnes NW. Race, race-based discrimination, and health outcomes among African Americans. Annu Rev Psychol. 2007;58:201225. Crossref, MedlineGoogle Scholar
5. Williams DR, Mohammed SA. Discrimination and racial disparities in health: evidence and needed research. J Behav Med. 2009;32:2047. Crossref, MedlineGoogle Scholar
6. Krieger N, Sidney S. Racial discrimination and blood pressure: the CARDIA study of young Black and White adults. Am J Public Health. 1996;86:13701378. LinkGoogle Scholar
7. Caughy MO, O'Campo PJ, Muntaner C. Experiences of racism among African American parents and the mental health of their preschool-aged children. Am J Public Health. 2004;94:21182124. LinkGoogle Scholar
8. Huebner DM, Davis MC. Perceived antigay discrimination and physical health outcomes. Health Psychol. 2007;26:627634. Crossref, MedlineGoogle Scholar
9. Krieger N. Embodiment: a conceptual glossary for epidemiology. J Epidemiol Community Health. 2005;59:350355. Crossref, MedlineGoogle Scholar
10. Crosby FJ. Relative Deprivation and Working Women. New York, NY: Oxford University Press; 1982. Google Scholar
11. Crosby F. The denial of personal discrimination. Am Behav Sci. 1984;27:371386. CrossrefGoogle Scholar
12. Taylor DM, Wright SC, Moghaddam FM, Lalonde RN. The personal/group discrimination discrepancy. Pers Soc Psychol Bull. 1990;16:254262. CrossrefGoogle Scholar
13. Taylor DM, Wright SC, Porter LE. Dimensions of perceived discrimination: the personal/group discrimination discrepancy. In: , Zanna MP, Olson JM, eds. The Psychology of Prejudice: The Ontario Symposium. Vol. 7. Hillsdale, NJ: Erlbaum; 1994:223255. Google Scholar
14. Quinn KA, Roese NJ, Pennington GL, Olson JM. The personal/group discrimination discrepancy: the role of informational complexity. Pers Soc Psychol Bull. 1999;25:14301440. CrossrefGoogle Scholar
15. Olson JM, Roesesc NJ, Meen J, Robertson DJ. The preconditions and consequences of relative deprivation: two field studies. J Appl Soc Psychol. 1995;25:944964. CrossrefGoogle Scholar
16. Hodson G, Esses VM. Distancing oneself from negative attributes and the personal/group discrimination discrepancy. J Exp Soc Psychol. 2002;38:500507. CrossrefGoogle Scholar
17. Holtgraves T. Social desirability and self-reports: testing models of socially desirable responding. Pers Soc Psychol Bull. 2004;30:161172. Crossref, MedlineGoogle Scholar
18. Orne MT. On the social psychology of the psychological experiment: with particular reference to demand characteristics and their implications. Am Psychol. 1962;17:776783. CrossrefGoogle Scholar
19. Rosenberg M. The conditions and consequences of evaluation apprehension. In: , Rosenthal R, Rosnow RL, eds. Artifact in Behavioral Research. New York, NY: Academic Press; 1969:279349. Google Scholar
20. Greenwald AG, Banaji MR. Implicit social cognition: attitudes, self-esteem, and stereotypes. Psychol Rev. 1995;102:427. Crossref, MedlineGoogle Scholar
21. Greenwald AG, McGhee DE, Schwartz JL. Measuring individual differences in implicit social cognition: the implicit association test. J Pers Soc Psychol. 1998;74:14641480. Crossref, MedlineGoogle Scholar
22. Greenwald AG, Nosek BA, Banaji MR. Understanding and using the Implicit Association Test: I. An improved scoring algorithm. J Pers Soc Psychol. 2003;85:197216. Crossref, MedlineGoogle Scholar
23. Project Implicit. Available at: https://implicit.harvard.edu/implicit/research. Accessed March 11, 2009. Google Scholar
24. Hebb DO. The Organization of Behavior: A Neuropsychological Theory. New York, NY: Wiley; 1949. Google Scholar
25. Bliss TV, Lomo T. Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. J Physiol. 1973;232:331356. Crossref, MedlineGoogle Scholar
26. Nisbett RE, Wilson TD. Telling more than we can know: verbal reports on mental processes. Psychol Rev. 1977;84:231259. CrossrefGoogle Scholar
27. Fazio RH. How do attitudes guide behavior? In: , Sorrentino RM, Higgins ET, eds. Handbook of Motivation and Cognition: Foundations of Social Behavior. New York, NY: Guilford; 1986:204243. Google Scholar
28. Stanley D, Phelps EA, Banaji MR. The neural basis of implicit attitudes. Perspect Psychol Sci. 2008;17:165170. Google Scholar
29. Greenwald AG. Implicit association test materials. Available at: http://faculty.washington.edu/agg/iat_materials.htm. Accessed March 11, 2009. Google Scholar
30. Blank RM, Dabady M, Citro CF, eds. Measuring Racial Discrimination. Washington, DC: National Academies Press; 2004. Google Scholar
31. Kressin NR, Raymond KL, Manze M. Perceptions of race/ethnicity-based discrimination: a review of measures and evaluation of their usefulness for the health care setting. J Health Care Poor Underserved. 2008;19:697730. Crossref, MedlineGoogle Scholar
32. Fazio RH, Jackson JR, Dunton BC, Williams CJ. Variability in automatic activation as an unobtrusive measure of racial attitudes: a bona fide pipeline? J Pers Soc Psychol. 1995;69:10131027. Crossref, MedlineGoogle Scholar
33. Dovidio JF, Kawakami K, Johnson C, Johnson B, Howard A. On the nature of prejudice: automatic and controlled processes. J Exp Soc Psychol. 1997;33:510540. CrossrefGoogle Scholar
34. Nosek BA, Smyth FL, Hansen JJ, et al.. Pervasiveness and correlates of implicit attitudes and stereotypes. Eur Rev Soc Psychol. 2007;18:3688. CrossrefGoogle Scholar
35. Dunham Y, Baron AS, Banaji MR. From American city to Japanese village: the omnipresence of implicit race attitudes. Child Dev. 2006;77:12681281. Crossref, MedlineGoogle Scholar
36. Green AR, Carney DR, Pallin DJ, et al.. The presence of implicit bias in physicians and its prediction of thrombolysis decisions for Black and White patients. J Gen Intern Med. 2007;22:12311238. Crossref, MedlineGoogle Scholar
37. Sabin JA, Rivara FP, Greenwald AG. Physician implicit attitudes and stereotypes about race and quality of medical care. Med Care. 2008;46:678685. Crossref, MedlineGoogle Scholar
38. Krieger N, Williams D, Moss N. Measuring social class in US public health research: concepts, methodologies and guidelines. Annu Rev Public Health. 1997;18:341378. Crossref, MedlineGoogle Scholar
39. Shaw M, Galobardes B, Lawlor DA, Lynch J, Wheeler B, Davey Smith G. The Handbook of Inequality and Socioeconomic Position: Concepts and Measures. Bristol, UK: Policy Press; 2007. CrossrefGoogle Scholar
40. Krieger N. Racial and gender discrimination: risk factors for high blood pressure? Soc Sci Med. 1990;30:12731281. Crossref, MedlineGoogle Scholar
41. Krieger N, Smith K, Naishadham D, Hartman C, Barbeau EM. Experiences of discrimination: validity and reliability of a self-report measure for population health research on racism and health. Soc Sci Med. 2005;61:15761596. Crossref, MedlineGoogle Scholar
42. Krieger N, Chen JT, Waterman PD, et al.. The inverse hazard law: blood pressure, sexual harassment, racial discrimination, workplace abuse and occupational exposures in US low-income Black, White, and Latino workers. Soc Sci Med. 2008;67:19701981. Crossref, MedlineGoogle Scholar
43. Hays RD, Hayashi T, Stewart AT. A five-item measure of socially desirable response set. Educ Psychol Meas. 1989;49:629636. CrossrefGoogle Scholar
44. Carney DR, Krieger N, Banaji MR. Implicit measures reveal evidence of personal discrimination. Self Ident. 2010;9:162176. CrossrefGoogle Scholar
45. National Center for Health Statistics. National Health Interview Survey. Available at: http://www.cdc.gov/nchs/nhis.htm. Accessed March 11, 2009. Google Scholar
46. Kessler RC, Andrews G, Colpe LJ, et al.. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol Med. 2002;32:959976. Crossref, MedlineGoogle Scholar
47. Kessler RC, Barker PR, Colpe LJ, et al.. Screening for serious mental illness in the general population. Arch Gen Psychiatry. 2003;60:184189. Crossref, MedlineGoogle Scholar
48. SAS Institute. SAS Language Reference, Version 8. Cary, NC: SAS Institute; 2001. Google Scholar
49. Health, United States, 2007 With Chartbook on Trends in the Health of Americans. Hyattsville, MD: National Center for Health Statistics; 2007. Google Scholar
50. US Census Bureau. American Community Survey (2006 data). Available at: http://www.census.gov/acs. Accessed: July 9, 2008. Google Scholar
51. Mossberger K, Tolbert CJ, McNeal RS. Digital Citizenship: The Internet, Society, and Participation. Cambridge, MA: MIT Press; 2008. Google Scholar
52. Krieger N. Why epidemiologists cannot afford to ignore poverty. Epidemiology. 2007;18:658663. Crossref, MedlineGoogle Scholar
53. Vargas CM, Burt VL, Gillum RF, Pamuk ER. Validity of self-reported hypertension in the National Health and Nutrition Examination Survey III, 1988–1991. Prev Med. 1997;26:678685. Crossref, MedlineGoogle Scholar
54. Lawlor DA, Smith GD. Early life determinants of adult blood pressure. Curr Opin Nephrol Hypertens. 2005;14:259264. Crossref, MedlineGoogle Scholar
55. Olson MA, Fazio RH. Reducing the influence of extra-personal associations on the Implicit Association Test: personalizing the IAT. J Pers Soc Psychol. 2004;86:653667. Crossref, MedlineGoogle Scholar
56. Greenwald AG, Poehlman TA, Uhlmann E, Banaji MR. Understanding and using the Implicit Association Test: III. Meta-analysis of predictive validity. J Pers Soc Psychol. In press. Google Scholar
57. Krieger N, Davey Smith G. Bodies count and body counts: social epidemiology and embodying inequality. Epidemiol Rev. 2004;26:92103. Crossref, MedlineGoogle Scholar
58. Kuh D, Hardy R, Rodgers B, Wadsworth ME. Lifetime risk factors for women's psychological distress in midlife. Soc Sci Med. 2002;55:19571973. Crossref, MedlineGoogle Scholar
59. Bar-Tal Y, Cohen-Mansfield J, Golander H. Which stress matters? The examination of temporal aspects of stress. J Psychol. 1998;132:569576. Crossref, MedlineGoogle Scholar
60. Cohen S, Kessler RC, Gordon LU, eds. Measuring Stress: A Guide for Public Health and Social Scientists. Oxford, UK: Oxford University Press; 1997. Google Scholar
61. Macleod J, Davey Smith G, Metcalfe C, Hart C. Is subjective social status a more important determinant of health than objective social status? Evidence from a prospective observational study of Scottish men. Soc Sci Med. 2005;61:19161929. Crossref, MedlineGoogle Scholar
62. Adler N. When one's main effect is another's error: material vs. psychosocial explanations of health disparities. A commentary on Macleod. Soc Sci Med. 2006;63:846850. Crossref, MedlineGoogle Scholar
63. Macleod J, Davey Smith G, Metcalfe C, Hart C. Subjective and objective status and health: a response to Adler's “When one's main effect is another's error.” Soc Sci Med. 2006;63:851857. CrossrefGoogle Scholar

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Nancy Krieger, PhD, Dana Carney, PhD, Katie Lancaster, MS, Pamela D. Waterman, MPH, Anna Kosheleva, MS, and Mahzarin Banaji, PhDNancy Krieger, Pamela D. Waterman, and Anna Kosheleva are with the Department of Society, Human Development, and Health, Harvard School of Public Health, Boston, MA. Dana Carney is with the Graduate School of Business, Columbia University, New York, NY. Katie Lancaster is with Radboud University Nijmegen, Netherlands. Mahzarin Banaji is with the Department of Psychology, Harvard University, Cambridge, MA “Combining Explicit and Implicit Measures of Racial Discrimination in Health Research”, American Journal of Public Health 100, no. 8 (August 1, 2010): pp. 1485-1492.

https://doi.org/10.2105/AJPH.2009.159517

PMID: 19965567