Researchers typically identify health disparities using self-reported race/ethnicity, a measure identifying individuals’ social and cultural affiliations. In this study, we use data from Waves 1, 3, and 4 of Add Health to examine health disparities by interviewer-ascribed skin color, a measure capturing the perceptions of race/ethnicity ascribed to individuals by others. Individuals with darker-skin tones may face greater exposure to serious stressors such as perceived discrimination, poverty, and economic hardship which can accumulate over the lifecourse and increase the likelihood of poor health. We found significant gradients in Body Mass Index (BMI), obesity, self-reported health, and depressive symptoms by interviewer-ascribed skin color but results differed by gender. Associations of BMI, obesity, and fair/poor health among women were only partially mediated by discrimination, self-reported stress, or low socioeconomic status and persisted after controlling for race/ethnicity. Among men, initial associations between skin color and both fair/poor health and depressive symptoms did not persist after controlling for race/ethnicity. This study demonstrates the value of considering stratification by skin color and gender in conjunction with race/ethnicity.
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Add Health Wave 5 is currently in the field and may be released in 2019–2020.
Among non-Hispanics in this sample, 151 adolescents reported White/Native American identities, 78 reported White/Asian identities, 86 reported White/Black race/ethnic identities, 20 reported Black/Asian identities, and 67 reported Black/Native American identities. In sensitivity analyses, we found that the associations between race/ethnicity and health did not depend on the racial/ethnic category to which multi-race individuals were assigned.
Previous studies have indicated that, interviewers perceive greater variation in skin tones within their own race/ethnicity than within another race/ethnicity (Hill 2002). To evaluate the association of race/ethnic matching of interviewers with respondents on interviewer-ascribed skin color, we estimated four separate ordered logistic regressions of skin color on interviewer’s race/ethnicity (White vs. non-White) among self-identified (1) White, (2) Asian, (3) Hispanic, and (4) Black respondents. Among White and Hispanic respondents, we found no evidence that the skin colors perceived by White interviewers differed from the skin colors perceived by non-white interviewers. However, among Black and Asian respondents, we found that White interviewers had higher odds of perceiving a darker skin color than non-White interviewers. We do not consider this result to be evidence of “bias” in interviewer-ascribed skin color since the aim of the Add Health questions was not to get an unbiased or objective measure of the respondent’s skin color. Instead, the aim was to identify how respondents would be perceived by others. Although interviewer race/ethnicity is correlated with interviewer-ascribed skin color, we have no reason to believe that interviewer race/ethnicity is correlated with a respondent’s health. A variable indicating that the interviewer was the same race as the respondents (1 = yes, 0 = no) in our models of health outcomes was never significant.
When parental income was added to the model, associations of young adult income and education with health remained unchanged.
Because of the high prevalence of obesity, odds ratios calculated from logistic models potentially overestimate the strength of associations and risk ratios calculated from binomial regressions are sometimes preferred. However, scholarship on the use of odds ratios versus risk ratios generally suggests that odds ratios become problematic when they fall outside the range of 0.5–2.5 (Hilbe 2011). Odd ratios for associations with obesity in this study fall below this range.
In additional analyses, we estimated multinomial logistic regressions of selecting a Black, Hispanic, or Asian versus a White racial/ethnic identity on skin color. We found strong positive associations between darker interviewer-assigned skin color and a non-White self-reported identity.
Regression analyses (available upon request) showed that skin color gradients in discrimination and stress did not differ by gender. Skin color gradients in income (Male βcolor = − 0.30 vs. Female βcolor = − 0.65. Wald test = 16.18, p < 0.001) and economic hardship (Male ORcolor = 0.05 vs. Female ORcolor = 0.13 Wald test = 9.98, p < 0.05) were significantly greater among women than among men.
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We would like to thank the UNC Gillings School of Global Public Health for providing the primary funding to Krista M. Perreira for this research project. We are also grateful to the Carolina Population Center for training support (T32 HD007168) and for general support (R24 HD050924) of this research. This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth).
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Perreira, K.M., Wassink, J. & Harris, K.M. Beyond Race/Ethnicity: Skin Color, Gender, and the Health of Young Adults in the United States. Popul Res Policy Rev 38, 271–299 (2019). https://doi.org/10.1007/s11113-018-9503-3
- Skin color/tone
- Health disparities/equity