Intersectional disparities in HIV testing among male youth in the U.S.
Ramachandran, Daesha Valli
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Background: HIV testing remains a significant public health problem, particularly for adolescents who are the fastest growing age group of those infected with HIV. Data on HIV testing among adolescents is more limited and has generally focused on behavioral risk factors associated with HIV risk. Although racial disparities in HIV acquisition and entry into care have been explored, less is known about how structural forces such as institutional racism work through social identities of race and class to influence HIV testing among male youth. In an effort to explain the underlying origins of health disparities in HIV testing, Intersectional Theory is used to ground this dissertation. This approach was chosen for its insight in understanding health disparities as products of myriad interactions between structural forces and the intersecting axes of multiple social identities. Methods: Data for this dissertation come from two sources. Aim1 relies on qualitative data from sixteen in-depth interviews among black male youth ages 18-24. Inductive content analysis was used to explore the connection between structural discrimination and individual identity. Aims 2 and 3 rely on the 2006-2010 National Survey of Family Growth to examine the relationship between race, SES and HIV testing among a nationally representative sample of 15-24-year-old sexually experienced black, white and Hispanic males. Logistic regression analyses were conducted to test whether intersectional disparities in HIV testing are present. Unitary and intersectional models to assess the race-SES relationship with HIV testing are compared. In Aim 3, past-year HIV testing is examined among a subset of male youth who have received STI services in the past year. Multivariate logistic regression models were also used in this analysis to assess intersectional effects of race and SES among those who received STI services. Results: Narratives from heterosexual black males in Baltimore highlight the influence of structural inequalities such as racism, poverty, social instability and incarceration on individual identity. Aims 2 and 3 expand the methodological approach to intersectional analyses. In Aim 2, data demonstrate a greater likelihood of HIV testing for black male youth compared to non-Hispanic whites in the additive model (AOR: 2.35, 95%CI: 1.55, 3.56), although no income effects were noted. In the intersectional model, however, Hispanic versus non-Hispanic differences emerged within income bracket. Race-stratified analyses revealed no within-race income effect but highlighted other socio-structural factors are inconsistently associated with HIV testing across racial groups. In Aim 3 no race effects emerged in the additive model, although when controlling for other possible confounders a lower likelihood of HIV testing for low-income youth was observed (AOR: 0.54, 95% CI: 0.31, 0.93). In the intersectional model, none of the race-income groups revealed any disparities in HIV testing, although older age was significantly associated with greater odds of past-year testing. Stratified analyses reveal that the income effects noted in the additive model were specific to black males in our sample (AOR: 0.29, 95% CI: 0.12, 0.68) with no income effects notes for non-Hispanic white or Hispanic males. Conclusions: This dissertation reaffirms the value of adopting an intersectional approach to health disparities research. Qualitative findings emphasize the importance of assessing how structural inequalities manifest in an individual’s identity. As structural approaches to HIV risk reduction continue to gain traction, ongoing efforts will be maximized if they integrate social identity theory, intersectionality and the way in which structural disparities influence microlevel behavior. Quantitative results underscore the importance of exploring disparities in HIV testing using intersectional methods, as additive models of disparities obscure sub-population-specific effects. By examining our research question though multiple models, we discovered that model specification is critical to understanding potential points of intervention.