In the United States (US), Blacks at every level of income and education face higher rates and earlier onset of cardiovascular disease (CVD) compared to Whites. Racism, which manifests at multiple social levels, is gaining recognition as a driver of health inequity. On an interpersonal level, numerous studies have associated racial discrimination with CVD outcomes among Blacks. Findings are nuanced, with the highest risk often emerging among those reporting the lowest levels of discrimination. Nonlinear associations may result from underreporting due to denial and self-blame. On a more structural level, an emerging area of work uses ?Big Data? to capture ?collective racial bias,? or the average amount of anti-Black bias in a defined geographic area. Early evidence has linked collective racial bias with racial disparities in CVD-related mortality, however the psychosocial and biologic pathways to health remain elusive. Moreover, the effect of collective racial bias on cardiovascular risk during young- to middle-adulthood, a salient window in the etiology of CVD, is unknown. The proposed study aims to address these gaps by leveraging Big Data from Google and Project Implicit to capture collective racial bias at the area-level across the US. We will apply collective racial bias measures to data from the National Longitudinal Study of Adolescent to Adult Health, a national cohort study with rich social, behavioral, and health outcome data collected over 30 years of follow-up. We will examine longitudinal, multilevel associations of collective racial bias with system-specific biomarkers and incident conditions indicative of cardiovascular risk progression from young adulthood (ages 24-32) to middle adulthood (ages 32- 42) among non-Hispanic Blacks (N=3,494) and non-Hispanic Whites (N=8,266); and explore whether associations are mediated and/or moderated by individuals? self-reported experiences of discrimination. Study strengths include: 1) combining Big Data with a rich longitudinal cohort study to examine multilevel associations between area racism and disease progression; 2) exploring mediation and moderation by self- reported discrimination to better understand the psychosocial mechanisms linking collective racial bias to health and potentially clarify nuanced findings in the self-reported discrimination and health literature; 3) explicitly focusing on the development of cardiovascular risk from young- to middle-adulthood, an important etiologic window marked by the emergence of cardiovascular risk and widening racial disparities; and 4) examining cardiovascular risk across multiple systems to better understand biologic mechanisms to health. Through the completion of this fellowship, I will develop the analytic and professional skills needed to begin my career as an empowered social epidemiologist researching the multilevel determinants of racial disparities in CVD across the lifecourse. After earning my PhD in epidemiology from UC Berkeley, I plan to pursue post- doctoral training before seeking a position as an early-investigator at an academic research university.
The proposed research aims to better understand the association between contextual-level racism and Black- White cardiovascular disparities in the United States, particularly the accelerated cardiovascular risk experienced among Blacks. Innovative ?Big Data? from Google and Project Implicit will measure place-based collective racial bias, which will be examined longitudinally in relation to multisystem biomarkers and incident conditions indicative of cardiovascular risk among a large, nationally representative sample of participants from young- to middle-adulthood. By exploring biologic and psychosocial pathways linking collective racial bias with cardiovascular risk progression among younger adults, this study is uniquely poised to address gaps in the current literature and inform multilevel and age-appropriate interventions during a salient window in the etiology of cardiovascular disparities.
