COLLECTIVE RACIAL BIAS AND CARDIOVASCULAR RISK AMONG BLACK AND WHITE US ADULTS

项目总结/摘要 在美国，黑人在各个收入和教育水平上都面临着更高的失业率和更早的失业率。 心血管疾病（CVD）与白人相比。种族主义表现在多个社会层面， 作为健康不平等的驱动因素获得认可。在人际关系方面，许多研究都将 种族歧视与黑人心血管疾病的结果。结果是微妙的，最高的风险往往是 在那些报告歧视程度最低的人中出现。非线性关联可能源于 由于否认和自责而少报。在更结构化的层面上，一个新兴的工作领域使用“大 数据”捕捉“集体种族偏见”，或反黑人偏见的平均数额在一个定义的地理区域。 早期证据表明，集体种族偏见与CVD相关死亡率的种族差异有关， 实现健康的社会心理和生物途径仍然难以捉摸。此外，集体种族偏见对 青年至中年期的心血管风险是CVD病因学的一个重要窗口，目前尚不清楚。 这项拟议的研究旨在通过利用Google和Project Implicit的大数据来解决这些差距， 捕捉美国地区层面的集体种族偏见。我们将采取集体种族偏见措施， 数据来自国家青少年到成人健康纵向研究，这是一项全国性的队列研究， 在30年的随访中收集的社会、行为和健康结果数据。我们将纵向检查， 集体种族偏见与系统特异性生物标志物和事件条件的多层次关联 表明从青年期（24-32岁）到中年期（32- 34岁）的心血管风险进展。 42)在非西班牙裔黑人（N= 3，494）和非西班牙裔白人（N= 8，266）中;并探索是否 个人自我报告的受歧视经历对这种联系起了中介和/或调节作用。 研究优势包括：1）将大数据与丰富的纵向队列研究相结合， 地区种族主义与疾病进展之间的关联; 2）探索自我调解和适度， 报告的歧视，以更好地了解将集体种族偏见与 健康和可能澄清自我报告的歧视和健康文献中的细微差别; 3） 明确关注从年轻到中年的心血管风险的发展，这是一个重要的 以心血管风险的出现和种族差异的扩大为标志的病因窗口;和4） 检查多个系统的心血管风险，以更好地了解健康的生物学机制。 通过完成这项奖学金，我将发展所需的分析和专业技能，开始我的 职业生涯作为一个授权的社会流行病学家研究种族差异的多层次决定因素， 心血管疾病贯穿整个生命周期。在加州大学伯克利分校获得流行病学博士学位后，我计划继续深造- 在一所学术研究型大学寻求早期研究者的职位之前，我先接受了博士培训。

PROJECT SUMMARY/ABSTRACT 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.