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

项目摘要/摘要 在美国，不同收入和教育水平的黑人面临着更高的发病率和更早的发病 心血管疾病(CVD)与白人相比。种族主义，表现在多个社会层面，是 作为健康不平等的推动者而获得认可。在人际关系层面上，许多研究都将 黑人中的种族歧视与心血管疾病后果。结果是细微差别的，风险最高的通常是 在报告的歧视程度最低的人中出现。非线性关联可能由以下原因引起 由于否认和自责而少报。在更具结构性的层面上，一个新兴的工作领域使用“Big” 数据，以捕捉集体种族偏见，或在一个定义的地理区域的平均反黑人偏见的数量。 早期证据已将集体种族偏见与心血管疾病相关死亡率的种族差异联系在一起，然而 通往健康的心理、社会和生物途径仍然难以捉摸。此外，集体种族偏见对 中青年时期的心血管风险是心血管疾病病因学中的一个显著窗口，目前尚不清楚。 拟议的研究旨在通过利用来自谷歌的大数据和隐含的项目来解决这些差距 捕捉全美地区层面的集体种族偏见。我们将采取集体种族偏见措施 数据来自全国青少年到成人健康纵向研究，这是一项由RICH进行的全国性队列研究 经过30多年的随访，收集了社会、行为和健康结果数据。我们将研究纵向， 集体种族偏见与系统特定的生物标志物和事件条件的多水平关联 表明心血管风险从青壮年(24-32岁)发展到中年(32-32岁) 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 联系是由个人自我报告的歧视经历来调节和/或调节的。 研究优势包括：1)将大数据与丰富的纵向队列研究相结合，以检查多水平 区域种族主义与疾病进展之间的联系；2)探索自我调节和节制 报告的歧视，以更好地了解将集体种族偏见与 健康，并有可能澄清自我报告的歧视和健康文献中的细微差别的结论；3) 明确关注从年轻到中年心血管风险的发展，一个重要的 以心血管风险的出现和日益扩大的种族差异为标志的病因窗口；以及4) 检查多个系统的心血管风险，以更好地了解健康的生物机制。 通过完成这项奖学金，我将发展分析和专业技能，以开始我的 作为一名被授权的社会流行病学家，研究#年种族差距的多层次决定因素 生命之路上的心血管疾病。在加州大学伯克利分校获得流行病学博士学位后，我计划攻读博士后 在一所学术研究型大学寻求早期研究人员的职位之前，先进行博士培训。

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.