Allostatic load, Response to discrimination stress, Discrimination Exposure frequency, and social Network structure and function (ARDEN)

摘要 目前的提案寻求严格测试人际歧视如何导致急性和慢性 拉美裔美国人的压力、稳态负荷和相关的健康风险以及社会支持网络的各个方面 可以通过减少压力传染来缓解这些影响。我们的项目关注歧视，作为移民 拉美裔美国人--尤其是新移民--报告的歧视频率通常比 在美国出生的西班牙裔。这反过来可能有助于拉美裔移民的健康优势，例如3 相对于在美国出生的拉美裔，预期寿命增加了几岁。风化假说与风化理论 同素作用表明，歧视暴露和相关压力的累积效应可以解释这些 健康差距。然而，这项提案将是第一批测试歧视机制的提案之一 压力有助于平衡负荷，重点是社会和文化机制；此外，我们将扩大 这项工作超越了个人，无法理解歧视压力如何作用于社交网络。研究领域 白人人口记录了社会支持如何保护人们免受压力对健康的负面影响， 但很少有研究在西班牙裔样本中检验这些影响。此外，先前的研究没有考虑到 社会支持如何在整个网络中保持压力：也就是说，当一个人经历压力时，他们的 支持他人的人也会感受到压力。这与歧视等压力源特别相关， 不仅影响目标，而且影响他们的社会关系。因此，为了减轻歧视的影响， 支持网络可能需要以特定的方式使用，并具有特定的品质。目前的研究测试 这些品质。首先，我们将确定歧视压力反应和歧视的程度 暴露频率结合起来可以预测不平衡负荷的变化，进而预测多发性慢性疾病的风险 健康后果是导致死亡的主要原因(例如，心血管疾病)。第二，我们将确定 缓解歧视的社交网络特质会让压力从一个人“溢出”到另一个人。最后， 我们确定辨别压力反应是如何通过网络传递的，以预测 网络的均衡负载。为了实现这一点，我们将使用抽样设计(受访者驱动抽样；RDS) 以及社交网络分析，让我们了解人与人之间的联系如何影响健康。 我们将检查社会支持；生物标志物的平衡负荷，急性和慢性压力，以及炎症；以及 每天对歧视频率进行日记评估。使用我们团队开发的新型蓝牙技术 我们还将评估参与者彼此交互的频率，并将这些数据链接到每个参与者 关于歧视的报告，以及他们是否与支持歧视的人讨论了歧视问题。这两种技术的结合 这些措施和基于网络的方法使我们能够对社会环境进行新的测试 影响健康，并可能在面对歧视时传递健康复原力。此外，它还将通知 如何最好地安排社会支持干预，以限制人际歧视的负面影响。

ABSTRACT The current proposal seeks to rigorously test how interpersonal discrimination contributes to acute and chronic stress, allostatic load and related health risk among Hispanics and how aspects of social support networks may mitigate these effects by reducing stress contagion. Our project focuses on discrimination, as immigrant Hispanics – particularly recent immigrants – often report discrimination at substantially lower frequencies than US-born Hispanics. This in turn may contribute to immigrant Hispanic’s health advantages, such as 3 additional years life expectancy, relative to US-born Hispanics. The Weathering Hypothesis and theories of allostasis suggest that the cumulative effects of discrimination exposure and related stress would explain these health disparities. However, this proposal will be among the first to test mechanisms of how discrimination stress contributes to allostatic load, with a focus on social and cultural mechanisms; moreover, we will extend this work beyond the individual to understand how discrimination stress acts on a social network. Research in White populations has documented how social support protects against the negative health effects of stress, but little work has examined these effects in Hispanic samples. Moreover, prior research does not account for how social support may perpetuate stress across a network: that is, when one person experiences stress, their supportive others will also experience stress. This is particularly relevant for stressors like discrimination, which impact not only the target but also their social ties. Thus, in order to mitigate the effects of discrimination, support networks may need to be used in specific ways and have specific qualities. The current study tests these qualities. First, we will determine the extent to which discrimination stress response and discrimination exposure frequency combine to predict changes in allostatic load, and in turn predicts risk for multiple chronic health outcomes that are leading causes of death (e.g., cardiovascular disease). Second, we will determine the social network qualities that mitigate discrimination stress “spilling over” from one person to another. Finally, we determine how discrimination stress response transmits across a network to predict changes in the networks’ allostatic load. To achieve this, we will use sampling designs (respondent-driven sampling; RDS) and social network analyses that allow us to understand how the connections across people influence health. We will examine social support; biomarkers of allostatic load, acute and chronic stress, and inflammation; and daily diary assessments of discrimination frequency. Using novel Bluetooth technology developed by our team we will also assess how often participants interact with one another and link that data to each participants reports of discrimination and whether they discussed discrimination with supportive others. The combination of these measures and network-based approach allows us to conduct novel tests of how social environments influence health and potentially convey health resilience in the face of discrimination. Additionally, it will inform how to best arrange social support interventions to limit the negative effects of interpersonal discrimination.