Modeling Health Histories and the Dynamics of Social Inequality in a Nonhuman Primate Population

项目摘要 拟议项目的广泛目标是增加对社会驱动的健康动态的理解。 通过开发改进的方法来使用新的灵长类动物模型预测个人健康史来实现差异 暴露于不同程度的社会劣势。不同亚群之间的健康差距是至关重要的 社会问题1-3因此，描述和预测个人健康史的准确模型是一种 确定干预战略的基本第一步。大多数当前的模型4-5假设观察到的 人类人口健康下降的逐渐积累反映了个人健康史的变化 在他们的一生中。然而，最近关于功能限制的研究表明，个人健康史是 更好的描述是一种间断的平衡模式，在这种模式下，个体可能经历一段时间的长期 稳定被突然的变化所打断。这指向了用于测试的模型之间的差异 关于累积劣势对寿命和实际健康史影响的假设 由个人经历的。为了缩小健康差距，从而促进所有群体的健康老龄化， 必须克服现有健康史预测方法在理论和实践上的局限性。 该项目旨在通过采用比较的方法提高对健康差距动态的分析理解。 方法使用来自卡约圣地亚哥恒河猴种群的数据。这一群体独特地允许 对自然主义的、社会复杂的人群的健康进行综合的纵向研究，因此是理想的灵长类 提供有关不同个体如何在多个健康状态之间转换的信息的模型系统 在社会分层的成年人寿命中。第一个目标是刻画多个状态之间的转换规则 使用健康指数对社会分层的成年人寿命进行健康评估，健康指数包括心理和 生理健康。第二个目标是使用这个新的经验数据集来制定和开发一个多状态 预测模型，用于分析健康变化模式及其与个人社会环境的关系。 第三个目标是预测累积的健康惩罚和不同的健康结果，以便确定 处于不同程度社会劣势的亚群之间稳定或不断变化的健康差距，例如 社会地位和社会融合。这些目标将允许制定准确的动态健康历史 预测模型，检查个人的社会性是否以及如何影响健康状态的转变 性别和年龄，并最终完善、修改和适应数据收集和当前模式 准确评估社会驱动的健康差距的假设。具体来说，这项研究将有助于 到NIH通过基础科学进行干预开发的0阶段，打开识别临界年龄的空间 作为后续纵向研究的目标，这将为未来的翻译研究提供重要的桥梁 人类。

Project Summary The broad goal of the proposed project is to increase understanding of the dynamics of socially driven health disparities by developing improved methods to forecast individual health histories using a new primate model exposed to different levels of social disadvantage. Health disparities across different subgroups are a crucial societal problem1-3 and thus, accurate models describing and forecasting individual health histories are a fundamental first step to identify strategies for intervention. Most current models4-5 assume that the observed gradual accumulation of health decline in human populations reflect the change in the health history of individuals across their lifespan. However, recent work on functional limitations6 suggests that individual health histories are better described as a punctuated equilibrium pattern where the individual may experience periods of long-term stability interrupted by sudden changes. This points towards discrepancies between models used to test hypotheses about the impact of cumulative disadvantage over the lifespan, and the actual health histories experienced by individuals. To narrow the health gap and thus foster healthy aging across all groups, the theoretical and practical limitations imposed by current health history forecast methods must be overcome. This project aims to improve analytical understanding of health disparity dynamics by employing a comparative approach using data from the Cayo Santiago rhesus macaque population. This population uniquely allows for integrative longitudinal studies of health in a naturalistic, socially complex population, and thus is an ideal primate model system to yield information about how different individuals transition between multiple states of health across the socially stratified adult lifespan. The first aim is to characterize transition rules between multiple states of health across the socially stratified adult lifespan using indexes of health comprising both psychological and physiological health. The second aim is to use this new empirical dataset to formulate and develop a multistate forecasting model to analyze changing patterns of health and their relation to an individual’s social environment. The third aim is to then forecast cumulative health penalties and divergent health outcomes in order to identify stable or changing gaps in health across subgroups exposed to different levels of social disadvantage, such as social status and social integration. These aims will allow the formulation of an accurate dynamic health history forecast model, the examination of whether and how individual sociality affects health state transitions across sex and age, and ultimately the refinement, modification, and adaptation of data collection and current model assumptions for accurate assessment of socially driven health disparities. Specifically, this study will contribute to the NIH Stage 0 of Intervention Development through basic science, opening space to identify critical ages to target in a follow-up longitudinal study that will provide a crucial bridge to future translational research with humans.