Objective and Perceived Neighborhood Characteristics and Cognitive Decline

描述（由研究者提供）：社区社会、经济、人口统计和建筑特征已被证明影响健康结果，如死亡率、肥胖、心血管疾病和抑郁，独立于个人因素。最近的文献表明，个人居住地区的社区社会经济地位（NSES）与认知功能相关，独立于个人因素。然而，迄今为止还没有研究在全国具有代表性的样本中检验纵向关系。此外，研究的重点是NSES，而没有考察其他与健康相关的客观邻里特征和感知邻里特征，没有考虑其他敏感的晚年认知结果，如认知障碍，也没有考虑基于健康状况的邻里选择是否会影响结果的客观性。该项目将通过开发一个国家客观社区特征数据库来填补现有研究的空白，将这些数据与在健康与退休研究（HRS）中评估的22年（1992-2014）的全国代表性老年人样本中评估的认知功能纵向数据联系起来，并研究客观和感知社区特征与认知功能之间的关系。本研究还将研究一系列潜在的介质，包括压力的生物标志物、血管、行为和社会心理因素，这些因素可能为社区影响认知结果的机制途径提供信息。它还将根据社会人口统计学和遗传特征确定最容易受到邻居影响的亚群体，并测试我们的发现对选择偏差方法的敏感性。我们将建立一个客观的社区测量数据库，包括NSES、种族/民族隔离和构成、城市扩张和食物环境，并将其与HRS中关于社区认知功能和主观评分的个人数据以及关于血管事件临床诊断的医疗保险数据相结合。结构方程模型将检验邻里效应的独立关联、中介和效应修正。一种被称为省略变量分析的创新统计技术将解决个体可能根据健康状况有选择地进入不同社区的可能性，倾向评分匹配将用于减少识别社区特征对认知结果影响的潜在偏差。这项研究可以揭示背景特征，这些特征可以作为干预的可修改目标，并且在预防认知衰退和损害方面产生比关注个人因素的干预更大的变化。调解分析将确定潜在的生物、血管、行为和社会心理机制，以确定更接近的干预目标，以缓冲生活在低质量社区的影响。确定易受伤害的亚群体将使公共卫生干预措施更有效地侧重于高危群体。

DESCRIPTION (provided by investigator): Neighborhood social, economic, demographic, and built characteristics have been shown to influence health outcomes such as mortality, obesity, cardiovascular disease, and depression, independent of individual factors. Recent literature suggests that the neighborhood socioeconomic status (NSES) of the area in which individuals live is correlated with cognitive functioning, independent of individual factors. However, no studies to date have examined longitudinal relationships in a nationally representative sample. In addition, studies have focused on NSES without examination of other objective health-relevant neighborhood characteristics and perceived neighborhood characteristics, have not considered other sensitive late-life cognitive outcomes such as cognitive impairment, and have not addressed whether selection into neighborhoods, based on health status for example, may bias results Objective. This project will fill gaps in existing research by developing a national database of objective neighborhood characteristics, linking these data with longitudinal data on cognitive function assessed in a nationally representative sample of older adults in the Health and Retirement Study (HRS) assessed over 22 years (1992-2014), and examining the relationship between both objective and perceived neighborhood characteristics and cognitive function over time. This study will also examine a host of potential mediators including biomarkers of stress, and vascular, behavioral, and psychosocial factors that may inform mechanistic pathways by which neighborhoods influence cognitive outcomes. It will also identify subgroups that are most vulnerable to neighborhood effects based on sociodemographic and genetic characteristics, and test the sensitivity of our findings to selection bias Methods. We will develop a database of objective neighborhood measures including NSES, racial/ethnic segregation and composition, urban sprawl, and food environment, and integrate it with individual data on cognitive function and subjective ratings of neighborhoods from the HRS and with Medicare data on clinical diagnoses of vascular events. Structural equation models will examine independent associations, mediation, and effect modification of neighborhood effects. An innovative statistical technique called omitted variable analysis will address the potential that individuals may selectively move into differing neighborhoods by health status and propensity score matching will be used to reduce potential biases in identifying the effects of neighborhood characteristics on cognitive outcomes Implications. This research can shed light on contextual characteristics that may serve as modifiable targets for interventions, and yield larger-scale changes in the effort to prevent cognitive decline and impairment than interventions that focus on individual factors. Mediation analyses will identify underlying biological, vascular, behavioral, and psychosocial mechanisms to pinpoint more proximal intervention targets to buffer against the effects of living in a lower quality neighborhood. Identification of vulnerable subgroups will focus public health interventions more efficiently on at-risk groups.