Understanding the contributions of the neighborhood social environment to cardiovascular disease risk factors in a multi-ethnic cohort of Chicago women

摘要 虽然在评估社区建设的影响方面取得了一些进展， 环境（即，物理属性，如杂货店或公园）对心脏代谢疾病风险的影响， 在理解邻里社会环境的贡献方面已经取得了进展（即， 社会经济构成、犯罪和集体效能）。大多数以前的社区研究的社会 环境利用美国人口普查指标来表征社区特征。 措施可以充分捕捉邻里一级的社会经济劣势和种族/民族的作用， 居住隔离，他们在评估其他方面的影响的能力更加有限， 与犯罪和集体效能有关的社会环境，如经济撤资和身体失调。 这是特别成问题的时候，公共卫生从业人员被要求确定突出的 目标是采取更具协作性的跨部门办法，处理健康的社会决定因素。在 认识到该领域的这一差距，本申请的目的是使用更新颖、更广泛的数据 来源，以调查研究不足的社会环境方面对心脏代谢的贡献。 在芝加哥的18-44岁的女性样本中的疾病风险。我们会在一九九九年达到这个目标。 部分原因是利用了谷歌的“街景”功能，这使得评估这些功能成为可能 虚拟地通过观看感兴趣的社区的高分辨率图像并虚拟地“行走”通过 17 -19我们还将使用商业位置数据库、住房存量和 负担能力数据和美国社区调查数据。这些目标的成功实现将 通过测试多目标、经济高效和可扩展的影响，有意义地改善该领域 社会环境对心脏代谢疾病风险的影响。

ABSTRACT While several advances have been made in the assessment of the impact of the neighborhood built environment (i.e., physical attributes like grocery stores or parks) on cardiometabolic disease risk, fewer gains have been made in understanding the contributions of the neighborhood social environment (i.e., socioeconomic composition, crime, and collective efficacy). Most prior neighborhood research on the social environment has utilized U.S. Census indicators, to characterize neighborhood features.14 Although these measures can sufficiently capture the role of neighborhood-level socioeconomic disadvantage and racial/ethnic residential segregation, they are more limited in their ability to assess the influence of other aspects of the social environment related to crime and collective efficacy like economic disinvestment and physical disorder. This is especially problematic at a time when public health practitioners are being called on to identify salient targets for more collaborative, cross-sector approaches to addressing social determinants of health. In recognition of this gap in the field, the objective of this application is to use more novel, widely available data sources to investigate the contributions of under-studied aspects of the social environment to cardiometabolic disease risk in a sample of women ages 18-44 living in the city of Chicago. We will achieve this objective in part by taking advantage of Google's “Street View” feature, which makes it possible to assess these features virtually by viewing high resolution images of neighborhoods of interest and taking a virtual “walk” through the neighborhood.17-19 We will also use a combination of business location databases, housing stock and affordability data, and American Community Survey data. The successful execution of these aims will meaningfully improve the field by testing the influence of multiple objective, cost-effective, and scalable measures of the social environment on cardiometabolic disease risk.