Causal Models of Segregation and Health

隔离与健康的因果模型

• 批准号: 7242772
• 负责人: Brian K. Finch
• 金额: $ 14.95万
• 依托单位: SAN DIEGO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-04-05 至 2009-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7242772
• 关键词: Address; Adult; Affect; Attenuated; Censuses; Characteristics; Condition; County; Data; Data Aggregation; Data Set; Dimensions; Ecological Bias; Economics; Health; Heterogeneity; Individual; Life Cycle Stages; Literature; Measures; Methods; Minority Groups; Modeling; Neighborhoods; Outcome; Pathway interactions; Physical environment; Poverty; Rate; Research; Research Project Grants; Respondent; Time; health disparity; improved; indexing; mortality; segregation; social; social space

DESCRIPTION (provided by applicant): Residential segregation has been argued by many as a fundamental cause of racial disparities in health (Acevedo-Garcia 2003; Schulz et al. 2002; Williams & Collins 2001). Segregation disproportionately affects minority groups by concentrating poverty, diminishing the quality of neighborhood social and physical environments, and attenuating individual socio-economic attainment and upward mobility (Collins and Williams 1999). However, we still do not fully understand the extent to which neighborhood segregation contributes to health disparities-not simply because relatively few studies have explicitly addressed this question, but because many of the past research attempts to measure the effects of racial segregation on health have used inappropriate methods and untenable assumptions (Duncan et al. 1997) and have relied largely on aggregate level data (Williams & Collins 2001). We are able to remedy many of the shortcomings in conventional analysis in the following ways. One, while prior studies have relied almost exclusively on aggregate data that is subject to ecological fallacy, we are able to utilize individual-level data to examine relationships between segregation and health (self-rated health and mortality) as well as neighborhood-level (census-tract) data to examine the possible pathways through which segregation affects health. Second, utilizing panel data, we can estimate the effects of segregation at various points in time during the life course; prior studies have almost all relied on a single (contemporary) point-in-time estimate of neighborhood effects on health. Third, we are able to calculate a broad set of segregation measures in addition to the traditionally used dissimilarity index-measures that have a much stronger theoretical and analytical correlation with concentrated poverty and other aspects of social space that may have a much stronger connection to health. Finally, and most importantly, by using panel data we are able to at least partially control for unobserved heterogeneity and recover causal estimates of the effects of segregation on health outcomes by controlling for unobserved individual-level characteristics through the application of various fixed-effect analyses. This project investigates the effect of residential segregation over the life-course on the health and mortality of Black and White adults using a sophisticated panel data set, merged with census data at the census-tract and county levels. This project will estimate unbiased effects of segregation and neighborhood poverty to determine how much any potential observed effects contribute to known racial disparities in health and mortality.

描述（由申请人提供）：居住隔离被许多人认为是健康方面种族差异的根本原因（Acevedo-Garcia 2003; Schulz et al. 2002;威廉姆斯&柯林斯2001）。隔离对少数群体的影响不成比例，因为它集中了贫困，降低了邻里社会和物质环境的质量，削弱了个人的社会经济成就和向上流动性（柯林斯和威廉姆斯，1999年）。然而，我们仍然没有完全理解邻里隔离在多大程度上有助于健康保护-不仅仅是因为相对较少的研究明确解决了这个问题，但由于过去许多试图衡量种族隔离对健康影响的研究都使用了不恰当的方法和站不住脚的假设，（邓肯等人，1997），并在很大程度上依赖于总体水平的数据（威廉姆斯和柯林斯，2001）。我们可以用以下方法来弥补传统分析中的许多缺点。第一，虽然以前的研究几乎完全依赖于受生态学谬误影响的汇总数据，但我们能够利用个人层面的数据来研究隔离与健康之间的关系（自测健康和死亡率）以及社区层面的（人口普查）数据来研究隔离影响健康的可能途径。其次，利用面板数据，我们可以估计在不同的时间点在生命过程中的隔离的影响;以前的研究几乎都依赖于一个单一的（当代）时间点的邻居对健康的影响估计。第三，除了传统上使用的相异指数措施外，我们还能够计算出一套广泛的隔离措施，这些措施与集中贫困和社会空间的其他方面有更强的理论和分析相关性，这些方面可能与健康有更强的联系。最后，也是最重要的是，通过使用面板数据，我们能够至少部分地控制未观察到的异质性，并通过应用各种固定效应分析控制未观察到的个人水平特征，恢复隔离对健康结果影响的因果估计。该项目调查居住隔离对黑人和白色成年人的健康和死亡率的影响，使用一个复杂的面板数据集，在人口普查区和县一级的人口普查数据合并。该项目将估计种族隔离和社区贫困的无偏影响，以确定任何潜在的观察到的影响在健康和死亡率方面对已知种族差异的贡献。