Doctoral Dissertation Research: Spatial Inequality in Birth Outcomes - Testing Classes of Proximate Mechanisms

博士论文研究：出生结果的空间不平等——近程机制的测试类别

• 批准号: 1802538
• 负责人: Jason Beckfield
• 金额: $ 1.17万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-15 至 2020-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1802538&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Spatial; Inequality

Low birth weight and preterm birth have lasting and negative implications for healthy child development, which influences their later educational and socioeconomic attainment. These birth outcomes also occur more frequently in some neighborhoods than in others, but we do not understand the mechanisms through which neighborhood characteristics may be influential. This project will analyze a wide range of mechanisms that vary by neighborhood and may contribute to adverse birth outcomes. These mechanisms include access to prenatal care, such as transportation time to hospitals or neighborhood social connectedness, as well as sources of stress such as public disorder, public conflicts or private conflicts. The project will control for a wide range of individual level determinants, including prior maternal health. It will also analyze a large sample of pregnancies from the same hospital, thus controlling for health care delivery, which can also implicate low birthweight and preterm birth outcomes. The project will provide implications for how neighborhood factors can influence birth outcomes, thus suggesting how social policies may be constructed to reduce birth outcome variation. This study will be the first to determine whether and why birth outcome inequality persists including both individual and neighborhood level determinants, holding sorting into hospitals constant. The project will use a novel dataset comprised of a cohort of 4,324 women who were recruited by their first trimester and who delivered live singleton births at the Brigham and Women's Hospital in Boston, MA., between the years 2006 and 2015. The project will use multivariate analysis to determine whether spatial inequality in birth outcomes occurs net of a wide range of individual determinants. The project will use clinical measures of maternal health status and exact addresses to capture neighborhood characteristics. Transportation times are computed using Google Maps API in R. Contextual sources of stress are merged with individual-level pregnancy data from two sources: (1) the 2006, 2008 and 2010 BNS (Boston neighborhood) Surveys, with linear imputations on non-survey years; and (2) annual reports from the City of Boston's 911 database and 311 databases. Contextual variables are merged with the pregnancy data in order to form a spatial weights matrix and data are analyzed using hierarchical linear modeling. Lastly, the project incorporates bio-markers of angiogenesis, which are available at three points in pregnancy for a subset of the data, to perform causal mediation analyses.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

低出生体重和早产对儿童的健康发展具有持久的负面影响，从而影响他们以后的教育和社会经济成就。这些出生结果在某些社区也比其他社区更频繁地发生，但我们不了解社区特征可能产生影响的机制。 该项目将分析因社区而异并可能导致不良出生结果的各种机制。 这些机制包括获得产前护理的机会，例如前往医院的交通时间或邻里社会联系，以及压力来源，例如公共骚乱、公共冲突或私人冲突。该项目将控制广泛的个人层面的决定因素，包括先前的孕产妇健康状况。它还将分析同一家医院的大量妊娠样本，从而控制医疗保健提供，这也可能涉及低出生体重和早产结果。 该项目将揭示邻里因素如何影响出生结果，从而提出如何制定社会政策来减少出生结果的差异。这项研究将首次确定出生结果不平等是否以及为何持续存在，包括个人和社区层面的决定因素，并保持医院的分类不变。该项目将使用一个新颖的数据集，该数据集由 4,324 名女性组成，她们是在 2006 年至 2015 年期间在马萨诸塞州波士顿的布莱根妇女医院招募的妊娠早期的活产单胎婴儿。该项目将使用多变量分析以确定出生结果的空间不平等是否在排除各种个人决定因素后发生。 该项目将使用孕产妇健康状况的临床测量和准确的地址来捕捉社区特征。运输时间是使用 R 中的 Google 地图 API 计算的。压力的背景来源与来自两个来源的个人层面怀孕数据合并：(1) 2006 年、2008 年和 2010 年 BNS（波士顿社区）调查，对非调查进行线性插补年; (2) 波士顿市 911 数据库和 311 数据库的年度报告。将上下文变量与妊娠数据合并，以形成空间权重矩阵，并使用分层线性模型对数据进行分析。最后，该项目结合了血管生成的生物标志物，这些标志物可在怀孕期间的三个时间点获得一部分数据，以进行因果中介分析。该奖项反映了 NSF 的法定使命，并通过使用基金会的评估进行评估，被认为值得支持。智力价值和更广泛的影响审查标准。