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。这些出生结局在一些社区也比在其他社区发生得更频繁，但我们不知道社区特征可能会通过什么机制产生影响。这个项目将分析广泛的机制，这些机制因社区而异，可能会导致不利的生育结果。这些机制包括获得产前护理，如前往医院的交通时间或邻里社会联系，以及公共秩序混乱、公共冲突或私人冲突等压力来源。该项目将控制广泛的个人水平决定因素，包括先前的孕产妇健康。它还将分析同一家医院的大样本怀孕，从而控制医疗保健交付，这也可能与低出生体重和早产结局有关。该项目将提供社区因素如何影响生育结果的影响，从而建议如何构建社会政策来减少生育结果的差异。这项研究将首次确定出生结果不平等是否以及为什么持续存在，包括个人和社区层面的决定因素，使医院分类保持不变。该项目将使用一个新的数据集，由4324名怀孕早期招募的女性组成，她们在2006年至2015年期间在马萨诸塞州波士顿的布里格姆妇女医院分娩。该项目将使用多变量分析来确定，在扣除广泛的个人决定因素后，是否会出现生育结果的空间不平等。该项目将使用产妇健康状况的临床测量和准确的地址来捕捉社区特征。使用Google Maps API in R计算交通时间。背景压力源与来自两个来源的个人怀孕数据合并：(1)2006年、2008年和2010年BNS(波士顿社区)调查，非调查年份线性推算；(2)波士顿911数据库和311数据库的年度报告。将上下文变量与怀孕数据合并，以形成空间权重矩阵，并使用分层线性建模来分析数据。最后，该项目结合了血管生成的生物标记物，这些标记物在怀孕三个时间点可用于数据的子集，以执行因果调解分析。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。