Risk and strength: determining the impact of area-level racial bias and protective factors on birth outcomes.

风险和优势：确定地区种族偏见和保护因素对出生结果的影响。

• 批准号: 10544027
• 负责人: Thu Nguyen
• 金额: $ 68.91万
• 依托单位: UNIV OF MARYLAND, COLLEGE PARK
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-17 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10544027
• 关键词: Accounting; Address; Area; Biometry; Birth; Birth Rate; Birth Weight; Black Populations; Buffers; California; Characteristics; Childhood; Communities; County; Data; Emotional; Epidemiology; Ethnic Origin; Event; Feedback; Female; First Pregnancy Trimester; Focus Groups; Geographic Information Systems; Health; Health behavior; Individual; Infant Mortality; Intelligence; Intervention; Investigation; Label; Lead; Logistics; Low Birth Weight Infant; Low Prevalence; Machine Learning; Maternal Health; Measurement; Measures; Methods; Minority; Modeling; Mothers; Natural experiment; Not Hispanic or Latino; Outcome; Policies; Politics; Population; Pregnancy Trimesters; Premature Birth; Prenatal care; Race; Research; Research Design; Resources; Risk; Risk Factors; Role; Second Pregnancy Trimester; Site; Time; Time trend; Twitter; United States; Work; adverse birth outcomes; community engaged research; design; digital; disability; disparity reduction; ethnic minority; evidence base; health disparity; innovation; machine learning model; neural network; protective factors; racial and ethnic; racial and ethnic disparities; racial bias; racial discrimination; racial disparity; racial identity; racial prejudice; racism; resilience; social media; sociodemographics; statistics; stressor; trend

PROJECT SUMMARY There are large and persistent racial and ethnic disparities in preterm birth and low birth weight. Individual-level risk factors do not fully explain the observed disparities. There is increasing evidence for the role of area-level racial bias in explaining these disparities, but we currently lack both the measures, methods, and findings to empirically evaluate its influence. The proposed research will advance the research in all 3 areas. We will be using online and social media data and machine learning models to create two measures of area-level racial bias and implement a robust research design to determine whether area-level racial bias impacts birth outcomes. Our investigative team—comprised of experts in the field of epidemiology, health disparities, machine learning, social media data, biostatistics, and community engaged research—is uniquely suited to implement the study aims. Our Specific Aims are to 1) track and detect changes in area-level racial bias and identify local and national race-related events during these time points, 2) determine the impact of changes in area-level racial bias on changes in adverse birth outcomes, and 3) identify protective factors for adverse birth outcomes. Because our data is collected repeatedly and finely across the United States, we can explicitly account for temporal trends and place effects. The proposed study uses new data to capture trends in racial bias with sophisticated machine learning models, and represents a critical advancement in the investigation of racial disparities in birth outcomes.

项目总结 在早产和低出生体重方面存在巨大和持久的种族和族裔差异。个人层面 风险因素不能完全解释观察到的差异。有越来越多的证据表明区域层面的作用 在解释这些差异时存在种族偏见，但我们目前缺乏措施、方法和发现来 经验性地评估其影响力。这项拟议的研究将推动这三个领域的研究。我们会的 使用在线和社交媒体数据和机器学习模型创建地区一级种族的两个衡量标准 偏见和实施强有力的研究设计，以确定地区层面的种族偏见是否会影响生育 结果。我们的调查团队-由流行病学、健康差距、 机器学习、社交媒体数据、生物统计和社区参与研究--非常适合 落实研究目标。我们的具体目标是1)跟踪和发现地区一级种族偏见的变化和 确定这些时间点内与种族有关的地方和国家事件，2)确定以下变化的影响 地区一级对不良出生结局变化的种族偏见，以及3)确定不良出生的保护性因素 结果。因为我们的数据是在美国各地反复而精细地收集的，我们可以明确地 考虑到时间趋势和地点效应。这项拟议的研究使用新的数据来捕捉种族的趋势 与复杂的机器学习模型的偏差，并代表着在调查 出生结果中的种族差异。