Measuring multidimensional structural racism as a driver of racial inequities in birth outcomes

衡量多维结构性种族主义作为出生结果种族不平等的驱动因素

• 批准号: 10809951
• 负责人: Tongtan Chantarat
• 金额: $ 15.5万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-21 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10809951
• 关键词: Address; Age; American; Award; Birth; Birth Records; Black Populations; Black race; California; Caucasians; Cities; Community Surveys; Complex; Confidence Intervals; Criminal Justice; Data; Data Set; Databases; Dimensions; Education; Employment; Ensure; Future; Geography; Health; Health Policy; Health Sciences; Heterogeneity; Home; Income; Inequity; Infant; Interdisciplinary Study; Justice; Knowledge; Life Cycle Stages; Link; Louisiana; Low Birth Weight Infant; Measurement; Measures; Methodology; Methods; Michigan; Minnesota; New York City; Outcome; Outcomes Research; Ownership; Pennsylvania; Personality; Persons; Pilot Projects; Premature Birth; Proxy; Publishing; Race; Research; Research Personnel; Resources; Risk; Small for Gestational Age Infant; Structural Racism; System; Testing; Time; Typology; US State; Washington; anti-racism; data portal; design; frontier; health determinants; health equity; health inequalities; improved; indexing; innovation; insight; multidisciplinary; population health; pregnant; racial disparity; residential segregation; risk prediction; theories

PROJECT SUMMARY The objective of the proposed study is to determine the linkages between multidimensional structural racism and preterm birth, low birthweight, and small-for-gestational-age birth among infants of US-born Black, foreign- born Black, and white pregnant people. The study team published an innovative, theory-driven approach to measuring structural racism as a multidimensional determinant of health called the Multidimensional Measure of Structural Racism (MMSR) in 2022. Unlike previous measures of structural racism, which focus on separate domains of structural racism (e.g., Black-white racial residential segregation, Black-white inequities in education, employment, income, homeownership, criminal justice), the MMSR uses Latent Class Analysis—a method commonly used by psychometricians to identify complex constructs (e.g., personality types)—to identify multidimensional structural racism (referred to as “structural racism typologies”). The MMSR captures the interconnection between structural racism domains and their combined health effects. The proposed study will calculate the MMSR using the data from the American Community Survey and the Vera Institute of Justice databases. First, we will create measures of (1) racial residential segregation, (2) education inequity, (3) employment inequity, (4) income inequity, (5) homeownership inequity, and (6) criminal justice inequity. Next, we will identify the structural racism typologies with Latent Class Analysis and link the typologies to birth records using the residential address of the pregnant people at the time of delivery. For Aim 1, we will use Vermunt's “3-step approach” to relate the structural racism typologies to the risks of preterm birth, low birthweight, and small-for-gestational-age birth for infants born to US-born Black, foreign-born Black, and white pregnant people in Minnesota during 2014-2021. This aim is an expansion of our pilot project, in which we focused only on births that occurred in 2018 in Minnesota. We will calculate period-specific, age-adjusted predicted risks, and 95% confidence interval for each group and compare them. For Aim 2, we will use the same analytical approach as in Aim 1; however, we will use birth records from Louisiana, Michigan, Washington, California, Pennsylvania, and New York City (i.e., one state/city from all US regions). We will calculate state-specific estimates to examine regional heterogeneity. Additionally, we will calculate multi-state estimates and compare them to the state-specific estimates to assess their consistency. Results of the proposed project will (1) elucidate the linkages between structural racism and birth outcomes and answer whether effect heterogeneity exists across time and place, (2) inform whether the MMSR needs further refinement before expanding its use to study other health outcomes in which racial inequities exist, and (3) make the MMSR methodology and the measure calculated for the proposed project available for public use.

项目总结 拟议研究的目的是确定多层面结构性种族主义之间的联系 在美国出生的黑人婴儿中，早产、低出生体重和小于胎龄儿-- 出生时为黑人，怀孕时为白人。研究小组发布了一种创新的、理论驱动的方法来 将结构性种族主义作为健康的多维决定因素进行测量，称为多维测量 结构性种族主义(MMSR)在2022年。与以前的结构性种族主义措施不同，结构性种族主义的重点是 结构性种族主义的领域(例如，黑人-白人种族居住隔离，黑人-白人不平等 教育、就业、收入、住房所有权、刑事司法)，MMSR使用潜在类别分析-a 心理测量学家通常用来识别复杂结构(例如，人格类型)的方法 确定多层面结构性种族主义(称为“结构性种族主义类型学”)。MMSR捕获 结构性种族主义领域之间的相互联系及其对健康的综合影响。建议进行的研究 将使用美国社区调查和维拉司法研究所的数据来计算MMSR 数据库。首先，我们将制定(1)种族居住隔离、(2)教育不平等、(3) 就业不平等，(4)收入不平等，(5)住房拥有不平等，和(6)刑事司法不平等。下一首, 我们将用潜在阶级分析来识别结构性种族主义类型，并将这些类型与出生联系起来 使用分娩时孕妇住址的记录。对于目标1，我们将使用 Vermunt提出的将结构性种族主义类型与早产风险联系起来的“三步法”， 出生在美国的黑人、出生在外国的黑人和白人所生婴儿的出生体重和小于胎龄儿 明尼苏达州2014-2021年的孕妇人数。这个目标是我们试点项目的扩展，在这个项目中，我们 只关注2018年明尼苏达州的出生情况。我们将计算特定时期、年龄调整后的 预测的风险，以及每组的95%可信区间并进行比较。对于目标2，我们将使用 分析方法与目标1相同；不过，我们将使用密歇根州路易斯安那州的出生记录， 华盛顿州、加利福尼亚州、宾夕法尼亚州和纽约市(即美国所有地区的一个州/市)。我们会 计算特定州的估计值，以检查地区的异质性。此外，我们还将计算多态 估计，并将它们与州特定的估计进行比较，以评估它们的一致性。评选结果 拟议项目将(1)阐明结构性种族主义和生育结果之间的联系，并回答 是否存在跨时间和跨地点的效果异质性，(2)通知MMSR是否需要进一步 在扩大使用它来研究存在种族不平等的其他健康后果之前进行改进，以及(3) 使MMSR方法和为拟议项目计算的措施可供公众使用。