Assessing intersectional, multilevel and multidimensional structural racism for English- and Spanish-speaking populations in the US

评估美国英语和西班牙语人群的交叉、多层次和多维结构性种族主义

• 批准号: 10777486
• 负责人: Ester Villalonga Olives
• 金额: $ 58.42万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-23 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10777486
• 关键词: Area; Back; Behavioral; Belief; Black Populations; Black race; Businesses; COVID-19 mortality; Censuses; Collaborations; Collection; Communities; Community Participation; Complex; Data; Data Sources; Dimensions; Discrimination; Economics; Education; Ensure; Epidemiologist; Ethnic Population; Goals; Graduate Education; Graduation Rates; Health; Health Promotion; Healthcare; Hispanic; Hispanic Populations; Housing; Individual; Institution; Latino; Latino Population; Measurement; Measures; Methodology; Methods; Modernization; Nature; Olives - dietary; Outcome; Pattern; Persons; Policies; Population; Psychometrics; Public Health; Publishing; Qualifying; Quality of life; Race; Racial Equity; Racial injustice; Research; Research Personnel; Resources; Respondent; Sampling; Scientist; Societies; Stress; Structural Racism; Surveys; System; Techniques; Testing; Translations; United States National Institutes of Health; cognitive interview; design; economic indicator; effective intervention; experience; health determinants; health disparity; health inequalities; indexing; inequitable distribution; marginalized population; minority health; minority health disparity; mortality; multi-racial; novel; performance tests; racial minority population; racial population; residential segregation; response; social; social disparities; sound; theories; tool

SUMMARY Advancing racial equity in the U.S. starts by collecting valid and reliable data on structural racism. Structural racism represents the totality of ways in which multiple systems and institutions interact to assert racist policies, practices, and beliefs about people in a marginalized group. The patterns and practices of structural racism, such as inequitable distribution of health care treatment and health-promoting resources, create and perpetuate health disparities. To date, measures of structural racism have been limited in a number of ways: First, our preliminary data revealed that Blacks and Whites interpret social indicators collected at the individual level differently for reasons other than those intended by the measure. That difference indicates a bias operationalized by the lack of measurement invariance (or equivalence). Second, existing measures are overly focused on a single population—Blacks—and overlook the Hispanic/Latino population, which is expected to be 21% of the U.S. population by 2030. Third, current indices based on individual- and societal-level variables capture limited domains and lack indicators from the economic sector, which is indispensable to measuring structural racism. The wealth gap across race and ethnic groups is directly related to structural racism and its downstream causal effects on health. According to leading social epidemiologist Dr. Nancy Krieger, “you can’t understand the system of racial injustice without understanding how it ties to economic injustice” (2022). To identify effective interventions to reduce structural racism, there is an urgent public health need to capture the complex nature and impacts of structural racism in a multicultural and multiracial society. Our overall objective is to develop an intersectional, comprehensive, multilevel, and multidimensional Structural Racism Measure that has measurement invariance and is valid for both Blacks and Hispanics/Latinos. We use modern psychometric techniques and incorporate economic indicators that better capture social disadvantage. Our specific aims are to: 1) Incorporate novel data sources that comprise economic indicators to assess ecological-level determinants of health disparities; 2) Create an item bank from existing discrimination measures and items of economic discrimination practices with measurement invariance across Blacks and Hispanics/Latinos; and 3) Test the validity of the new Structural Racism Measure with a diverse sample of Blacks and Hispanics/Latinos. At the completion of the proposed research, the expected outcome will be a theory-driven, culturally- and racially-relevant, and psychometrically sound measure available to the public to assess structural racism. The overall positive impact of the tool will be its use in research settings to find effective interventions to reduce racial health inequities.

总结 在美国推进种族平等，首先要收集关于结构性种族主义的有效和可靠的数据。结构 种族主义代表了多种制度和机构相互作用， 政策，实践和对边缘化群体的人的信念。结构化的模式和实践 种族主义，例如保健治疗和促进健康资源的不公平分配， 使健康差距永久化。迄今为止，结构性种族主义的措施在若干方面受到限制： 首先，我们的初步数据显示，黑人和白人解释在个人收集的社会指标 由于措施意图以外的原因，水平不同。这种差异表明 由于缺乏测量不变性（或等效性）而可操作化。第二，现有措施过于 集中在一个单一的人口-黑人-和忽视西班牙裔/拉丁裔人口，这是预计将 到2030年将占美国人口的21%。第三，基于个人和社会变量的现行指数 涵盖有限的领域，缺乏经济部门的指标，这是衡量 结构性种族主义种族和族裔群体之间的贫富差距与结构性种族主义及其 对健康的下游因果影响。根据著名社会流行病学家南希·克里格博士的说法，“你不能 理解种族不公正的制度，而不理解它与经济不公正的关系。 （2022年）。为了确定有效的干预措施，减少结构性种族主义，公共卫生迫切需要 在一个多文化和多种族的社会中，抓住结构性种族主义的复杂性质和影响。我们 总体目标是发展一个交叉的，综合的，多层次的，多维度的结构 种族主义测量具有测量不变性，对黑人和西班牙裔/拉丁裔都有效。我们使用 现代心理测量技术，并纳入更好地反映社会不利因素的经济指标。 我们的具体目标是：1）纳入新的数据来源，包括经济指标，以评估 健康差异的生态水平决定因素; 2）从现有的歧视中创建一个题库 经济歧视做法的措施和项目，在黑人之间具有测量不变性， 西班牙裔/拉丁裔;和3）测试新的结构性种族主义措施的有效性与不同的样本， 黑人和西班牙裔/拉丁裔。在拟议的研究完成后，预期成果将是 理论驱动的，文化和种族相关的，和心理测量的声音措施提供给公众， 评估结构性种族主义。该工具的总体积极影响将是其在研究环境中的使用， 采取有效干预措施，减少种族卫生不平等。