# Assessing intersectional, multilevel and multidimensional structural racism for English- and Spanish-speaking populations in the US > **NIH NIH R01** · UNIVERSITY OF MARYLAND BALTIMORE · 2024 · $75,405 ## Abstract 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. ## Key facts - **NIH application ID:** 11099508 - **Project number:** 3R01MD019029-02S1 - **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE - **Principal Investigator:** Ester Villalonga Olives - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $75,405 - **Award type:** 3 - **Project period:** 2023-09-23 → 2026-03-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/11099508 ## Citation > US National Institutes of Health, RePORTER application 11099508, Assessing intersectional, multilevel and multidimensional structural racism for English- and Spanish-speaking populations in the US (3R01MD019029-02S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/11099508. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*