Assessing intersectional, multilevel and multidimensional structural racism for English- and Spanish-speaking populations in the US
Assessing intersectional, multilevel and multidimensional structural racism for English- and Spanish-speaking populations in the US
批准号:
10777486
负责人:
Ester Villalonga Olives
金额:
$58.42万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-09-23 至 2027-03-31
关键词:
AreaBackBehavioralBeliefBlack PopulationsBlack raceBusinessesCOVID-19 mortalityCensusesCollaborationsCollectionCommunitiesCommunity ParticipationComplexDataData SourcesDimensionsDiscriminationEconomicsEducationEnsureEpidemiologistEthnic PopulationGoalsGraduate EducationGraduation RatesHealthHealth PromotionHealthcareHispanicHispanic PopulationsHousingIndividualInstitutionLatinoLatino PopulationMeasurementMeasuresMethodologyMethodsModernizationNatureOlives - dietaryOutcomePatternPersonsPoliciesPopulationPsychometricsPublic HealthPublishingQualifyingQuality of lifeRaceRacial EquityRacial injusticeResearchResearch PersonnelResourcesRespondentSamplingScientistSocietiesStressStructural RacismSurveysSystemTechniquesTestingTranslationsUnited States National Institutes of Healthcognitive interviewdesigneconomic indicatoreffective interventionexperiencehealth determinantshealth disparityhealth inequalitiesindexinginequitable distributionmarginalized populationminority healthminority health disparitymortalitymulti-racialnovelperformance testsracial minority populationracial populationresidential segregationresponsesocialsocial disparitiessoundtheoriestool
中文摘要
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英文摘要
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.
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