Advancing intersectional discrimination measures for health disparities research

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PROJECT SUMMARY/ABSTRACT Guided by intersectionality frameworks, health disparities researchers have documented health disparities at the intersection of multiple axes of social status and position (SSP), particularly race/ethnicity, gender, and sexual orientation. To advance from identifying to intervening upon such intersectional health disparities, studies that examine underlying mechanisms are required. Much research demonstrates the negative health impacts of perceived discrimination within single health disparity populations. Quantitative approaches to assessing the role of discrimination in generating intersectional health disparities remain in their infancy, however. Members of our team recently introduced the Intersectional Discrimination Index (InDI) to address this gap. The InDI comprises three measures of enacted (day-to-day and major) and anticipated discrimination; these attribution-free measures ask about experiences of mistreatment “because of who you are.” These measures show promise for intersectional health disparities research but require further validation across intersectional groups and languages. Additionally, the proposal to remove attributions is controversial and no direct comparison has been conducted. Therefore, this study aims to (1) cognitively and (2) psychometrically evaluate the Intersectional Discrimination Index (InDI) in English and Spanish and (3) determine whether attributions should be included. Study aims will draw on three original sequentially collected sources of data: (a) Qualitative cognitive interviews in English and Spanish (n=50) with a sample purposively recruited across intersecting SSP (gender, sexual orientation, race/ethnicity, socio-economic status, age, nativity); (b) a Spanish quantitative survey (n=500; 50% SGM); and (c) an English quantitative survey (n=3000), with quota sampling by race/ethnicity (Black, Latinx, White), SGM status, and gender. The study's key deliverable will be bilingual measures of anticipated, day-to-day, and major discrimination validated for multiple health disparity populations using rigorous qualitative, quantitative, and mixed methods. This expected outcome will support NIMHD priorities for Health Disparities Science by strengthening measurement of discrimination in population health research, thereby improving understanding of how it contributes to health disparities.