Impact of Racial Categorization on Effect Estimates: An HIV Stigma Analysis. Issue 4 (5th January 2022)

• Record Type: Journal Article
• Authors: Facente, Shelley N; Lam-Hine, Tracy; Bhatta, Dharma N; Hecht, Jennifer
• Is Part Of: American journal of epidemiology. Volume 191:Issue 4(2022)
• Page Start: 689
• Page End: 695
• Publication Date: 2022-01-05
• Subjects: HIV -- multiracial categorization -- race factors -- racism -- regression analysis -- stigma; Epidemiology -- Periodicals; Public health -- Periodicals; 614.4
• DOI: 10.1093/aje/kwab289
• Languages: English
• ISSNs: 0002-9262

Abstract

Abstract: Suboptimal racial categorization potentially introduces bias in epidemiologic analysis and interpretation, making it difficult to appropriately measure factors leading to racial health disparities. As part of an analysis focused on predictors of experiencing human immunodeficiency status (HIV)-related stigma among men who have sex with men living with HIV in San Francisco, we struggled with the most appropriate ways to categorize people who reported more than 1 racial identity, and we aimed to explore the implications of different methodological choices in this analysis. We fitted 3 different multivariable linear regression models, each utilizing a different approach to racial categorization: the "multiracial, " "othering, " and "hypodescent" models. We estimated an adjusted risk difference in mean score for reported frequency of experiencing HIV-related stigma on a 4-point scale, adjusting for age, race, gender identity, injection history, housing, mental health concerns, and viral load. Use of a hypodescent model for racial categorization led to a shift in the point estimate through the null for Blacks/African Americans, and it improved precision for that group. However, it obscured the association of increased stigma and race for multiracial people, compared with monoracial counterparts. We conclude that methodological decisions related to racial categorization of participants can dramatically affect race-related study findings in predictor regression models.