Attributions for Everyday Discrimination and All-Cause Mortality Risk Among Older Black Women: A Latent Class Analysis Approach.

Abstract

This study examined the relationship between number of attributed reasons for everyday discrimination and all-cause mortality risk, developed latent classes of discrimination attribution, and assessed whether these latent classes were related to all-cause mortality risk among U.S. older Black women. Participants were from the 2006 and 2008 waves of the Health and Retirement Study (N = 1,133; 335 deaths). Vital status was collected through the National Death Index through 2013 and key informant reports through 2019. Latent class analyses were conducted on discrimination attributions. Weighted Cox proportional hazards model was used to predict all-cause mortality. Analyses controlled for demographic characteristics, socioeconomic status, and health. Reporting greater attributions for everyday discrimination was associated with higher mortality risk (hazard ratio [HR] = 1.117; 95% confidence interval [CI]: 1.038-1.202; p < .01), controlling for demographic characteristics, socioeconomic status, and health as well as health behaviors. A 4-class solution of the latent class analysis specified the following attribution classes: No/Low Attribution; Ancestry/Gender/Race/Age; Age/Physical Disability; High on All Attributions. When compared to the No/Low Attribution class, membership in the High on All Attributions class was associated with greater mortality risk (HR = 2.809; CI: 1.458-5.412; p < .01). Findings underscore the importance of everyday discrimination experiences from multiple sources in shaping all-cause mortality risk among older Black women. Accordingly, this study problematizes the homogenization of Black women in aging research and suggests the need for health interventions that consider Black women's multiplicity of social statuses.