# Measuring the Impact of Structural Gendered Racism on Black Women’s Cognitive Aging and Risk of Alzheimer’s Disease and Related Dementia: A Mixed-Methods Investigation

> **NIH NIH K23** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $197,747

## Abstract

PROJECT ABSTRACT
Disparities in Alzheimer’s disease and related dementias (ADRD) exist across race as well as sex/gender, and
structural oppression is hypothesized to be an important social determinant. Mounting evidence demonstrates
that exposure to structural racism in early- and mid-life is associated with lower memory and neurocognitive
performance in late life. To remediate the harms of structural racism, it is essential to understand how racism
intersects with other systems of oppression, like structural sexism, to affect the cognitive aging of those with
multiple marginalized identities (e.g., being both Black and a woman). Cognitive aging is strongly impacted by
the social environment across the lifecourse and may be accelerated by early life exposures to cross-cutting
racism and sexism. Among aging Black women, exposure to intersecting structural racism and sexism (i.e.,
structural gendered racism) across the life course may compound to accelerate cognitive decline and increase
ADRD risk. This hypothesis has not been tested due to measurement limitations as well as reliance on between-
group comparative study designs, which limit our understanding of the mechanistic processes that impact
vulnerable populations and confer dementia risk. In response to this need, the scientific goal of this study is to
use a mixed-methodological, within-group approach to: (1) develop and validate a multidimensional index of
structural gendered racism using qualitative interviews with Black women, (2) identify categorically distinct latent
class profiles of structural gendered racism across index dimensions, and (3) examine the associations between
latent class profiles of early life exposure to structural gendered racism with cognitive aging and ADRD risk. To
achieve these aims, 68 Black women aged 50 or older across the United States will participate in qualitative
interviews of their experiences of gendered racism to further explicate structural domains of influence (e.g.,
gendered racism impacting access to housing). The PI will then conduct focus group interviews with research
and data experts to determine metrics that capture each gendered racism domain and identify corresponding
datasets that will be leveraged to create a state-level gendered racism index. This index will be linked to
geographically coded data on early childhood from 4,793 Black women in the Health and Retirement Study
(HRS) and their cognitive data collected over 24 years in late adulthood. These new data will be analyzed for
gendered racism latent classes, and the latent classes will be examined in relation to cognitive aging trajectories
and ADRD risk. Consistent with NIA’s Strategic Directions for Research, this study may illuminate mechanisms
underlying Black women’s risk for ADRD and identify critical intervention points that can be addressed to reduce
racial-sex/gender disparities in ADRD and improve the cognitive health of Black women. The research plan is
complemented by tra...

## Key facts

- **NIH application ID:** 10985773
- **Project number:** 1K23AG084871-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Tanisha G Hill-Jarrett
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $197,747
- **Award type:** 1
- **Project period:** 2024-09-15 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10985773

## Citation

> US National Institutes of Health, RePORTER application 10985773, Measuring the Impact of Structural Gendered Racism on Black Women’s Cognitive Aging and Risk of Alzheimer’s Disease and Related Dementia: A Mixed-Methods Investigation (1K23AG084871-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10985773. Licensed CC0.

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