# A Life Course Approach to Understanding Racial and Ethnic Disparities in Alzheimer's Disease and Related Dementias and Health Care

> **NIH NIH R01** · YALE UNIVERSITY · 2022 · $729,460

## Abstract

PROJECT SUMMARY
 As the share of U.S. older population and number of people living with Alzheimer's Disease and Related
Dementias (ADRD) continue to grow rapidly, marked racial and ethnic gaps in prevalence and incidence of
ADRD and ADRD-attributable health care persist. This study aims to deepen our understanding of racial/ethnic
disparities in ADRD and related health care utilization using a life course approach. We will utilize appropriate
machine learning (ML) approaches to examine how life course factors, especially early-life circumstances, may
accumulate over the life course in ways that differ across populations to shape ADRD risk and its racial/ethnic
disparities; how risk factors in midlife and later life may explain racial/ethnic disparities in ADRD-attributable
health care use and outcomes for persons with ADRD. Identifying ADRD risk in the preclinical stage is crucial,
our holistic life course approach holds promise in enhancing prevention at the population level and addressing
racial/ethnic gaps.
 Our overarching goal is to address ADRD-related health and health care inequities, guided by novel
evidence starting from early stages of life, and ideally delay the onset or slow the progression of ADRD. To
achieve our overall goal, we will adapt ML to a comprehensive set of data linking longitudinal survey, medical
claims, and life history information for non-Hispanic Blacks (Blacks), Hispanics, and non-Hispanic Whites
(Whites) in 1995-2018 Health and Retirement Study (HRS).
 We will pursue four specific aims: 1) develop and validate ML and other models for ADRD prediction,
examining multifactorial influences of life course factors; 2) understand individual and collective contributions of
early-life circumstances to ADRD and its racial/ethnic gap; 3) examine the effect of incident ADRD on health
care use and its dynamics pre- and post- ADRD diagnosis, and racial/ethnic gaps; 4) investigate the extent to
which midlife and later-life factors may mediate the effects of ADRD on health care and its racial/ethnic gap.
 This study will add significant value to narrowing disparities in ADRD and its health care, by using ML
algorithms to explore the role of a uniquely rich set of life course factors on racial/ethnic gaps in ADRD; by
augmenting a diverse and nationally representative longitudinal survey with administrative data to
systematically examine ADRD and racial/ethnic gaps in health care. Taken together, these findings will inform
1) development of risk prediction models for ADRD to offer a cost-effective approach for population-level
screening in the preclinical stage, identification of risk factors and groups at elevated risk of ADRD for targeted
preventive interventions; 2) products that can aid individuals and clinicians in making informative assessments;
and 3) policies addressing ADRD-attributable health and health care inequity starting from early stages of life,
leveraging midlife and later-life mediators, and ideally delaying the ...

## Key facts

- **NIH application ID:** 10448032
- **Project number:** 1R01AG077529-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Xi Chen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $729,460
- **Award type:** 1
- **Project period:** 2022-07-01 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10448032

## Citation

> US National Institutes of Health, RePORTER application 10448032, A Life Course Approach to Understanding Racial and Ethnic Disparities in Alzheimer's Disease and Related Dementias and Health Care (1R01AG077529-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10448032. Licensed CC0.

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