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 ...