Project Summary/Abstract Marginalized populations have both greater rates of Alzheimer’s disease (AD) and greater risk of misdiagnosis, which can delay access to treatment and caregiving. These differences reflect a life course cascade of social inequity in the United States that disproportionately impacts historically marginalized racial and ethnic groups and results in a host of physical and cognitive health disparities. Racial and ethnic differences in neuropsychological test performance, which is commonly used to diagnose AD, are well- documented and may contribute to racial/ethnic differences in the detection and diagnosis of AD. The effects of this structural racism begin in early life experiences and extend to educational and occupational opportunities, social mobility, wealth accumulation, and stress. Differences in social classes of origin (SCO) often persist across the lifespan and develop into differences in adult socioeconomic status (SES). Associated with adult SES are additional contextual SES factors that can mediate health risks like food scarcity and access to healthcare. Beyond SES and associated financial stressors, racial and ethnic minorities also face greater social stress from experiences of discrimination, which have been linked to greater cognitive risk. These differences in risk compound measurement biases in neuropsychological testing, making accurate diagnosis of impairment more difficult and contributing to the observed racial/ethnic differences in scores. The proposed study will examine these complex life course factors to better understand why racial and ethnic disparities arise in AD and how clinicians can reduce the risk of misdiagnosis. In order to study each of these factors in the necessary complexity, the Health and Retirement Study and Harmonized Cognitive Assessment Protocol datasets will be used to study the effects of SCO, adult SES, contextual SES factors, stress, and test bias on verbal memory, which robustly declines early in the AD disease course. The first two study aims utilize Bayesian explanatory item response theory to separate group-level racial/ethnic differences attributable to measurement bias in the tests themselves from the effects of life course social inequities as a cause of the group-level differences. The final aim will test the resulting models’ ability to improve diagnostic accuracy for AD among minority populations. In line with the National Institute of Aging’s Health Disparities Research Framework, these models leverage environmental, sociocultural, and behavioral factors to understand the interactions and pathways that life course differences have in normal and abnormal aging, dementia risk, and measurement bias. The fellowship will support training and mentoring in the complexities of normal and abnormal cognitive aging, sophisticated statistical methods, and role of social equity in AD assessment to support my growth as a researcher in AD disparities and the development of ne...