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

项目总结 作为美国老年人口的比例和阿尔茨海默病及相关疾病患者的数量 痴呆症(ADRD)继续快速增长，在患病率和发病率方面存在明显的种族和族裔差距 可持续发展和可归因于可持续发展的医疗保健仍然存在。这项研究旨在加深我们对种族/民族的理解 使用生命过程方法的ADRD和相关卫生保健利用方面的差异。我们将适当地利用 机器学习(ML)方法研究生活过程因素，特别是早期生活环境，可能 在生命过程中以不同人群不同的方式积累，以形成ADRD风险及其种族/民族 差异；中年和晚年的风险因素如何解释ADRD的种族/民族差异-可归因于 ADRD患者的卫生保健使用和结果。在临床前阶段识别ADRD风险至关重要， 我们的整体生命过程方法在加强人口层面的预防和解决 种族/民族差距。 我们的总体目标是解决与ADRD相关的健康和医疗保健不平等问题，由Noval指导 从生命的早期阶段开始提供证据，理想情况下可以延缓ADRD的发病或进展。至 为了实现我们的总体目标，我们将使ML适应一套全面的数据链接纵向调查、医学 非西班牙裔黑人(黑人)、西班牙裔美国人和非西班牙裔白人的索赔和生活史信息 (白人)1995-2018年健康和退休研究(HRS)。 我们将追求四个具体目标：1)开发和验证ML和其他用于ADRD预测的模型， 考察生命历程因素的多因素影响；2)了解个人和集体对生命过程的贡献 早期生活环境与ADRD及其种族/民族差距；3)检查ADRD事件对健康的影响 ADRD诊断前后的护理利用及其动态，以及种族/族裔差距；4)调查 哪些中年和晚年因素可能会调节ADRD对医疗保健的影响及其种族/民族差距。 这项研究将通过使用ML，为缩小ADRD及其医疗保健方面的差距增加重要价值 探索一组独特丰富的生命历程因素对ADRD种族/民族差距的作用的算法； 利用行政数据加强具有全国代表性的多样化纵向调查，以 系统地审查ADRD和卫生保健方面的种族/族裔差距。综上所述，这些发现将告诉我们 1)为ADRD开发风险预测模型，为人口层面提供经济有效的方法 临床前阶段的筛查，识别ADRD的危险因素和高危人群 预防性干预；2)可帮助个人和临床医生进行信息评估的产品； 以及3)从生命早期阶段开始解决可归因于ADRD的健康和保健不平等问题的政策； 利用中年和晚年的调解人，理想地延缓ADRD的发生或进展。

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 onset or progression of ADRD.