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

了解阿尔茨海默病及相关痴呆症和医疗保健中的种族和民族差异的生命全程方法

• 批准号: 10448032
• 负责人: Xi Chen
• 金额: $ 72.95万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10448032
• 关键词: Address; Affect; Algorithms; Alzheimer disease prevention; Alzheimer' s disease diagnosis; Alzheimer' s disease model; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Ambulatory Care; American; Behavioral; Biological; Black Populations; Caring; Cause of Death; Childhood; Clinical; Cost Control; Cost of Illness; Data; Data Set; Development; Diabetes Mellitus; Diagnosis; Disease; Early Diagnosis; Education; Elderly; Emergency department visit; Environment; Environmental Risk Factor; Ethnic Origin; Family; Future; Goals; Health and Retirement Study; Healthcare; Hispanic Populations; Home; Hospitalization; Hypertension; Incidence; Individual; Knowledge; Life; Life Cycle Stages; Link; Longitudinal Surveys; Machine Learning; Measures; Mediating; Mediator of activation protein; Medical; Medicare; Mental Depression; Modeling; Nature; Neuropsychology; Not Hispanic or Latino; Obesity; Older Population; Outcome; Pattern; Patterns of Care; Persons; Play; Policies; Population; Prevalence; Prevention; Preventive care; Process; Race; Research Personnel; Risk Factors; Role; Sampling; Sensitivity and Specificity; Shapes; Smoking; Social isolation; Societal Factors; Time; Trees; alcohol misuse; base; clinical care; clinical diagnosis; clinical trial participant; community-level factor; cost effective; dementia care; dementia risk; diagnostic tool; ethnic minority; health care disparity; health care service utilization; high risk; improved; life history; machine learning algorithm; machine learning model; middle age; novel; physical inactivity; pre-clinical; prevent; preventive intervention; racial and ethnic; racial and ethnic disparities; random forest; risk prediction model; screening; social; social culture

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.

项目摘要 随着美国老年人口的比例和患有阿尔茨海默病及相关疾病的人数的增加， 痴呆症（ADRD）继续迅速增长，在患病率和发病率方面存在明显的种族和族裔差距， ADRD和ADRD归因的医疗保健持续存在。这项研究旨在加深我们对种族/民族的理解 ADRD和相关卫生保健利用的差异使用生命过程的方法。我们将利用适当的 机器学习（ML）方法来研究生命过程因素，特别是早期生活环境， 在生命过程中以不同人群的方式积累，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归因的种族/民族差异 ADRD患者的医疗保健使用和结局。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有望加强人口层面的预防， 种族/民族差距。 我们的总体目标是解决与ADRD相关的健康和卫生保健不公平问题， 从生命的早期阶段开始的证据，并理想地延迟ADRD的发作或减缓ADRD的进展。到 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， 非西班牙裔黑人（黑人）、西班牙裔和非西班牙裔白人的索赔和生活史信息 1995-2018年健康与退休研究（HRS）。 我们将追求四个具体目标：1）开发和验证ML和其他ADRD预测模型， 检查生命过程因素的多因素影响; 2）了解个人和集体的贡献， ADRD的早期生活环境及其种族/民族差异; 3）检查ADRD事件对健康的影响 ADRD诊断前后的护理使用及其动态，以及种族/民族差异; 4）调查 哪些中年和晚年因素可能介导ADRD对医疗保健的影响及其种族/民族差异。 这项研究将增加显着的价值，缩小差距ADRD及其医疗保健，通过使用ML 算法，以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; 用行政数据加强一项多样化的、具有全国代表性的纵向调查， 系统地研究ADRD和医疗保健中的种族/民族差距。综合起来，这些发现将为我们提供 1)开发ADRD的风险预测模型，为人群水平提供具有成本效益的方法 在临床前阶段进行筛查，识别ADRD风险升高的风险因素和人群， 预防性干预措施; 2）可以帮助个人和临床医生进行信息评估的产品; 3）从生命早期阶段开始解决ADRD引起的健康和卫生保健不公平的政策， 利用中年和晚年介质，理想情况下延迟ADRD的发作或进展。