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

基本信息

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
项目类别：
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&apos s disease diagnosis Alzheimer&apos s disease model Alzheimer&apos s disease related dementia Alzheimer&apos 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

项目摘要

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.

项目摘要作为美国老年人口的份额以及患有阿尔茨海默氏病及相关的人数痴呆症（ADRD）继续迅速增长，在患病率和发病率的流行和种族差距上明显 ADRD和ADRD-Attributable医疗保健持续存在。这项研究旨在加深我们对种族/种族的理解使用生命课程方法，ADRD和相关医疗保健利用的差异。我们将利用适当的机器学习（ML）的方法来检查生活课程因素，尤其是早期生活的情况如何在人生过程中以各种人群不同以塑造ADRD风险及其种族/种族的方式积累差异；中年和以后生命中的危险因素如何解释Adrd-Atributut的种族/种族差异 ADRD患者的医疗保健使用和结果。在临床前阶段确定ADRD风险至关重要，我们的整体生活课程方法有望在增强人口层面的预防和解决种族/种族差距。我们的总体目标是在新颖的指导下解决与ADRD相关的健康和医疗保健不平等问题从生命的早期开始的证据，理想地延迟了ADRD的发展或减慢。到实现我们的整体目标，我们将适应ML，以链接纵向调查，医学的全面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历史信息（白人）在1995 - 2018年健康与退休研究（HRS）中。我们将追求四个具体目标：1）开发和验证ADRD预测的ML和其他模型，研究生活课程因素的多因素影响； 2）了解个人和集体贡献早期生活的环境及其种族/种族差距； 3）检查事件对健康的影响护理及其动态前后的诊断以及种族/种族差距； 4）研究范围哪些中年和后期的因素可能会介导ADRD对医疗保健及其种族/族裔差距的影响。这项研究将通过使用ML来增加ADRD及其医疗保健的狭窄差异的重要价值探索一系列独特的人生课程因素在ADRD中的种族/族裔差距中的作用；经过通过行政数据来扩大多样化和全国代表性的纵向调查系统地检查医疗保健中的ADRD和种族/族裔差距。综上所述，这些发现将告知 1）开发ADRD的风险预测模型，为人群级别提供一种成本效益的方法在临床前阶段进行筛查，鉴定有针对性的ADRD风险较高的风险因素和群体预防干预措施； 2）可以帮助个人和临床医生进行信息评估的产品； 3）从生命的早期开始，解决Adrd-Atributable-Atribut-Attribut-Attribut-Attribut-Attribut totribut totribut totribut totribut totribut totribut totribut totribut totribut totribut totribut tobributib to的健康和医疗保健不平等，利用中年和后期的介体，理想地延迟了ADRD的发作或进展。