Social and behavioral determinants of health and Alzheimer’s Disease: Cohort study of the US military veteran population

健康和阿尔茨海默病的社会和行为决定因素：美国退伍军人群体的队列研究

基本信息

批准号：
10591049
负责人：
HONG YU
金额：
$ 79.63万
依托单位：
UNIVERSITY OF MASSACHUSETTS LOWELL
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-03-01 至 2028-02-29
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10591049
关键词：
Address Adult Affect Age Alzheimer disease prevention Alzheimer&apos s Disease Alzheimer&apos s disease diagnosis Alzheimer&apos s disease risk Behavioral Black American Cause of Death Clinic Clinical Cohort Studies Data Dementia Development Dose Early Diagnosis Economic Factors Elderly Electronic Health Record Ethnic Origin Exposure to Generations Health Health Care Costs Health Surveys Healthcare Healthcare Systems High Prevalence Homelessness Impaired cognition Incidence Informatics Infrastructure Integrated Health Care Systems Intervention Job loss Learning Machine Learning Maintenance Measures Mental Health Modeling Natural Language Processing Neurology Not Hispanic or Latino Observational Study Outcome Patient-Focused Outcomes Patients Persons Population Post-Traumatic Stress Disorders Predisposition Primary Care Process Public Policy Race Regression Analysis Reporting Research Research Personnel Resources Retrieval Risk Risk Reduction Secure Signs and Symptoms Smoking Social Work Social isolation Stress Structure System Time Traumatic Brain Injury United States Department of Veterans Affairs Update Veterans Veterans Health Administration Vulnerable Populations Woman Work behavioral health case control cost cost effective deep learning model dementia risk design digital repositories electronic structure ethnic minority experience food insecurity health determinants high risk population human old age (65+)improved innovation intersectionality men military veteran modifiable risk opioid overdose population health predictive modeling prevent racial minority sex social social health determinants structured data substance use virtual

项目摘要

Social and behavioral determinants of health and Alzheimer’s Disease: Cohort study of the US military veteran population Alzheimer’s Disease (AD) affects an estimated 5.8 million US adults. Veterans are particularly susceptible to AD due to demographic, clinical, and economic factors. Social determinants of health are the conditions in which people are born, live, work, and age. Adverse social determinants of health include job loss and financial and food insecurity. Together with behavioral health factors (e.g., smoking and substance use) and mental health, adverse social and behavioral determinants of health (SBDH) contribute to adverse health outcomes. Associations between SBDH and AD have been noted, but most studies used structured electronic health record (EHR) or survey data. SBDH are not routinely added to structured EHR. Natural language processing (NLP) approaches can be developed to automatically extract SBDH and their attributes. This application responds to PAR-22-093 and NOT-AG-18-047. The specific aims are: Aim 1: Establish NLP-enriched case definitions of adverse SBDH and AD-related information (e.g., signs and symptoms of cognitive decline), and examine their incidences by first chart-reviewing ~10,000 EHR notes (e.g., primary care, neurology, psychiatric, and social work notes) and then developing and evaluating sophisticated NLP systems for automatically capturing SBDH and AD-related information. Aim 2: Using NLP enriched SBDH as independent variables from a nested case-control design, we will analyze the associations between adverse SBDH and incident AD. We will also evaluate how the associations vary by age, sex, race/ethnicity. We will compare results using NLP-enriched SBDH vs. using structured data (only) SBDH. Hypothesis 1: Patients with adverse SBDH have substantially higher AD risk, after adjusting for potential covariables (e.g., patient-specific demographic and clinical factors). Hypothesis 2: The effects of adverse SBDH on AD risk vary by age, sex and race/ethnicity, after adjusting for covariables (e.g., patient- specific clinical factors). Hypothesis 3: The effects of adverse SBDH on incident AD are likely cumulative and duration-dependent, with more and longer adverse SBDH leading to higher AD risk. Aim 3: Early AD diagnosis may prevent or delay AD development through intervention efforts on SBDH.34 Cognitive decline occurs 4-8 years prior to AD diagnosis.35 We will study whether inclusion of NLP-enriched adverse SBDH and AD-related information helps early AD diagnosis. We will use three types of predictive models: statistical regression, traditional machine learning, and innovative deep learning models.

健康和阿尔茨海默病的社会和行为决定因素：美国军方的队列研究退伍军人人口据估计，有 580 万美国成年人特别容易受到阿尔茨海默病 (AD) 的影响。 AD 是由人口、临床和经济因素造成的健康状况的社会决定因素。人们的出生、生活、工作和年龄对健康不利的社会决定因素包括失业和经济状况。以及食物不安全以及行为健康因素（例如吸烟和药物滥用）和精神因素。健康、不良社会和行为健康决定因素 (SBDH) 会导致不良健康结果。 SBDH 和 AD 之间的关联已被注意到，但大多数研究使用结构化电子健康记录 (EHR) 或调查数据通常不会添加到结构化自然语言处理中。可以开发（NLP）方法来自动提取 SBDH 及其属性。响应 PAR-22-093 和 NOT-AG-18-047 具体目标是：目标 1：建立 NLP 丰富的不良 SBDH 和 AD 相关信息（例如体征和症状）的病例定义认知能力下降的症状），并通过首先查看约 10,000 个 EHR 笔记的图表来检查其发生率（例如，初级保健、神经病学、精神病学和社会工作笔记），然后开发和评估复杂的用于自动捕获 SBDH 和 AD 相关信息的 NLP 系统。目标 2：使用 NLP 丰富的 SBDH 作为嵌套病例对照设计的自变量，我们将分析不良 SBDH 与 AD 事件之间的关联。我们还将评估这些关联如何。因年龄、性别、种族/民族而异。我们将比较使用 NLP 丰富的 SBDH 与使用结构化数据的结果。（仅）SBDH 假设 1：调整后，SBDH 不良的患者的 AD 风险显着更高。潜在的协变量（例如，患者特定的人口统计和临床因素）。假设 2：的影响。调整协变量（例如，患者- 假设 3：不良 SBDH 对 AD 事件的影响可能是累积性的。持续时间依赖性，更多和更长时间的不良 SBDH 会导致更高的 AD 风险。目标 3：早期 AD 诊断可以通过 SBDH 干预措施来预防或延缓 AD 的发展。34 认知能力下降发生在 AD 诊断前 4-8 年。 35 我们将研究是否包含富含 NLP 的内容不良 SBDH 和 AD 相关信息有助于早期 AD 诊断。我们将使用三种类型的预测。模型：统计回归、传统机器学习和创新深度学习模型。