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

• 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
• 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' s Disease; Alzheimer' s disease diagnosis; Alzheimer' 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

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.

健康和阿尔茨海默氏病的社会和行为决定者：美国军方的队列研究 资深人口 阿尔茨海默氏病（AD）影响约580万美国成年人。退伍军人特别容易受到 广告由于人口，临床和经济因素。健康决定者是 人们是出生，生活，工作和年龄的人。健康的不利社会决定者包括失业和财务 和粮食不安全。以及行为健康因素（例如吸烟和吸烟）和精神 健康，不利的社会和行为决定者（SBDH）会导致不利的健康成果。 已经注意到SBDH和AD之间的关联，但是大多数研究都使用了结构化电子健康 记录（EHR）或调查数据。 SBDH通常不添加到结构化的EHR中。自然语言处理 （NLP）可以开发方法来自动提取SBDH及其属性。此应用程序 响应PAR-22-093和NOT-AG-18-047。具体目的是： 目标1：建立不良SBDH和与广告相关信息的富含NLP的案例定义（例如，符号和标志和 认知能力下降的症状），并通过第一次查看〜10,000 EHR注释来检查其造型（例如 初级保健，神经病学，精神病和社会工作说明），然后开发和评估复杂的 用于自动捕获SBDH和广告相关信息的NLP系统。 AIM 2：使用NLP富含SBDH作为嵌套的情况对照设计的自变量，我们将 分析不良SBDH与事件AD之间的关联。我们还将评估关联如何 因年龄，性别，种族/种族而异。我们将使用富含NLP的SBDH与使用结构化数据比较结果 （仅）SBDH。假设1：调整后，不良SBDH患者的AD风险较高。 潜在的协变量（例如患者特定的人口统计学和临床​​因素）。假设2： 在调整协变量后，对AD风险的不利SBDH因年龄，性别和种族/种族而异（例如，患者 - 特定的临床因素）。假设3：不良SBDH对事件AD的影响可能是累积的，并且 持续时间依赖性，越来越多的广告SBDH导致更高的AD风险。 AIM 3：通过对SBDH的干预工作，早期的AD诊断可能会阻止或延迟广告开发。34 认知能力下降发生在AD诊断之前的4 - 8年。35我们将研究是否包含NLP富含 不良SBDH和与广告相关的信息有助于早期的广告诊断。我们将使用三种类型的预测 模型：统计回归，传统的机器学习和创新的深度学习模型。