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：对美国军方的队列研究 退伍军人人口 据估计，美国有580万成年人患有阿尔茨海默病。退伍军人特别容易患上 AD是由于人口、临床和经济因素造成的。健康的社会决定因素是 人的出生、生活、工作和年龄。健康的不利社会决定因素包括失业和经济状况 和粮食不安全。与行为健康因素(如吸烟和药物使用)和精神健康因素一起 健康、不利的社会和行为健康决定因素导致不利的健康结果。 SBDH和AD之间的关联已经被注意到，但大多数研究使用结构化电子健康 记录(EHR)或调查数据。SBDH不是常规地添加到结构化EHR中。自然语言处理 可以开发(NLP)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。具体目标是： 目标1：建立不良的SBDH和AD相关信息(例如，体征和AD)的NLP丰富的病例定义 认知衰退的症状)，并通过第一次查看图表-查看约10,000个EHR笔记来检查它们的发生率(例如， 初级保健、神经学、精神病学和社会工作笔记)，然后开发和评估复杂 用于自动捕获SBDH和AD相关信息的NLP系统。 目的2：使用NLP富含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事件之间的关系。我们还将评估这些协会如何 因年龄、性别、种族/民族而异。我们将比较使用NLP丰富的SBDH和使用结构化数据的结果 (仅限)SBDH。假设1：经校正后，SBDH阴性的患者患AD的风险显著增加 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： 在调整了协变量后，AD风险的不利SBDH因年龄、性别和种族/民族而异(例如，患者- 特定的临床因素)。假设3：不良SBDH对AD事件的影响可能是累积的和 持续时间依赖，更多和更长的不良SBDH导致更高的AD风险。 目的3：早期诊断阿尔茨海默病可能通过干预SBDH.34预防或延缓阿尔茨海默病的发展 认知能力下降发生在AD诊断前4-8年。35我们将研究是否纳入NLP丰富的 阴性的SBDH和AD相关信息有助于AD的早期诊断。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。

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.