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:
  • 金额:
    $ 79.63万
  • 依托单位:
  • 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

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:美国军队的队列研究

项目成果

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HONG YU其他文献

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{{ truncateString('HONG YU', 18)}}的其他基金

Improving Suicide Prediction using NLP-Extracted Social Determinants of Health
使用 NLP 提取的健康社会决定因素改善自杀预测
  • 批准号:
    10656321
  • 财政年份:
    2020
  • 资助金额:
    $ 79.63万
  • 项目类别:
Improving Suicide Prediction using NLP-Extracted Social Determinants of Health
使用 NLP 提取的健康社会决定因素改善自杀预测
  • 批准号:
    10428629
  • 财政年份:
    2020
  • 资助金额:
    $ 79.63万
  • 项目类别:
Improving Suicide Prediction using NLP-Extracted Social Determinants of Health
使用 NLP 提取的健康社会决定因素改善自杀预测
  • 批准号:
    10251336
  • 财政年份:
    2020
  • 资助金额:
    $ 79.63万
  • 项目类别:
Improving Suicide Prediction using NLP-Extracted Social Determinants of Health
使用 NLP 提取的健康社会决定因素改善自杀预测
  • 批准号:
    10100989
  • 财政年份:
    2020
  • 资助金额:
    $ 79.63万
  • 项目类别:
Resource Curation and Evaluation for EHR Note Comprehension
EHR 笔记理解的资源管理和评估
  • 批准号:
    9925807
  • 财政年份:
    2018
  • 资助金额:
    $ 79.63万
  • 项目类别:
Resource Curation and Evaluation for EHR Note Comprehension
EHR 笔记理解的资源管理和评估
  • 批准号:
    9794757
  • 财政年份:
    2018
  • 资助金额:
    $ 79.63万
  • 项目类别:
Systems for Helping Veterans Comprehend Electronic Health Record Notes
帮助退伍军人理解电子健康记录笔记的系统
  • 批准号:
    9768225
  • 财政年份:
    2015
  • 资助金额:
    $ 79.63万
  • 项目类别:
Systems for Helping Veterans Comprehend Electronic Health Record Notes
帮助退伍军人理解电子健康记录笔记的系统
  • 批准号:
    9894743
  • 财政年份:
    2015
  • 资助金额:
    $ 79.63万
  • 项目类别:
EHR Anticoagulants Pharmacovigilance
EHR 抗凝剂药物警戒
  • 批准号:
    9190384
  • 财政年份:
    2014
  • 资助金额:
    $ 79.63万
  • 项目类别:
EMR Adverse Drug Event Detection for Pharmacovigilance
用于药物警戒的 EMR 药物不良事件检测
  • 批准号:
    9123554
  • 财政年份:
    2014
  • 资助金额:
    $ 79.63万
  • 项目类别:

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