Using Digital Signals from Credit Data for Early Detection of Alzheimer's Disease and Related Dementias

使用信用数据中的数字信号早期检测阿尔茨海默病和相关痴呆症

• 批准号: 10590416
• 负责人: CAROLE R GRESENZ
• 金额: $ 69.14万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10590416
• 关键词: Acceleration; Address; Adult; Affect; Algorithms; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Automobile Driving; Biological Markers; Brain imaging; Caring; Characteristics; Computational algorithm; Data; Data Sources; Databases; Dementia; Diagnosis; Disease; Early Diagnosis; Early identification; Economics; Education; Enrollment; Equilibrium; Equity; Ethnic Origin; Family; Gender; Goals; Habits; Health; Home; Household; Individual; Life Style; Liquid substance; Machine Learning; Medicare; Medicare claim; Monitor; Moods; Neurobehavioral Manifestations; New York; Outcome; Outcome Measure; Patients; Persons; Pharmaceutical Preparations; Predisposition; Probability; Quality of life; Race; Research; Risk; Risk Factors; Safety; Screening procedure; Signal Transduction; Signs and Symptoms; Social Security Number; Source; Structure; Support System; Symptoms; Time; Update; comorbidity; cost; diagnostic tool; digital; digital technology; emerging adult; experience; financial decision making; machine learning algorithm; machine learning method; machine learning model; modifiable risk; payment; prediction algorithm; preference; research clinical testing; response; tool

Project Summary The value of early diagnosis for Alzheimer’s disease and related dementias (ADRD) is increasingly recognized. However, available diagnostic tools rely primarily on the manifestation of cognitive symptoms that interfere with everyday activities, and screening tools to support earlier identification of individuals with ADRD are lacking. Credit data represent a unique foundational data source upon which machine learning algorithms can be developed to identify individuals at risk for ADRD and facilitate earlier diagnosis. The strength of the information signal from credit data for identifying those at risk for ADRD is supported by previous research that finds, first, that significant limitations and rapid declines in financial capacity are a hallmark of early-stage disease and, second, that afflicted individuals and their families experience negative economic consequences during early-stage disease. We propose using a massive database—that we have already constructed—of credit data from Equifax which is the basis of the Federal Reserve Bank of New York’s Consumer Credit Panel (CCP), merged at the individual level using a unique common identifier (Social Security number), with Medicare enrollment and claims data. The data encompass more than 84 million person-years of data in total, with more than 1.7 million individuals who have been diagnosed with ADRD. Our specific aims are to: (1) Estimate the effects of early-stage ADRD on a wide range of financial outcomes measured in credit data, allowing for potential differences in the effects of early-stage ADRD depending on characteristics such as race/ethnicity, education, gender, and household structure; (2) Apply machine learning methods to our already- developed massive data base with merged credit (CCP) and Medicare data in order to develop algorithms that are capable of identifying individuals at risk for ADRD; and (3) Assess the robustness of the algorithm to the inclusion of newly available years of Medicare claims and enrollment data. The findings from Specific Aim 1 are important for identifying and understanding the specific financial outcomes individuals with ADRD are most susceptible to during the early stage of disease and will help inform the machine learning models in Specific Aims 2 and 3.

项目摘要 阿尔茨海默病及相关痴呆（Alzheimer's disease and related dementias，ADRD）的早期诊断价值日益受到重视 认可.然而，现有的诊断工具主要依赖于认知症状的表现， 干扰日常活动，以及支持早期识别ADRD个体的筛查工具 缺乏。信用数据代表了机器学习算法所依赖的独特的基础数据源。 可以开发用于识别ADRD风险个体并促进早期诊断。的强度 来自信用数据信息信号用于识别那些处于ADRD风险中的人得到先前研究的支持， 发现，第一，财政能力的重大限制和迅速下降是早期阶段的标志， 第二，受影响的个人及其家庭经历了负面的经济后果 在疾病早期。我们建议使用一个我们已经建立的庞大数据库， 来自Equifax的信用数据是纽约联邦储备银行消费者信用小组的基础 (CCP)，在个人层面使用唯一的共同标识符（社会安全号码）合并， Medicare登记和索赔数据。这些数据总共包含了超过8400万人年的数据， 超过170万人被诊断患有ADRD。我们的具体目标是：（1） 估计早期ADRD对信贷数据中衡量的各种财务结果的影响， 考虑到早期ADRD效应的潜在差异，这取决于以下特征， 种族/民族，教育，性别和家庭结构;（2）将机器学习方法应用于我们已经- 开发了合并信用（CCP）和医疗保险数据的海量数据库，以开发算法， 能够识别ADRD风险个体;以及（3）评估算法对ADRD风险的稳健性。 包括新获得的医疗保险索赔和登记数据。具体目标1 对于识别和理解ADRD患者的具体财务结果非常重要， 在疾病的早期阶段容易受到影响，并将有助于为特定的机器学习模型提供信息。 目标2和3。