Leveraging Existing Data and Analytic Methods for Health Disparities Research Related to Aging and Alzheimer's Disease and Related Dementias (ADRD)

利用现有数据和分析方法进行与衰老和阿尔茨海默氏病及相关痴呆症 (ADRD) 相关的健康差异研究

• 批准号: 10540591
• 负责人: IGOR AKUSHEVICH
• 金额: $ 5万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10540591
• 关键词: Address; Adult; Age; Aging; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Americas; Applications Grants; Applied Research; Applied Skills; Area; COVID-19 pandemic; Caring; Clinic; Clinical; Collaborations; Communication Methods; Communities; Data; Data Analyses; Data Analytics; Data Set; Data Sources; Diffuse; Diffusion; Discipline; Disease; Economics; Educational workshop; Electronic Health Record; Environmental Risk Factor; Epidemiology; Ethics; Event; Exposure to; Faculty; Familiarity; Feedback; Focus Groups; Funding; Future; Generations; Gerontology; Goals; Health; Health Disparities Research; Health Promotion; Heterogeneity; Hybrids; Individual; Interdisciplinary Study; Knowledge; Link; Measures; Medical; Medicare/Medicaid; Methodology; Methods; Minority-Serving Institution; Modeling; Modernization; Nature; Outcome; Participant; Pattern; Persons; Population; Postdoctoral Fellow; Publications; Reproducibility; Research; Research Design; Research Personnel; Resources; Role; Schedule; Schools; Series; Societies; Socioeconomic Factors; Statistical Methods; Statistical Models; Structure; Subgroup; Surveys; Time trend; United States Department of Veterans Affairs; Vulnerable Populations; Work; advanced analytics; age related; analytical method; analytical tool; base; career; comorbidity; data management; dementia risk; digital; disease disparity; evidence base; health data; health disparity; improved; innovation; interest; mixed dementia; multiple chronic conditions; parent grant; population health; preference; prospective; success; symposium; theories; trend; virtual; web site; yeast two hybrid system

Abstract The next two installments of a series of hybrid virtual/in-person workshops to be held in 2023-2024 are proposed. The aim of these two sessions is to provide new knowledge on how existing and recently developed analytic methods can be used for detailed, reliable, and reproducible analysis to make progress in understanding the causes and mechanisms of health-related disparities in Alzheimer’s disease (AD), related dementias (ADRD), and other prominent age-related diseases. The long-term goal of the series is to provide a resource focused on diffusing methodological know-how, demonstrating the capabilities of newly developed methodologies, expanding on the rigor and range of application of well-established methods, promoting correct use of big health data both from a methodological and ethical prospective as well as providing a forum for experts and newcomers interested in health disparities and age-related diseases to discuss their ideas and showcase their research. In the planning of the 2023-2024 workshops, we will continue the important work that has already been done by our team and build upon the accomplishments of previous Symposia and Workshops in this series including those funded by the parent grant of this proposal (R13AG069381). As has become customary over the course of the series the exact schedules of the 2023/2024 workshops will be finalized after receiving input from 2022/2023 participants. We have had great success in using this model as evidenced by active participation of representatives of Alzheimer's Disease research centers, minority institutions, and other methodological focus groups as well as the increasing attendance of our workshops and symposia. The overall focus of the workshops will be: “Evaluating heterogeneity and Structure in AD/ADRD risks and outcomes” for 2023 and “Determinants of AD/ADRD risks: application of innovative methodologies to Big Health Data” for 2024. Each workshop will contain three substantive and one supporting methodologic session. The list of substantive sessions will include: i) patterns of mixed dementia and their components, ii) trends and disparities in AD/ADRD survival, iii) the COVID-19 pandemic and AD/ADRD risk and outcomes, iv) role of younger ages in the risk of AD and associated disparities, v) multimorbidity/comorbidity and AD risk, and vi) non-clinic-related determinants of the risk of AD/ADRD and their role in time trends and health disparities. The methodologic session will discuss supportive approaches that will deal with multiple modern administrative, population, and clinical datasets. We plan to maintain the accessibility level of the methodologic presentations by combining methodological innovations with practical hands-on demonstrations while addressing topics of the highest interest to the audience based on feedback gathered using pool and post-event surveys administered over the course of past Workshops. Furthermore, by bringing researchers together in a unified format rather than separating them across field- specific sections we endeavor to overcome the fragmented nature of the fields involved in such research by centering it around the application of methods and data, making it especially useful for early career investigators.

摘要 建议在2023-2024年期间举办一系列虚拟/面对面混合讲习班的下两个阶段。 这两个会议的目的是提供新的知识，如何现有的和最近开发的分析 方法可用于详细、可靠和可重复的分析，以在理解 阿尔茨海默病（AD）、相关痴呆（ADRD）、 和其他与年龄有关的疾病。该系列的长期目标是提供一种资源， 传播方法学知识，展示新开发方法的能力， 扩大成熟方法应用的严谨性和范围，促进正确使用大健康 从方法和伦理角度收集数据，并为专家和新来者提供论坛 对健康差距和与年龄有关的疾病感兴趣，讨论他们的想法并展示他们的研究。在 2023-2024年研讨会的规划，我们将继续已经完成的重要工作， 我们的团队，并建立在以前的研讨会和研讨会在这个系列的成就，包括 由本提案的母基金资助的项目（R13 AG 069381）。这已经成为课程的惯例 在该系列中，2023/2024年研讨会的确切时间表将在收到来自 2022/2023参与者。我们在使用这一模式方面取得了巨大成功， 阿尔茨海默病研究中心的代表，少数民族机构和其他方法的重点 我们的工作组以及越来越多的参加我们的讲习班和专题讨论会。讲习班的总体重点 将是：2023年“评估AD/ADRD风险和结局的异质性和结构”和“决定因素” AD/ADRD风险：创新方法在大健康数据中的应用”。每个工作坊将 包括三次实质性会议和一次辅助性方法会议。实质性会议清单将包括： i）混合型痴呆及其组成部分的模式，ii）AD/ADRD存活率的趋势和差异，iii）AD/ADRD存活率的变化， 2019冠状病毒病大流行和AD/ADRD风险和结局，iv）年轻人在AD风险中的作用和相关 差异，v）多发病/合并症和AD风险，和vi）非临床相关的风险决定因素， AD/ADRD及其在时间趋势和健康差异中的作用。方法论会议将讨论支持 将处理多个现代管理、人口和临床数据集的方法。我们计划 通过将方法创新与以下方面相结合， 实际动手示范，同时解决最感兴趣的主题，以观众的基础上， 在过去的研讨会上，通过汇总和事后调查收集反馈。 此外，通过以统一的形式将研究人员聚集在一起，而不是将他们跨领域分开， 具体部分，我们奋进克服这些研究所涉及的领域的分散性， 围绕方法和数据的应用，使其特别有用的早期职业调查。