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.

抽象的 建议在 2023 年至 2024 年举办一系列混合虚拟/面对面研讨会的下两期。 这两场会议的目的是提供有关现有和最近开发的分析如何进行的新知识 方法可用于详细、可靠和可重复的分析，以在理解方面取得进展 阿尔茨海默病 (AD)、相关痴呆 (ADRD) 等健康相关差异的原因和机制， 该系列的长期目标是提供专注于与年龄相关的疾病的资源。 传播方法论知识，展示新开发方法论的能力， 扩大成熟方法的严格性和应用范围，促进大健康的正确运用 从方法论和伦理角度提供数据，并为专家和新人提供论坛 对健康差异和年龄相关疾病感兴趣的人可以讨论他们的想法并展示他们的研究。 在规划 2023-2024 年研讨会时，我们将继续开展已完成的重要工作 我们的团队并以本系列之前的研讨会和研讨会的成就为基础，包括 由本提案（R13AG069381）的母基金资助的项目在整个过程中已成为惯例。 在该系列中，2023/2024 年研讨会的具体时间表将在收到来自以下机构的意见后最终确定： 2022/2023 年参与者的积极参与证明我们在使用此模式方面取得了巨大成功。 阿尔茨海默病研究中心、少数群体机构和其他方法论焦点的代表 研讨会和研讨会的总体重点。 将于 2023 年“评估 AD/ADRD 风险和结果的异质性和结构”和“决定因素” AD/ADRD 风险：创新方法在大健康数据中的应用”2024 年。每个研讨会将 包含三场实质性会议和一场支持性方法会议。实质性会议的清单包括： i) 混合性痴呆及其组成部分的模式，ii) AD/ADRD 生存率的趋势和差异，iii) COVID-19 大流行和 AD/ADRD 风险和结果，iv) 年轻年龄在 AD 风险中的作用及相关 v) 多发病/共病和 AD 风险，以及 vi) 与临床无关的风险决定因素 AD/ADRD 及其在时间趋势和健康差异中的作用方法会议将讨论支持性的问题。 我们计划处理多种现代管理、人口和临床数据集的方法。 通过将方法创新与 实际动手演示，同时讨论观众最感兴趣的主题 通过过去研讨会期间管理的集体调查和活动后调查收集反馈。 此外，通过将研究人员以统一的形式聚集在一起，而不是将他们分开跨领域 我们努力克服此类研究所涉及领域的分散性 它以方法和数据的应用为中心，使其对早期职业调查人员特别有用。