National Cohort Studies of Alzheimer's Disease, Related Dementias and Air Pollution

阿尔茨海默病、相关痴呆症和空气污染的国家队列研究

• 批准号: 10594215
• 负责人: Antonella Zanobetti
• 金额: $ 23.82万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10594215
• 关键词: Air Pollution; Algorithms; Alzheimer' s disease related dementia; Censuses; Code; Cohort Studies; Collaborations; Communities; Complex; Computer Systems; Computer software; Data; Data Aggregation; Data Analyses; Data Set; Deposition; Detection; Development; Digital Libraries; Dimensions; Disease Progression; Docking; Educational workshop; Environmental Exposure; Environmental Risk Factor; Epidemiologic Methods; Epidemiology; Exposure to; FAIR principles; Goals; Hospitalization; Individual; Infrastructure; Ingestion; Internet; Lead; Libraries; Long-Term Effects; Maps; Medical; Medicare/Medicaid; Metadata; Meteorological Factors; Outcome; Parents; Population; Public Health; Publishing; Reproducibility; Research; Research Personnel; Side; Societal Factors; Socioeconomic Factors; Source; Standardization; Statistical Methods; TRUST principles; Taxonomy; Time; Trust; United States Centers for Medicare and Medicaid Services; Work; community engagement; data exploration; data format; data handling; data repository; data sharing; data tools; data visualization; epidemiologic data; epidemiology study; health data; high dimensionality; improved; machine learning method; meetings; multidimensional data; open source; parent grant; repository; socioeconomics; symposium; virtual

Project Summary Background. The specific aims of the parent grant (R01 AG066793) are to conduct national epidemiological studies of Medicare and Medicaid claims to estimate the effects of long-term exposures to air pollution on Alzheimer’s disease (AD) and related dementias (ADRD) hospitalization and disease progression (Aim 1), to apply machine learning methods to identify co-occurrence of individual-level, environmental, and societal factors that lead to increase vulnerability (Aim 2), and to develop statistical methods to disentangle the effects of air pollution exposure from other confounding factors and to correct for potential outcome misclassification (Aim 3). The parent R01 relies on a wide range of epidemiological data ranging from environmental exposures, to claims data, to meteorological and socioeconomic factors. While we curated massive amounts of data for the parent R01, the data has not been deposited to a public data repository and we have not made it publicly available. Overall Goals. With this supplement our goal is to enable effective dissemination and reuse for high-dimensional high-volume data, including the data products from the parent R01. These goals will be achieved by forming a new collaboration and partnership with Harvard dataverse, expanding their current capacity to store and share geospatial public health data. Our specific aims are to: implement automatic metadata extraction for high dimensional dataset formats NetCDF and HDF5 (Aim 1), implement an integration with Jupyter Binder that allows exploration and viewing of complex high-dimensional data from Dataverse (Aim 2), enhance reuse, community engagement and reproducibility of R01 research with the demonstration of data analysis using synthetic CMS claims data (Aim 3). Impact. Each new feature in the Dataverse software platform, such as the ones proposed in Aims 1 and 2, is typically propagated to all 77 Dataverse installations, which enhances their impact worldwide. Dataverse also has a vibrant community of open-source contributors and digital libraries, and organizes annual community meetings at Harvard that last for several days. We will use this platform to promote the work in this supplement and engage the community and attend other workshops and conferences with the same goal. We will closely follow the impact of these developments through an existing Dataverse collaboration with the Make Data Count project, which provides usage metrics standardization (such as the number of views, downloads, and citations of data), and enables us to self-modify and improve our data releases. This supplement will also have a direct impact on the parent R01, enhancing reuse, community engagement and reproducibility which will in turn lead to more robust epidemiological conclusions (Aim 3).

项目概要 背景 家长补助金 (R01 AG066793) 的具体目标是开展全国流行病学调查。 对医疗保险和医疗补助索赔的研究旨在估计长期暴露于空气污染对健康的影响 阿尔茨海默病 (AD) 和相关痴呆 (ADRD) 住院治疗和疾病进展（目标 1）， 应用机器学习方法来识别个人层面、环境和社会因素的共现 导致脆弱性增加（目标 2），并开发统计方法来理清空气的影响 其他混杂因素造成的污染暴露并纠正潜在的结果错误分类（目标 3) 母体 R01 依赖于广泛的流行病学数据，从环境暴露到 我们根据气象和社会经济因素收集了大量数据。 父R01，数据尚未存入公共数据存储库，我们尚未公开 可用的。 总体目标 通过此补充，我们的目标是实现高维度的有效传播和重用。 大容量数据，包括来自母公司 R01 的数据产品，这些目标将通过形成一个来实现。 与哈佛数据宇宙的新合作和伙伴关系，扩大了他们当前的存储和共享能力 我们的具体目标是：实现高地理空间公共卫生数据的自动元数据提取。 维度数据集格式 NetCDF 和 HDF5（目标 1），实现与 Jupyter Binder 的集成 允许探索和查看来自 Dataverse 的复杂高维数据（目标 2），增强重用， R01 研究的社区参与度和可重复性，并使用数据分析进行演示 合成 CMS 索赔数据（目标 3）。 Dataverse 软件平台中的每项新功能（例如目标 1 和 2 中提出的功能）都具有影响力。 通常会传播到所有 77 个 Dataverse 设施，这也增强了 Dataverse 在全球的影响力。 拥有一个充满活力的开源贡献者和数字图书馆社区，并组织年度社区 在哈佛大学举行的持续几天的会议我们将利用这个平台来推广本增刊的工作。 我们将密切关注社区并参加其他具有相同目标的研讨会和会议。 通过现有的 Dataverse 与 Make Data Count 的合作来跟踪这些发展的影响 项目，提供使用指标标准化（例如浏览量、下载量和引用量） 数据），并使我们能够自我修改和改进我们的数据发布。 对母体 R01 的影响，增强重用、社区参与和可重复性，这反过来又会导致 得出更可靠的流行病学结论（目标 3）。