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).

项目摘要 背景。父母赠款（R01 AG066793）的具体目的是进行国家流行病学 Medicare和Medicaid主张的研究估算了长期暴露对空气污染对 阿尔茨海默氏病（AD）和相关痴呆症（ADRD）住院和疾病进展（目标1） 应用机器学习方法来识别个人级别，环境和社会因素的同时出现 这导致增加脆弱性（AIM 2），并开发统计方法以消除空气的影响 其他混杂因素的污染暴露，并纠正潜在的结果错误分类（AIM） 3）。父R01依赖于广泛的流行病学数据，从环境暴露到 向气象和社会经济因素索赔数据。我们策划了大量数据 父母R01，数据尚未存入公共数据存储库，我们尚未公开将其存入 可用的。 总体目标。有了这种补充，我们的目标是实现有效的传播和重复使用高维 大量数据，包括来自父r01的数据产品。这些目标将通过形成一个 与哈佛大学数据的新合作和合作伙伴关系，扩大了他们当前的存储和分享能力 地理空间公共卫生数据。我们的具体目的是：将自动元数据提取以高 尺寸数据集格式NETCDF和HDF5（AIM 1），与Jupyter Binder进行集成 允许探索和查看来自数据的复杂高维数据（AIM 2），增强重复使用， R01研究的社区参与和可重复性，并使用数据分析证明 合成CMS声称数据（AIM 3）。 影响。 Dataverse软件平台中的每个新功能（例如AIMS 1和2中提出的功能）是 通常传播到所有77个Dataverse安装，从而增强其在全球的影响。也是数据 拥有一个充满活力的开源贡献者和数字图书馆的社区，并组织年度社区 哈佛会议持续了几天。我们将使用此平台来促进此补品中的工作 并以相同的目标参加社区并参加其他研讨会和会议。我们将密切 通过现有的数据计数来遵循这些发展的影响 项目，提供用法指标标准化（例如观看次数，下载和引用数量 数据），使我们能够自我修改并改善我们的数据发布。该补充剂也将直接 对父母R01的影响，增强重复使用，社区参与和可重复性，这反过来将领先 为了更强大的流行病学结论（AIM 3）。