Relationship Between Multiple Environmental Exposures and CVD Incidence and Survival: Vulnerability and Susceptibility

多重环境暴露与 CVD 发病率和生存率之间的关系：脆弱性和易感性

• 批准号: 10163485
• 负责人: Francesca Dominici
• 金额: $ 24.67万
• 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: 2020-09-18 至 2022-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10163485
• 关键词: Address; Adopted; Air Pollutants; Air Pollution; Algorithms; Cessation of life; Code; Collaborations; Communities; Computer software; Data; Data Aggregation; Data Science; Data Set; Dependence; Development; Docking; Documentation; Ensure; Environmental Exposure; Environmental Health; Etiology; Exposure to; Future; Goals; Health; Hospitalization; Incidence; Language; Libraries; Licensing; Medical Records; Methods; Outcome; Output; Population; Predisposition; Procedures; Publishing; Pythons; Readiness; Research; Research Personnel; Software Engineering; Source; Source Code; Standardization; Statistical Methods; Structure; Subgroup; Testing; Time; United States National Institutes of Health; Vulnerable Populations; Weather; Work; base; cardiovascular disorder risk; cloud platform; cohort; computing resources; data analysis pipeline; data format; design; environmental agent; flexibility; improved; innovation; interest; large datasets; lifestyle factors; mortality; novel; open source; parent grant; portability; repository; response; socioeconomics; tool

Project Summary The specific aims of the parent grant (R01 ES028033) are to develop new methods to assess the association between long-term exposure to multiple environmental agents (air pollution and weather) on several health outcomes (e.g. risk of cardiovascular disease, mortality); and identify vulnerable populations. As part of the work conducted in Aim 1 of the parent grant, we developed two scientific R packages that address two common challenges in environmental health: 1) how to estimate flexibly the exposure-response curve under a statistical approach that allows users to assess causality from analyses of observational data1 (GPSmatching); and 2) how to identify subgroups of the population that are more or less vulnerable to adverse health effects of environmental exposure2 (denovo). While most environmental health research traditionally relies on existing well-established statistical approaches, there is increasing interest in the community to adopt new cutting-edge statistical tools, such as GPSmatching and denovo. GPSmatching and denovo are valuable R packages and offer innovative functionalities to researchers, however, they are not well designed in terms of software engineering best practices to be accessible to a broader scientific community and are not optimized for larger datasets and efficient utilization on the cloud. With this administrative proposal, we plan to establish a sustainable collaboration with professional software engineers to redeploy our packages and disseminate them to a wider research community. More specifically, our aims include; refactoring and improving the source code of both the GPSmatching and denovo R packages in order to meet the best software engineering practices and community open source development, improving cloud readiness by optimizing and parallelizing the R packages, and porting the packages to Python. The refactoring of the GPSmatching and denovo software packages will enable a very large user base of researchers to efficiently utilize the tools, add capabilities to the codebase, and offer improvements to the algorithms. This proposal will make these tools widely accessible to the community and will establish workflows and procedures that will allow more easily for the future dissemination of other innovative statistical methods.

项目概要 家长补助金 (R01 ES028033) 的具体目标是开发评估协会的新方法 长期接触多种环境因素（空气污染和天气）对多种健康的影响 结果（例如心血管疾病的风险、死亡率）；并确定弱势群体。作为 在母基金的目标 1 中进行的工作中，我们开发了两个科学 R 包，解决两个问题 环境健康领域的常见挑战：1）如何灵活估计特定环境下的暴露-反应曲线 统计方法，允许用户通过观察数据分析评估因果关系1（GPS匹配）； 2) 如何识别或多或少容易受到不良健康影响的人口亚群 环境暴露2（从头）。虽然大多数环境健康研究传统上依赖于现有的 随着成熟的统计方法的出现，社区越来越有兴趣采用新的尖端统计方法 统计工具，例如 GPSmatching 和 denovo。 GPSmatching 和 denovo 是很有价值的 R 软件包， 为研究人员提供创新功能，但是，它们在软件方面设计不佳 设计最佳实践以供更广泛的科学界使用，并且并未针对更大范围进行优化 云上的数据集和高效利用。 通过这项行政提案，我们计划与专业软件建立可持续的合作 工程师重新部署我们的软件包并将其传播到更广泛的研究社区。更具体地说， 我们的目标包括；重构和改进 GPSmatching 和 denovo R 包的源代码 为了满足最佳软件工程实践和社区开源开发，改进 通过优化和并行化 R 包并将包移植到 Python 来做好云准备。 GPSmatching 和 devo 软件包的重构将带来非常大的用户群 研究人员有效地利用这些工具，向代码库添加功能，并对 算法。该提案将使社区可以广泛使用这些工具，并建立工作流程 以及未来更容易传播其他创新统计方法的程序。