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.

项目摘要 父母赠款的具体目的（R01 ES028033）是开发新方法来评估关联 在多种健康的长期接触多种环境药物（空气污染和天气）之间 结果（例如心血管疾病，死亡率的风险）；并确定弱势群体。作为一部分 在父母赠款的AIM 1中进行的工作，我们开发了两个科学的R包，这些套件解决了两个 环境健康中的共同挑战：1）如何灵活地估计A的暴露响应曲线 统计方法允许用户通过观察数据分析（GPSMatching）评估因果关系； 2）如何识别或多或少容易受到不利健康影响的人群的亚组 环境暴露2（DENOVO）。尽管大多数环境健康研究传统上都依赖于现有 建立良好的统计方法，社区对采用新的尖端的兴趣越来越多 统计工具，例如GPSMatching和Denovo。 GPSMatching和Denovo是有价值的R包，并且 但是，为研究人员提供创新的功能，但是，它们的设计不佳 更广泛的科学界可以使用的工程最佳实践，并且不适合大型科学界 云上的数据集和有效利用。 通过此行政建议，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包裹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软件包的源代码 为了满足最佳的软件工程实践和社区开源开发，改善 通过优化和并行化R软件包，并将软件包移植到Python来准备。 GPSMatching和Denovo软件包的重构将使非常大的用户群 研究人员有效利用工具，为代码库增加功能，并为 算法。该建议将使这些工具可广泛地访问社区，并将建立工作流程 以及将使未来其他创新统计方法传播更容易的程序。