Enhancing SPACE, an innovative python package to account for spatial confounding used to estimate climate-sensitive events among older Medicare

项目摘要 世卫组织将空气污染和气候变化列为2019年十大威胁中的两个，以及更早的研究 表明气候变化暴露和大脑健康之间的联系。此外，老年人的负担 阿尔茨海默病(AD)和相关痴呆症(ADRD)预计到2060年将翻一番，其中最大的 拉美裔美国人的人数增加了。气候变化对环境的影响可能会对大脑健康 我们没有准备好应对的紧急情况。迄今为止，人们对高温或空气污染的影响知之甚少， 包括野火烟雾，所有这些都受到气候变化的影响，对患有AD/ADRD的老年人来说。我们的 Parent R01通过估计高温和空气污染对病因的影响来解决这些科学空白 具体的入院、再入院和死亡率，并传播在这些研究中使用的统计方法 分析。考虑到由各种因素(例如，社会经济， 人口统计学、气象学)与野火烟雾和高温暴露以及ADRD有关 住院治疗至关重要。有多种方法可以针对空间混淆进行调整，但也有以下几种 没有明确的指导方针，说明在什么情况下应该使用哪种方法。为了解决这个问题，作为 我们开发的父R01 Spacebench是一款基于Python的统计软件，用于比较 使用基准数据集表示真实数据的空间混淆算法，并允许研究人员 为特定数据集选择最佳方法。虽然Spacebench是一款创新的软件，但它是 开发的目的是确定功能的优先顺序，但还需要做更多的工作来确保它遵循软件工程 最佳做法和重大改进是必要的，以使其更容易为更广泛的受众所接受。在……里面 这一行政补充，我们建议通过重构 现有代码(目标1)，通过添加允许更广泛用户基础的R API将软件导入R(目标2)， 以及提高再现性，为云使用提供容器和文档(目标3)。重构 SPACEBENCH软件包的使用将使大量研究人员能够有效地利用该工具， 向代码库添加功能，并改进在 软件。然后，我们将把这个工具应用到我们对气候相关变量和AD/ADRD结果的研究中。 云就绪战略消除了特定的依赖关系，并允许更广泛的可用性。优化的和 重构的SPACEBASE包将为解释空间混杂提供一个更好的框架 与暴露于环境制剂有关，将适用于广泛的环境 健康研究。

Project Summary The WHO listed air pollution and climate change as two of the top ten threats in 2019, and earlier research indicates links between climate change exposures and brain health. Further, the burden of older persons with Alzheimer’s disease (AD) and related dementias (ADRD) is expected to double by 2060, with the largest increase for Hispanic Americans. The environmental impact of climate change could become a brain health emergency that we are unprepared to tackle. To date, little is known regarding impacts of heat or air pollution, including wildfire smoke, all of which are impacted by climate change, on the elderly with AD/ADRD. Our parent R01 addresses these scientific gaps by estimating the impact of both heat and air pollution on cause specific admissions, readmissions, and mortality and disseminating the statistical methods used in these analyses. Accounting for spatial confounding that results from various factors (e.g. socioeconomic, demographic, meteorological) being associated with both wildfire smoke and heat exposure and ADRD hospitalizations is critical. There are various approaches to adjust for spatial confounding, however, there are no clear guidelines on which approach should be used under which setting. To solve this problem, as part of the parent R01 we developed spacebench, a python based statistical software to compare the performance of spatial confounding algorithms using benchmark datasets representing the real data and allowing researchers to select the optimal method for a specific dataset. While spacebench is an innovative software, it was developed to prioritize functionality but more work needs to be done to ensure it follows software engineering best practices, and significant improvement is necessary to make it more accessible to a wider audience. In this administrative supplement, we propose to enhance the existing spacebench software by refactoring the existing code (Aim 1), importing the software to R by adding an R API allowing for a wider user base (Aim 2), and increasing reproducibility providing containers and documentation for cloud usage (Aim 3). The refactoring of the spacebench software package will enable a large user base of researchers to efficiently utilize the tool, add capabilities to the codebase, and offer improvements to the spatial confounding algorithms implemented in the software. Then we will apply this tool to our research on climate-related variables and AD/ADRD outcomes. The cloud readiness strategies remove specific dependencies and allow wider usability. The optimized and refactored spacebench packages will provide a superior framework for accounting for spatial confounding associated with exposure to environmental agents which will be applicable to a wide range of environmental health research.