高分辨率和基于时空地点的可计算规模暴露。

Erika Rasnick, Patrick Ryan, Jeff Blossom, Heike Luttmann-Gibson, Nathan Lothrop, Rima Habre, Diane R Gold, Andrew Vancil, Joel Schwartz, James E Gern, Cole Brokamp
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引用次数: 0

摘要

基于地点的暴露(称为 "地理标志物")是健康的有力决定因素,但由于缺乏开放数据和整合工具,对其的研究往往不足。现有的 DeGAUSS(用于多地点研究的分散地理标志物评估)软件已成功应用于多地点研究,确保了可重复性和健康信息的保护。然而,DeGAUSS 依赖于地理标志物数据的传输,这对于高分辨率时空数据来说并不可行,因为数据量太大,无法本地存储或通过互联网下载。我们对 DeGAUSS 框架进行了扩展,使其适用于高分辨率时空地理标记。我们的方法是将基于粗略位置和年份的数据子集存储在一个在线存储库中,然后下载适当的子集,利用准确的日期和位置在本地完成暴露评估。我们创建并验证了两个免费开源的 DeGAUSS 容器,用于估算高分辨率的每日环境空气污染物暴露量,将已发布的暴露评估模型转化为可计算的暴露量,以便进行大规模的地理标志物评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High Resolution and Spatiotemporal Place-Based Computable Exposures at Scale.

Place-based exposures, termed "geomarkers", are powerful determinants of health but are often understudied because of a lack of open data and integration tools. Existing DeGAUSS (Decentralized Geomarker Assessment for Multisite Studies) software has been successfully implemented in multi-site studies, ensuring reproducibility and protection of health information. However, DeGAUSS relies on transporting geomarker data, which is not feasible for high-resolution spatiotemporal data too large to store locally or download over the internet. We expanded the DeGAUSS framework for high-resolution spatiotemporal geomarkers. Our approach stores data subsets based on coarsened location and year in an online repository, and appropriate subsets are downloaded to complete exposure assessment locally using exact date and location. We created and validated two free and open-source DeGAUSS containers for estimation of high-resolution, daily ambient air pollutant exposures, transforming published exposure assessment models into computable exposures for geomarker assessment at scale.

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