Department of Cancer AI and Digital Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea; Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.
Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.
Environ Res. 2023 Apr 15;223:115451. doi: 10.1016/j.envres.2023.115451. Epub 2023 Feb 9.
Both exposure monitoring and exposure prediction have played key roles in assessing individual-level long-term exposure to air pollutants and their associations with human health. While there have been notable advances in exposure prediction methods, improvements in monitoring designs are also necessary, particularly given new monitoring paradigms leveraging low-cost sensors and mobile platforms.
We aim to provide a conceptual summary of novel monitoring designs for air pollution cohort studies that leverage new paradigms and technologies, to investigate their characteristics in real-world examples, and to offer practical guidance to future studies.
We propose a conceptual summary that focuses on two overarching types of monitoring designs, mobile and non-mobile, as well as their subtypes. We define mobile designs as monitoring from a moving platform, and non-mobile designs as stationary monitoring from permanent or temporary locations. We only consider non-mobile studies with cost-effective sampling devices. Then we discuss similarities and differences across previous studies with respect to spatial and temporal representation, data comparability between design classes, and the data leveraged for model development. Finally, we provide specific suggestions for future monitoring designs.
Most mobile and non-mobile monitoring studies selected monitoring sites based on land use instead of residential locations, and deployed monitors over limited time periods. Some studies applied multiple design and/or sub-design classes to the same area, time period, or instrumentation, to allow comparison. Even fewer studies leveraged monitoring data from different designs to improve exposure assessment by capitalizing on different strengths. In order to maximize the benefit of new monitoring technologies, future studies should adopt monitoring designs that prioritize residence-based site selection with comprehensive temporal coverage and leverage data from different designs for model development in the presence of good data compatibility.
Our conceptual overview provides practical guidance on novel exposure assessment monitoring for epidemiological applications.
暴露监测和暴露预测在评估个体水平长期暴露于空气污染物及其与人类健康的关系方面都发挥了关键作用。虽然暴露预测方法已经取得了显著进展,但监测设计的改进也是必要的,特别是考虑到利用低成本传感器和移动平台的新监测模式。
我们旨在提供一个新概念性的总结,介绍利用新范式和技术的空气污染队列研究的新型监测设计,研究其实例中的特征,并为未来的研究提供实用的指导。
我们提出了一个概念性的总结,重点关注两种总体监测设计类型,移动和非移动,以及它们的亚型。我们将移动设计定义为从移动平台进行监测,将非移动设计定义为从永久或临时位置进行的固定监测。我们只考虑具有成本效益采样设备的非移动研究。然后,我们讨论了先前研究在空间和时间表示、设计类别之间的数据可比性以及用于模型开发的数据方面的异同。最后,我们为未来的监测设计提供了具体的建议。
大多数移动和非移动监测研究根据土地利用而不是居住地点选择监测地点,并在有限的时间段内部署监测器。一些研究应用了多种设计和/或子设计类别到同一区域、时间段或仪器,以进行比较。更少的研究利用来自不同设计的数据来通过利用不同的优势来改进暴露评估。为了最大限度地受益于新的监测技术,未来的研究应采用优先考虑以居住地为基础的选址、全面的时间覆盖范围并利用不同设计的数据进行模型开发的监测设计,前提是具有良好的数据兼容性。
我们的概念性综述为暴露评估监测在流行病学应用中的新型方法提供了实用的指导。