Methodological challenges in spatial and contextual exposome-health studies. Issue 7 (3rd April 2023)

• Record Type: Journal Article
• Authors: Hu, Hui; Liu, Xiaokang; Zheng, Yi; He, Xing; Hart, Jaime; James, Peter; Laden, Francine; Chen, Yong; Bian, Jiang
• Is Part Of: Critical reviews in environmental science and technology. Volume 53:Issue 7(2023)
• Page Start: 827
• Page End: 846
• Publication Date: 2023-04-03
• Subjects: Deep learning -- exposome -- external exposome -- machine learning -- spatial and contextual exposome -- statistical method; Albert Juhasz and Lena Ma; Pollution -- Environmental aspects -- Periodicals; Environmental protection -- Periodicals; Environmental engineering -- Periodicals; Environmental health -- Periodicals; Nature -- Effect of human beings on -- Periodicals; 363.73
• DOI: 10.1080/10643389.2022.2093595
• Languages: English
• ISSNs: 1064-3389

Abstract

Abstract: The concept of the exposome encompasses the totality of exposures from a variety of external and internal sources across an individual's life course. The wealth of existing spatial and contextual data makes it appealing to characterize individuals' external exposome to advance our understanding of environmental determinants of health. However, the spatial and contextual exposome is very different from other exposome factors measured at the individual-level as spatial and contextual exposome data are more heterogenous with unique correlation structures and various spatiotemporal scales. These distinctive characteristics lead to multiple unique methodological challenges across different stages of a study. This article provides a review of the existing resources, methods, and tools in the new and developing field for spatial and contextual exposome-health studies focusing on four areas: (1) data engineering, (2) spatiotemporal data linkage, (3) statistical methods for exposome-health association studies, and (4) machine- and deep-learning methods to use spatial and contextual exposome data for disease prediction. A critical analysis of the methodological challenges involved in each of these areas is performed to identify knowledge gaps and address future research needs. Graphical Abstract: UF0001