Methodological challenges in spatial and contextual exposome-health studies

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

Keywords:

• Deep learning
• exposome
• external exposome
• machine learning
• spatial and contextual exposome
• statistical method

HANDLING EDITORS:

• Albert Juhasz and Lena Ma

Disclosure statement

The authors declare they have nothing to disclose.

Additional information

Funding

Research reported in this publication was supported in part by the National Institute of Environmental Health Sciences under award numbers R21ES032762 and P30ES000002; and in part by the National Heart, Lung, and Blood Institute under award number K01HL153797. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.