Methodological Challenges in Spatial and Contextual Exposome-Health Studies.

Area of Science:

• Environmental Health Sciences
• Epidemiology
• Geospatial Health

Background:

• The exposome concept integrates all environmental exposures throughout life.
• Spatial and contextual exposome data present unique heterogeneity and spatiotemporal scales, differing from individual-level measurements.
• Existing data offer opportunities to study environmental health determinants but pose methodological challenges.

Purpose of the Study:

• To review existing resources, methods, and tools for spatial and contextual exposome-health research.
• To identify and analyze methodological challenges in this developing field.
• To highlight knowledge gaps and future research needs in spatial and contextual exposome analysis.

Main Methods:

• Review of data engineering techniques for heterogeneous spatial and contextual data.
• Examination of spatiotemporal data linkage methods for exposome studies.
• Analysis of statistical approaches for exposome-health association studies.
• Exploration of machine and deep learning applications for disease prediction using exposome data.

Main Results:

• Identified unique methodological challenges in data engineering, spatiotemporal linkage, statistical analysis, and machine learning for spatial exposome data.
• Highlighted the heterogeneity and distinct correlation structures of spatial and contextual exposome data.
• Provided a critical analysis of current resources and tools.

Conclusions:

• Addressing the methodological challenges in spatial and contextual exposome research is crucial for advancing our understanding of environmental health.
• Further research is needed to develop and refine methods for data integration, analysis, and prediction.
• This review serves as a foundation for future research in geospatial exposome-health studies.