The Spatial and Contextual Exposome:
Methodological Challenges
and Paths Forward

Hui Hu Ph.D.

Assistant Professor of Medicine

Associate Epidemiologist

Channing Division of Network Medicine

Brigham and Women's Hospital and Harvard Medical School

May 8, 2025

Source: Hu et al. 2022

Examples of publicly available spatial and contextual exposome data sources

Source: Hu et al. 2022

• There is a wealth of existing data on spaital and contextual exposome
   
• Can be linked to individuals based on their geolocations to retrospectively assess historical exposures
   
• Spatial and contextual exposome are very different from other individual-level exposome factors:
  - multiple methodological challenges

• Multiple data sources available to assess the same spatial and contextual measure
   
• PM2.5:
  - Air Quality System (AQS) - EPA
  - Fused Air Quality Surface Using Downscaling (FAQSD) - EPA
  - LUR modelled estimates - CACES
  - GWR modelled estimates - ACAG
  ...
• Walkability:
  - Walk Score - Redfin
  - National Walkability Index - EPA
  ...

Data Source Identification

Source: Hu et al. 2022

• How to choose?
  - Based on spatiotemporal coverage and scale
   
• What if there are still multiple options available with similar spatiotemporal coverage and scale?
  - Traditional studies: domain knowledge + sensitivity analyses
   

• This approach is increasingly challenging in spatial and contextual exposome studies:
  - Lack of expertise
  - Infeasible to conduct sensitivity analyses
   

• Potential solution: establish reference spatial and contextual exposome databases with gold-standard measures
  - on different spatial and contextual exposome constructs
  - across different geographic areas and time periods

Variable Selection

• Large variabilities in the number of variables included in existing exposome-health research
  -  8 ~ 14,663 based on our systematic review
   
• A data source may include multiple variables measuring similar spatial and contextual exposome constructs
  - Example: ACS includes thousands of variables characterizing contextual-level social environment

Variable Selection

• For many spatial and contextual exposome factors, it is possible to generate multiple variables with the exposures aggregated at different spatiotemporal windows
  - Example: ACS, FARA

• Large impacts on downstream studies:
  - different p-value cut points used to account for multiple testing

• Potential solution:
  - develop ontology-based approaches to standardize variable selection and the approaches of making these choices

Challenge 2:

Spatiotemporal linkages of spatial and contextual exposome data to indivdiuals

Spatiotemporal Linkages

Why not just pre-link everything?

• Spatial and contextual exposome factors are spatiotemporally dynamic

Spatiotemporal Linkage

Challenges in Spatiotemporal Linkage

Challenges in Sharing of Geospatial Information

Challenge 3:

Statistical methods for spatial and contextual exposome-health studies

Many statistical methods have been developed/applied in exposome-health studies

Source: Hu et al. 2022

Differences between toxicant/chemical mixtures and the spatial and contextual exposome

2

4

3

2

4

5

Scalability

• Some methods have been applied to studies with thousands of exposures

 

Scalability

• Most existing methods have only been applied to studies with relatively small number of exposures and sample size
  - existing simulation studies often consider small scale scenarios

Lack of consensus to handle heterogeneous spatiotemporal scales

• Different spatiotemporal aggregations can lead to subsequently different associations
  - the modifiable areal unit problem
  - the modifiable temporal unit problem
   
• Two approaches widely used in the field to address data heterogeneity:
  - area-/population- and time-weighted averages based on pre-selected spatiotemporal exposure windows
  - preserve the original spatiotemporal scales and account for them in analyses using appropriate statistical methods
   
• Largely unknown:
  - performance of these two strategies
  - whether and how the modifiable areal/temporal unit problems impact results
  - whether the performance and impacts are different by exposures

Challenge 4:

Using spatial and contextual exposome data for disease prediction

• Spatial and contextual exposome data are promising to be used in disease prediction:
  - wide availability of geolocation data in both clinical and research settings
  - low cost to obtain and append these data to large number of individuals
   
• Most existing efforts so far only used single or very few spatial and contextual factors

• Sociodemographic factors: age, gender, race/ethnicity, health insurance
• Comorbidities
• County-level COVID-19 related factors (#days since first case, vaccination rates, hospital bed capacity)
• With or without the spatial and contextual exposome

• Traditional machine learning models have been predominantly used
  - manual spatiotemporal aggregations -> loss of spatiotemporal structures -> loss of model performance
  
  
  
  
  
  
  
  
  
  
   
• Deep learning approaches are appealing
  - perform automatic feature selection and engineering
  - especially useful for data with spatiotemporal structures

• Spatial and contextual data include spatiotemporal structures, which cannot be fully leveraged by traditional machine learning models
   
• Deep learning has been shown to outperform traditional machine learning to preserve spatiotemporal structures in image and time series data

• Differences between spatial/contextual data and image/time-series data
  - thousands of variables vs. few variables
  - different spatiotemporal resolutions vs. common resolution
  - for certain exposures (e.g., air pollution, green space), only a few 'pixels' matters (based on time-activity pattern)
   
• Existing deep learning model architectures cannot fully leverage the predictive power of spatial/contextual data

Summary

• Engineering of the spatial and contextual exposome data
• Spatiotemporal linkages of spatial and contextual exposome data to individuals
• Statistical methods for spatial and contextual exposome-health studies
• Using spatial and contextual exposome data for disease prediction

Acknowledgements

• NIH/NIEHS R24ES036131
• NIH/NIA RF1AG084178
• NIH/NHLBI K01HL153797
• NIH/NIEHS R21ES032762
• NIH/NHLBI OTAHL161847
• AHA 17SDG33630165

Thank you!

hui.hu@channing.harvard.edu

hui-hu.com

github.com/benhhu

SPACESCANS