Methodological Challenges in
Spatial and Contextual Exposome-Health Studies

Hui Hu Ph.D.

Assistant Professor of Medicine

Associate Epidemiologist

Channing Division of Network Medicine

Brigham and Women's Hospital and Harvard Medical School

July 27, 2022

Two domains:

• The internal exposome
• The external exposome

Source: Hu et al. 2022

Examples of publicly available spatial and contextual exposome data sources

Source: Hu et al. 2022

• 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 in 2020
   
• 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

• Different buffer sizes
• Area-/population-weighted averages
• Not necessarily circular buffers, can also use other shapes

• Challenges in scalability using buffer-based approach:
  - large number of exposures
  - large number of sample size
  - most existing packages implements in-memory processing
  
  
• Potential solutions:
  - Google Earth Engine
  - PostGIS with PG-Strom

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 appealing 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

Ackowledgements

• NIH/NHLBI K01HL153797
• NIH/NIEHS R21ES032762
• NIH/NHLBI OTAHL161847

Thank you!

hui.hu@channing.harvard.edu

hui-hu.com

github.com/benhhu