Journal of the American Statistical Association· 2026Q1
A Spatial Interference Approach to Account for Mobility in Air Pollution Studies with Multivariate Continuous Treatments
- 1citations
- Q1SCImago
- 2026year
Short summary
A new spatial interference framework using cell phone mobility data improves causal estimates of air pollution's health effects by accounting for individuals' time spent in different geographic regions, outperforming traditional methods that only use residential addresses.
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Key points
- Develops a spatial interference framework to model air pollution exposure considering individual mobility.
- Incorporates cell phone mobility data to estimate time spent in various geographic regions.
- Derives expressions for bias incurred by ignoring mobility in exposure assessment.
- Proposes policy-relevant estimands and novel Bayesian estimation strategies.
- Applies the methodology to study air pollution's effect on mortality among US Medicare enrollees.
AI-generated from the title and abstract; the full text is not read.
Abstract
We develop new methodology to improve our understanding of the causal effects of multivariate air pollution exposures on public health accounting for mobility. Typically, in environmental health studies, exposure to air pollution for an individual is assigned based on their residential address, though many people spend time in different regions with potentially different levels of air pollution. To account for this, we incorporate estimates of the mobility of individuals from cell phone mobility data to obtain a more accurate estimate of their air pollution exposure. We treat this as an interference problem, where individuals in one geographic region can be affected by exposures in other regions due to mobility into those areas. We propose policy-relevant estimands and derive expressions showing the extent of bias one would obtain by ignoring individuals’ mobility. We additionally highlight the benefits of the proposed interference framework relative to a measurement error framework to account for mobility. Utilizing flexible Bayesian methodology we develop novel estimation strategies to estimate causal effects that account for this spatial spillover. Lastly, we use the proposed methodology to study the health effects of ambient air pollution on mortality among Medicare enrollees in the United States.
The authors' abstract, as published at the source. Journal of the American Statistical Association, 2026 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics