A spatial interference approach to account for mobility in air pollution studies with multivariate continuous treatments

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