Estimating spatially varying health effects of wildland fire smoke using mobile health data
Wildland fire smoke exposures are an increasing severe threat to public health, and thus there is a growing need for studying the effects of protective behaviors on improving health. Emerging smartphone applications provide unprecedented opportunities to study this as well as novel challenges. Smoke Sense, a citizen science project, provides an interactive platform for participants to engage with a smartphone app in recording the air quality, their health symptoms, and behaviors taken to reduce smoke exposures. We propose a new, doubly robust estimator of the structural nested mean model parameter that accounts for spatially- and time-varying effects via a local estimating equation approach with geographical kernel weighting. Moreover, our analytical framework is flexible enough to handle informative missingness by inverse probability weighting of estimating functions. We evaluate the new method using the extensive simulation studies and apply it to Smoke Sense survey data collected from smartphones for a better understanding of smoke preventive measures and health problems. Our results include the estimation of how the protective behaviors’ effects vary timely on average and locally for different spatial locations, and find that protective behaviors have more significant effects on reducing health symptoms in the Southwest than the Northwest in U.S.