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Wildland fire smoke exposures are an increasing threat to public health, andthus there is a growing need for studying the effects of protective behaviorson reducing health outcomes. Emerging smartphone applications provideunprecedented opportunities to deliver health risk communication messages to alarge number of individuals when and where they experience the exposure andsubsequently study the effectiveness, but also pose novel methodologicalchallenges. Smoke Sense, a citizen science project, provides an interactivesmartphone app platform for participants to engage with information about airquality and ways to protect their health and record their own health symptomsand actions taken to reduce smoke exposure. We propose a new, doubly robustestimator of the structural nested mean model parameter that accounts forspatially- and time-varying effects via a local estimating equation approachwith geographical kernel weighting. Moreover, our analytical framework isflexible enough to handle informative missingness by inverse probabilityweighting of estimating functions. We evaluate the new method using extensivesimulation studies and apply it to Smoke Sense data reported by the citizenscientists to increase the knowledge base about the relationship between healthpreventive measures and improved health outcomes. Our results estimate how theprotective behaviors effects vary over space and time and find that protectivebehaviors have more significant effects on reducing health symptoms in theSouthwest than the Northwest region of the USA.

Estimating spatially varying health effects of wildland fire smoke using mobile health data