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

Wildland fire smoke exposures are an increasing threat to public health, and thus there is a growing need for studying the effects of protective behaviors on reducing health outcomes. Emerging smartphone applications provide unprecedented opportunities to deliver health risk communication messages to a large number of individuals when and where they experience the exposure and subsequently study the effectiveness, but also pose novel methodological challenges. Smoke Sense, a citizen science project, provides an interactive smartphone app platform for participants to engage with information about air quality and ways to protect their health and record their own health symptoms and actions taken to reduce smoke exposure. 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 extensive simulation studies and apply it to Smoke Sense data reported by the citizen scientists to increase the knowledge base about the relationship between health preventive measures and improved health outcomes. Our results estimate how the protective behaviors effects vary over space and time and find that protective behaviors have more significant effects on reducing health symptoms in the Southwest than the Northwest region of the USA.