  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.
