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.

翻译：野火烟雾暴露对公共健康的威胁日益加剧，因此亟需研究防护行为对降低健康风险的实际效果。新兴的智能手机应用提供了前所未有的机遇：既能在个体暴露发生时向其推送健康风险提示信息，又能随后评估干预效果，但同时也带来了新的方法学挑战。公民科学项目"Smoke Sense"通过交互式智能手机应用平台，使参与者能够获取空气质量信息与健康防护指南，并记录自身健康症状及减少烟雾暴露所采取的措施。本文提出一种新型双重稳健估计量，用于结构嵌套均值模型的参数估计。该估计量通过地理核加权的局部估计方程方法，有效处理空间与时间异质性效应。此外，我们的分析框架通过估计函数的逆概率加权，能够灵活处理信息性缺失数据。我们通过大量模拟研究验证了新方法的性能，并将其应用于公民科学家报告的Smoke Sense数据，以深化关于健康防护措施与健康改善之间关系的认知。研究结果量化了防护行为效果随时空变化的规律，发现防护行为对美国西南地区健康症状的缓解作用显著强于西北地区。