|
Activity Number:
|
503
- Causal Inference for Spatiotemporal Data
|
|
Type:
|
Invited
|
|
Date/Time:
|
Thursday, August 11, 2022 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Section on Statistics and the Environment
|
|
Abstract #319211
|
|
|
Title:
|
Estimating Spatially Varying Health Effects in App-Based Citizen Science Research
|
|
Author(s):
|
Lili James Wu and Brian James Reich* and Shu Yang and Ana Rappold
|
|
Companies:
|
North Carolina State University and North Carolina State University and North Carolina State University and US Environmental Protection Agency
|
|
Keywords:
|
Causal inference;
Double robustness;
Treatment heterogeneity;
Propensity score;
Citizen science;
Epidemiology
|
|
Abstract:
|
Wildland fire smoke is 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 large number of individuals when and where they experience the exposure and subsequently study the effectiveness, but also pose methodological challenges. Smoke Sense, a citizen science project, provides an interactive smart phone app 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 doubly robust estimator of the structural nested mean model parameter that accounts for spatially -varying effects and informative missingness. 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. We find protective behaviors reduce symptoms and the reduction varies spatially.
|
Authors who are presenting talks have a * after their name.
