Project 1 (RP1): Characterize and Intervene in Early-Life stage Stressors Using Mixed Methods.

This project studies how both chemical (e.g., air pollution) and non-chemical (e.g., social and environmental conditions) stressors affect children’s school absences due to gastrointestinal and respiratory illnesses. We are testing two practical, low-cost solutions:

• Portable air purifiers in classrooms (Engineering Control)
• A three-tier, school-based environmental health education program (administrative control)

Causal relationships are evaluated using a 2×2 cluster randomized factorial design alongside a complementary quasi-experimental approach, supported by qualitative insights from family contexts.

Project 2 (RP2): Develop a Stakeholder-Driven & Data-DrivenChildren’s Health & Social (CHS) Vulnerability Index.

This project combines data from indoor sensors, outdoor monitoring (e.g., EPA), weather systems (Mesonet), and advanced modeling tools to better understand environmental conditions. Using machine learning, we predict how these conditions affect children’s health.

This information is used to create a vulnerability index that helps communities take targeted action to reduce school absenteeism and improve health outcomes.