Year of Award

2024

Document Type

Dissertation

Degree Type

Doctor of Philosophy (PhD)

Degree Name

Toxicology

Department or School/College

School of Public and Community Health Sciences

Committee Chair

Erin Landguth

Commitee Members

Jon Graham, Zeina Jaffar, Christopher Migliaccio, Curtis Noonan

Publisher

University of Montana

Abstract

Background: In recent years, air pollution, particularly fine particulate matter (PM2.5) originating from wildfire smoke, has surged despite declining anthropogenic emissions. This trend has led to concerning adverse health effects across many different morbidity and mortality outcomes. Still, many gaps remain in understanding of how wildfire-specific smoke impacts our health, including how to isolate the contribution of wildfire-originating PM2.5 within the total ambient or background PM2.5, as well as how and when delayed wildfire smoke exposure will impact certain disease outcomes, and relevant to this dissertation, particularly for communicable respiratory infections, such as influenza.

Purpose: The purpose of this research was to consolidate existing literature on the link between increased influenza risk and PM2.5, determine how best to extract wildfire-specific PM2.5 from background ambient PM2.5, and ultimately, quantify the relationship between influenza and wildfire-specific PM2.5 across a broad geographic region of the western United States.

Methods: A review with comparative analyses were used to determine the state-of-the art for isolating wildfire-specific PM2.5 from the total ambient PM2.5. Then, a meta-analysis was utilized to summarize what is known about the relationships (both correlations and causations) between influenza risk and PM2.5. Finally, applying a seasonal thresholding method, we isolated wildfirespecific PM2.5 for six western states (Arizona, Colorado, Montana, Nevada, Oregon, and Washington) from 2010 to 2019. We exploited influenza or influenza-like illness (ILI) data from the six state health departments. Employing generalized linear distributed lag models adjusted for confounding variables, our investigation centered on assessing the impact of PM2.5 exposure during preceding wildfire seasons on influenza or ILI risk during subsequent flu seasons, as well as the short-term effects of PM2.5 during the current flu season.

Results: The scoping review and comparative analysis revealed 3 wildfire-specific PM2.5 datasets that generally agreed better with each other in higher wildland fire activity years and in more rural locations. Key findings from the systematic review and meta-analysis revealed 16 studies with overall results that showed a 10 μg/m³ increase in daily PM2.5 levels leading to a 1.5% rise in influenza risk (95% CI: 0.08%, 2.2%), the majority of studies occurred in Asia, and differences observed across temperatures and delayed lag times post-exposure. Finally, the modeling of the impact of wildfire-specific PM2.5 on influenza risk across 6 western US states revealed distinct state groupings based on the effects of wildfire season exposure, with some states exhibiting increased influenza risk (Colorado, Nevada, Oregon) and others showing decreased risk (Arizona, Montana, Washington). Moreover, in certain states (Colorado, Nevada, Oregon, Washington), short-term PM2.5 exposure (0 – 1 weeks) was associated with an immediate heightened risk, while in others, longer delayed effects (2 – 4 weeks) were observed (Arizona, Montana, Oregon, Washington).

Conclusions: This study adds to the evidence that exposure to PM2.5 contributes to the risk of influenza. However, gaps remain concerning generalizability across exposure timing. In addition, this study revealed that very few wildfire-specific PM2.5 datasets are made publicly accessible, despite the abundance of wildfire-specific PM2.5 exposure modeling. Open-sourced data is vital for advancing research, driving innovation, and enhancing transparency across scientific fields, and as wildfire-specific PM2.5 models and datasets begin to proliferate, health researchers have a unique opportunity to explore intermodel comparisons, which allows for further insights and may lead to more robust conclusions.

Available for download on Wednesday, June 18, 2025

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