Associations of wildfire smoke PM_2.5 exposure with cardiorespiratory events in Colorado 2011-2014

Substantial increases in wildfire activity have been recorded in recent decades. Wildfires influence the chemical composition and concentration of particulate matter ≤2.5 μm in aerodynamic diameter (PM_2.5). However, relatively few epidemiologic studies focus on the health impacts of wildfire smoke PM_2.5 compared with the number of studies focusing on total PM_2.5 exposure.

We estimated the associations between cardiorespiratory acute events and exposure to smoke PM_2.5 in Colorado using a novel exposure model to separate smoke PM_2.5 from background ambient PM_2.5 levels.

We obtained emergency department visits and hospitalizations for acute cardiorespiratory outcomes from Colorado for May-August 2011-2014, geocoded to a 4 km geographic grid. Combining ground measurements, chemical transport models, and remote sensing data, we estimated smoke PM_2.5 and non-smoke PM_2.5 on a 1 km spatial grid and aggregated to match the resolution of the health data. Time-stratified, case-crossover models were fit using conditional logistic regression to estimate associations between fire smoke PM_2.5 and non-smoke PM_2.5 for overall and age-stratified outcomes using 2-day averaging windows for cardiovascular disease and 3-day windows for respiratory disease.

Per 1 μg/m³ increase in fire smoke PM_2.5, statistically significant associations were observed for asthma (OR = 1.081 (1.058, 1.105)) and combined respiratory disease (OR = 1.021 (1.012, 1.031)). No significant relationships were evident for cardiovascular diseases and smoke PM_2.5. Associations with non-smoke PM_2.5 were null for all outcomes. Positive age-specific associations related to smoke PM_2.5 were observed for asthma and combined respiratory disease in children, and for asthma, bronchitis, COPD, and combined respiratory disease in adults. No significant associations were found in older adults.

This is the first multi-year, high-resolution epidemiologic study to incorporate statistical and chemical transport modeling methods to estimate PM_2.5 exposure due to wildfires. Our results allow for a more precise assessment of the population health impact of wildfire-related PM_2.5 exposure in a changing climate.