Abstract
BACKGROUND: 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. OBJECTIVES: 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. METHODS: 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. RESULTS: Per 1 μg/m(3) 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. DISCUSSION: 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.