Evaluation of a spatially resolved forest fire smoke model for population-based epidemiologic exposure assessment

Exposure to forest fire smoke (FFS) is associated with multiple adverse health effects, mostly respiratory. Findings for cardiovascular effects have been inconsistent, possibly related to the limitations of conventional methods to assess FFS exposure. In previous work, we developed an empirical model to estimate smoke-related fine particulate matter (PM(2.5)) for all populated areas in British Columbia (BC), Canada. Here, we evaluate the utility of our model by comparing epidemiologic associations between modeled and measured PM(2.5). For each local health area (LHA), we used Poisson regression to estimate the effects of PM(2.5) estimates and measurements on counts of medication dispensations and outpatient physician visits. We then used meta-regression to estimate the overall effects. A 10 μg/m(3) increase in modeled PM(2.5) was associated with increased sabutamol dispensations (RR=1.04, 95% CI 1.03–1.06), and physician visits for asthma (1.06, 1.04–1.08), COPD (1.02, 1.00–1.03), lower respiratory infections (1.03, 1.00–1.05), and otitis media (1.05, 1.03–1.07), all comparable to measured PM(2.5). Effects on cardiovascular outcomes were only significant using model estimates in all LHAs during extreme fire days. This suggests that the exposure model is a promising tool for increasing the power of epidemiologic studies to detect the health effects of FFS via improved spatial coverage and resolution.