Short-term environmental exposures and emergency department imaging utilization: effects on imaging volumes and...

The mechanisms by which short-term environmental exposures influence emergency department (ED) imaging demand have not been systematically characterized. The purpose of this study was to evaluate short-term associations between climate-related environmental exposures and ED imaging utilization with respect to both absolute imaging volumes and per-visit imaging rates.

This time-stratified case-crossover study linked daily ED imaging counts from five academic hospitals in Toronto, Canada, to local environmental data over 11 years (2013-2023). Six exposures were evaluated: fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), ambient temperature, wind speed, rainfall, and snowfall. Conditional Poisson regression models were used to evaluate associations between environmental exposures and absolute imaging volumes and per-visit imaging rates (the latter modeled by including the natural log of daily ED patient visits as a Poisson offset).

A total of 1,946,465 imaging studies were included. Higher short-term exposure to PM_2.5 (IRR 1.020; 95%CI 1.017, 1.024 per 10 μg/m³), NO₂ (IRR 1.017; 95%CI 1.014, 1.020 per 10 ppb), and temperature (IRR 1.040; 95%CI 1.035, 1.044 per 10°C) were associated with higher imaging volumes, while wind (IRR 0.989; 95%CI 0.986, 0.991 per 10 km/h), rainfall (IRR 0.993; 95%CI 0.990, 0.996 per 10 mm), and snowfall (IRR 0.959; 95%CI 0.949, 0.969 per 10 cm) were associated with lower volumes (all p<0.001). After accounting for ED patient visit volumes, PM_2.5, NO₂, rainfall, and snowfall were associated with higher per-visit imaging rates; temperature was associated with lower per-visit imaging rates; and wind speed showed no significant association. The volume-rate dissociation was most pronounced for snowfall (4.1% volume decrease, 5.2% per-visit rate increase). Associations varied by patient and imaging subgroup.

Short-term environmental exposures influence ED imaging demand through distinct pathways, including changes in patient volumes and changes in per-visit imaging rates. Distinguishing volume-driven surges that require capacity expansion, including staffing, from acuity-driven shifts that require attention to case complexity and downstream resource intensity supports more precise operational planning in radiology.