Characterizing the impact of snowfall on patient attendance at an urban emergency department in Toronto, Canada.

Abstract

OBJECTIVES\nWe sought to determine whether addition of a snowfall variable improves emergency department (ED) patient volume forecasting. Our secondary objective was to characterize the magnitude of effect of snowfall on ED volume.\n\n\nMETHODS\nWe used daily historical patient volume data and local snowfall records from April 1st, 2011 to March 31st, 2018 (2542\u202fdays) to fit a series of four generalized linear models: a baseline model which included calendar variables and three different snowfall models with an indicator variable for either any snowfall (>0\u202fcm), moderate snowfall (≥1\u202fcm), or large snowfall (≥5\u202fcm). To evaluate model fit, we examined the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Incident rate ratios were calculated to determine the effect of snowfall in each model.\n\n\nRESULTS\nAll three snowfall models demonstrated improved model fit compared to the model without snowfall. The best fitting model included a binary variable for snowfall (<1\u202fcm vs. ≥1\u202fcm). This model showed a statistically significant decrease in daily ED volume of 2.65% (95% CI: 1.23%-4.00%) on snowfall days.\n\n\nDISCUSSION\nThe addition of a snowfall variable results in improved model performance in short-term ED volume forecasting. Snowfall is associated with a modest, but statistically significant reduction in ED volume.