Abstract
Objectives
We 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.
Methods
We used daily historical patient volume data and local snowfall records from April
1st, 2011 to March 31st, 2018 (2542 days) 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 cm), moderate snowfall
(≥1 cm), or large snowfall (≥5 cm). 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.
Results
All three snowfall models demonstrated improved model fit compared to the model without
snowfall. The best fitting model included a binary variable for snowfall (<1 cm vs.
≥1 cm). This model showed a statistically significant decrease in daily ED volume
of 2.65% (95% CI: 1.23%–4.00%) on snowfall days.
Discussion
The 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.
Keywords
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Article Info
Publication History
Published online: May 31, 2019
Accepted:
May 16,
2019
Received in revised form:
May 6,
2019
Received:
March 5,
2019
Identification
Copyright
Crown Copyright © 2019 Published by Elsevier Inc. All rights reserved.
