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Am J Emerg Med. 2019 Aug;37(8):1544-1546. doi: 10.1016/j.ajem.2019.05.032. Epub 2019 May 31.

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

Author information

1
Faculty of Medicine, University of Toronto, Toronto, Canada; Li Ka Shing Centre for Healthcare Analytics Research and Training (LKS-CHART), St. Michael's Hospital, Toronto, Canada.
2
Li Ka Shing Centre for Healthcare Analytics Research and Training (LKS-CHART), St. Michael's Hospital, Toronto, Canada; Department of Statistics, University of Toronto, Toronto, Canada.
3
Li Ka Shing Centre for Healthcare Analytics Research and Training (LKS-CHART), St. Michael's Hospital, Toronto, Canada; Department of Medicine, University of Toronto, Toronto, Canada; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Canada; Dalla Lana Faculty of Public Health, University of Toronto, Toronto, Canada; Institute for Clinical Evaluative Sciences, Toronto, Canada.
4
Li Ka Shing Centre for Healthcare Analytics Research and Training (LKS-CHART), St. Michael's Hospital, Toronto, Canada; Department of Medicine, University of Toronto, Toronto, Canada; Department of Emergency Medicine, St. Michael's Hospital, Toronto, Canada. Electronic address: sam.vaillancourt@utoronto.ca.

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:

Emergency department; Emergency medicine; Forecasting; Health services research; Snow; Weather

PMID:
31201115
DOI:
10.1016/j.ajem.2019.05.032

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