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

  • Sparsh Shah
    Affiliations
    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
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  • Joshua Murray
    Affiliations
    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
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  • Muhammad Mamdani
    Affiliations
    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
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  • Samuel Vaillancourt
    Correspondence
    Corresponding author at: Department of Emergency Medicine, St. Michael's Hospital, 30 Bond St, Toronto, ON M5B 1W8, Canada.
    Affiliations
    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
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      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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