LO75: The impact of snowfall on patient attendance at an urban academic emergency department. (May 2019)

• Record Type: Journal Article
• Authors: Shah, S.; Murray, J.; Mamdani, M.; Vaillancourt, S.
• Is Part Of: CJEM. Volume 21(2019)Supplement 1
• Page Start: S35
• Page End: S35
• Publication Date: 2019-05
• Subjects: forecasting, -- patient volume, -- weather; Emergency Treatment -- Periodicals; Emergency Medicine -- Periodicals; Emergency medical services -- Canada -- Periodicals; Medical emergencies -- Canada -- Periodicals; Emergency medical services; Medical emergencies; Canada; Periodicals; 616.02505
• DOI: 10.1017/cem.2019.118
• Languages: English
• ISSNs: 1481-8035

Abstract

Abstract : Introduction : Accurate forecasting of emergency department (ED) patient visits can inform better resource matching. Calendar variables such as day of week and time of day are routinely used as predictors of ED volume. Further improvement in forecasting will likely come from dynamic variables. The effect of snowfall on ED volumes in colder climates remains poorly understood. We sought to determine whether accounting for snowfall improves ED patient volume forecasting. Our secondary objective was to characterize the magnitude of effect of snowfall on ED volume.Methods : This was a retrospective observational study using historical patient volume data and local snowfall records from April 1st, 2011 to March 31st, 2018 (2, 542 days) at a single urban ED. We fit a series of four generalized linear models: a baseline model which included calendar variables and three different snowfall models which contained the variables in the baseline model plus an indicator variable for modelling snowfall. Each snowfall model had a different daily threshold for its indicator variable: any snowfall ( >0cm), moderate snowfall ( > = 1 cm), or high snowfall ( > = 5 cm). We modeled daily ED volume as the dependent variable using a Poisson distribution. To evaluate model fit, we examined the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) in each of the four models. In both cases, a lower number indicates better model fit. Incident rate ratios were calculated to determine the effect of snowfall. We used the delta method to calculate confidence intervals.Results : A total of 2542 days were used to develop the model. All three snowfall models demonstrated improved model fit compared to the baseline model with lower AIC and BIC values. The best fitting model included a binary variable for moderate snowfall ( > = 1cm/day). This model showed a statistically significant decrease in ED volume of 2.65% (95% CI: 1.23% -4.00%) on snowfall days, representing 5.4 (95% CI: 2.5 -8.2) patients per day at our hospital with an average daily volume of 205 patients.Conclusion : The addition of a snowfall variable results in improved forecasting model performance in ED volume forecasting with optimal threshold set at 1 cm of snow in our setting. Snowfall is associated with a modest, but statistically significant reduction in ED volume.