Abstract
Slip and fall injuries represent a significant burden to the Canadian general public and healthcare system; the annual financial cost of these accidents in Canada is estimated to be $2 billion (2014). Interestingly, slip and fall accidents are not evenly distributed across the provinces, with the rate of hospitalization due to falls in Alberta being nearly three times greater than the rate in Ontario. Our research aim was to create the Alberta Slip and Fall Index (ASFI) – a simple scale like the UV or Air Quality index – that could be used to warn the general public about the presence of slippery conditions. The ASFI could be paired with interventions proven to prevent outdoor slips and falls, like promoting the use of ice cleats.
Eleven years (January 2008 - December 2018) of emergency room presentations to the four adult hospitals in Calgary, Alberta were filtered based on the ICD-10 diagnostic code W00 (slip and fall due to ice and snow). Multivariable dispersion-corrected Poisson regression models were used to analyze the weather conditions and time of year most predictive of slip and fall injuries. A slip and fall risk calculator (the ASFI) was designed using output from statistical modelling. To validate the ASFI we compared model predicted slip and fall risk to real presentations using retrospective weather and patient data.
The final dataset included 14,977 slip and fall incidents. The three months with the most emergency room presentations were January(n = 3591), February(n = 2997), and March(n = 2954); each of these predicted increased slip and fall accidents(p < 0 .001). Same day ice was significantly associated with more slip and fall accidents, as was the presence of ice one, two, and three days prior(p < 0 .001). Snow one day prior was mildly protective against slip and fall accidents, but this effect was not significant(p = 0.861). Snow, ice, and time of year variables can be input into the ASFI calculator, which computes the likelihood of slip and fall accidents on a 0-40 point scale, with 40 indicating maximum fall risk. Upon validation of the ASFI, we generally found days with the highest raw frequency of slip and fall accidents had higher ASFI scores. Although the ASFI can theoretically result in a score of 40, when we entered realistic weather conditions it was impossible to create a score higher than 20.
The ASFI represents a tool that can be used to prevent slip and fall accidents due to icy and snowy conditions. As demonstrated by our inability to maximize the risk score when using realistic weather conditions, the ASFI is imperfect. Despite its shortcomings, the ASFI is a preliminary step towards effectively disseminating information about the weather conditions likely to lead to falls. Ideally, a refined ASFI will help people better understand when to use protective equipment and take extra precaution outdoors. If implementing the ASFI led to even a 1% decrease in injuries caused by falls, the annual Canadian healthcare savings would be roughly $2 million.
