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Introduction: On March 16 2017, emergency departments and urgent care centres (collectively, EDs) in Calgary saw 3 times the number of fall-related ED visits, and 8 times the number of ED orthopedic consultations and admissions than the daily average for March 2014-2016. Fall-related injuries have significant associated morbidity and burden of disease, as well as cost to the health care system, caregivers and society. The purpose of this study was to use regression analysis to generate best fit models and identify weather and temporal variables which predict the frequency of fall-related ED visits, orthopedic consultations and admissions in winter (November-March). Methods: Daily number of ED visits, orthopedic consults, and orthopedic admissions for presenting complaint of Lower Extremity Injury, Upper Extremity Injury, or with an ED diagnosis of Fracture or Fall, were obtained for winter months from November 1 2013 to March 31 2017 from the Alberta Health Services ED database. Weather data was obtained from Environment Canada. Linear and multiple regression were performed to evaluate the predictive value of individual weather and temporal parameters, and derive the best-fitting model to predict the number of ED visits, orthopedic consultations, and orthopedic admissions. Results: Individual predictive factors (p<0.05) were month, temperature, overnight temperature drop from >0C to <0C, day of the week, amount of snow on the ground at 05:00, post-chinook day (chinooks are a warm winter wind in Calgary that can cause large temperature swings), maximum wind gust speed, and presence of precipitation. The best-fit multivariable models predicting fall-related ED visits (F-stat=15.36, R2=0.171), orthopedic consults (F-stat=6.369, R2=0.048), and orthopedic admissions (F-stat=8.658, R2=0.126) were statistically significant (probability of F-statistics all <0.0001). Conclusion: This study is, to the best of our knowledge, the first to use multiple regression to compute models using weather and temporal variables that can predict fall-related ED visits, orthopedic consults and admissions. This information could be used to alert the population regarding an increased fall and fracture risk ahead of the weather occurrence, as well as municipal snow and ice clearing services, who may be able to mitigate that risk. The ability to predict the frequency of fall-related injuries could enable EDs, EMS, orthopedic services, and hospitals to adjust resource and staffing allocation in anticipation of increases in fall-related injuries.
