The effect of weather and temporal factors on emergency hand trauma consultations

Abstract

Abstract Introduction Fluctuations in the numbers of patient consultations in hand trauma emergency units are challenging in terms of both scheduling and the provision of sufficient resources. Trauma consultations in general are affected by both temporal and meteorological variables. As the genesis and epidemiology of hand trauma have their own characteristics, this study aimed to identify the influence of temporal and meteorological factors on hand trauma consultations. Materials and methods All patients treated for hand trauma in our level one trauma center in 2019 were included in the study population and the data were analyzed in retrospect. The daily weather data, including temperature, sunshine duration, precipitation, humidity and wind speed, as well as temporal factors such as time of day, weekday and public holidays were considered and correlated with patient consultations. Gender differences were studied as well. Results We included 4787 hand trauma patients (66.4% male, mean age 38.4 ± 19.3 years, 31.7% occupational injuries). Significantly more consultations occurred on Saturdays as compared to weekdays (14.8 ± 0.6, n = 52 vs. 13.0 ± 0.2, n = 261; p = 0.028), and fewer occurred on official holidays (11.8 ± 0.5, n = 63 vs. 13.4 ± 0.2, n = 302; p = 0.0047). We found a significant positive correlation between daily consultations, sunshine duration (r = 0.14, p = 0.0056) and the mean temperature (r = 0.20, p < 0.0001); in contrast, a significant negative correlation between daily consultations and humidity (r = − 0.17, p = 0.001) was observed. Furthermore, fewer consultations were seen on days with precipitation (12.7 ± 0.3, n = 219 vs. 13.8 ± 0.3, n = 146; p = 0.009). The variation was considerably stronger in men. Conclusions Hand trauma consultations increased with increasing temperatures, duration of sunshine, and decreasing humidity. Peak admissions were seen on Fridays and Saturdays. These findings can assist in predicting days with peak admissions to allocate resources appropriately.