Background: We sought to determine whether trauma patient admission volume to our Level I trauma center was correlated with observable weather or seasonal phenomena.
Methods: Trauma registry data and national weather service data for the period between September 1, 1992, and August 31, 1998, were combined into a common data set containing trauma admission data and weather data for each day. Sequential linear regression models were constructed to determine relationships between variables in the data set.
Results: There is a highly significant relationship (p < 0.00001) between maximum daily temperature and trauma admissions (R = 0.22). Rain is associated with a decrease in overall trauma volume. Rain had no effect on the number of admissions because of motor vehicle crash, however. Neither humidity nor snowfall affects trauma admission volume. Trauma admissions are significantly more frequent in July and August, and on Saturdays and Sundays (p < 0.05). Linear regression analysis identified maximum temperature, precipitation, day of week, and month as independent predictors of trauma admission volume (p < 0.001, R = 0.328).
Conclusion: There is a significant relationship between weather and trauma center activity; temperature and precipitation are independently associated with trauma admission volume at our institution. Statistical models of trauma incidence should consider these phenomena. Evaluation of a larger, population-based data set is needed to confirm these relationships.