Røislien Jo, Søvik Signe, Eken Torsten
Faculty of Health Sciences, University of Stavanger, Stavanger, Norway.
Department of Research, Norwegian Air Ambulance Foundation, Drøbak, Norway.
PLoS One. 2018 Feb 9;13(2):e0192568. doi: 10.1371/journal.pone.0192568. eCollection 2018.
Trauma is a leading global cause of death, and predicting the burden of trauma admissions is vital for good planning of trauma care. Seasonality in trauma admissions has been found in several studies. Seasonal fluctuations in daylight hours, temperature and weather affect social and cultural practices but also individual neuroendocrine rhythms that may ultimately modify behaviour and potentially predispose to trauma. The aim of the present study was to explore to what extent the observed seasonality in daily trauma admissions could be explained by changes in daylight and weather variables throughout the year.
Retrospective registry study on trauma admissions in the 10-year period 2001-2010 at Oslo University Hospital, Ullevål, Norway, where the amount of daylight varies from less than 6 hours to almost 19 hours per day throughout the year. Daily number of admissions was analysed by fitting non-linear Poisson time series regression models, simultaneously adjusting for several layers of temporal patterns, including a non-linear long-term trend and both seasonal and weekly cyclic effects. Five daylight and weather variables were explored, including hours of daylight and amount of precipitation. Models were compared using Akaike's Information Criterion (AIC).
A regression model including daylight and weather variables significantly outperformed a traditional seasonality model in terms of AIC. A cyclic week effect was significant in all models.
Daylight and weather variables are better predictors of seasonality in daily trauma admissions than mere information on day-of-year.
创伤是全球主要的死亡原因之一，预测创伤入院负担对于创伤护理的良好规划至关重要。多项研究发现创伤入院存在季节性。日照时长、温度和天气的季节性波动不仅会影响社会和文化习俗，还会影响个体的神经内分泌节律，最终可能改变行为并增加创伤易感性。本研究的目的是探讨全年日照和天气变量的变化在多大程度上可以解释每日创伤入院中观察到的季节性。
对2001年至2010年期间挪威奥斯陆大学医院乌勒瓦尔分院10年的创伤入院情况进行回顾性登记研究，该地区全年日照时长从每天不到6小时到近19小时不等。通过拟合非线性泊松时间序列回归模型分析每日入院人数，同时调整多层时间模式，包括非线性长期趋势以及季节性和每周周期性效应。研究了五个日照和天气变量，包括日照时长和降水量。使用赤池信息准则（AIC）比较模型。
就AIC而言，包含日照和天气变量的回归模型明显优于传统的季节性模型。所有模型中每周周期性效应均显著。
对于每日创伤入院的季节性，日照和天气变量比仅关于一年中的日期的信息是更好的预测指标。
