de Weger Letty A, Beerthuizen Thijs, Hiemstra Pieter S, Sont Jacob K
Department of Pulmonology, Leiden University Medical Center, PO Box 9600, Albinusdreef 2, 2300RC, Leiden, The Netherlands,
Int J Biometeorol. 2014 Aug;58(6):1047-55. doi: 10.1007/s00484-013-0692-5. Epub 2013 Jun 20.
One-third of the Dutch population suffers from allergic rhinitis, including hay fever. In this study, a 5-day-ahead hay fever forecast was developed and validated for grass pollen allergic patients in the Netherlands. Using multiple regression analysis, a two-step pollen and hay fever symptom prediction model was developed using actual and forecasted weather parameters, grass pollen data and patient symptom diaries. Therefore, 80 patients with a grass pollen allergy rated the severity of their hay fever symptoms during the grass pollen season in 2007 and 2008. First, a grass pollen forecast model was developed using the following predictors: (1) daily means of grass pollen counts of the previous 10 years; (2) grass pollen counts of the previous 2-week period of the current year; and (3) maximum, minimum and mean temperature (R (2)=0.76). The second modeling step concerned the forecasting of hay fever symptom severity and included the following predictors: (1) forecasted grass pollen counts; (2) day number of the year; (3) moving average of the grass pollen counts of the previous 2 week-periods; and (4) maximum and mean temperatures (R (2)=0.81). Since the daily hay fever forecast is reported in three categories (low-, medium- and high symptom risk), we assessed the agreement between the observed and the 1- to 5-day-ahead predicted risk categories by kappa, which ranged from 65 % to 77 %. These results indicate that a model based on forecasted temperature and grass pollen counts performs well in predicting symptoms of hay fever up to 5 days ahead.
三分之一的荷兰人口患有过敏性鼻炎，包括花粉热。在本研究中，针对荷兰的草花粉过敏患者开发并验证了提前5天的花粉热预测模型。使用多元回归分析，利用实际和预测的天气参数、草花粉数据以及患者症状日记，开发了一个两步花粉和花粉热症状预测模型。因此，80名草花粉过敏患者对他们在2007年和2008年草花粉季节的花粉热症状严重程度进行了评分。首先，使用以下预测因子开发了一个草花粉预测模型：（1）前10年草花粉计数的每日平均值；（2）当年前两周的草花粉计数；（3）最高、最低和平均温度（R² = 0.76）。第二个建模步骤涉及花粉热症状严重程度的预测，包括以下预测因子：（1）预测的草花粉计数；（2）一年中的天数；（3）前两个为期两周时间段的草花粉计数移动平均值；（4）最高和平均温度（R² = 0.81）。由于每日花粉热预测分为三类（低、中、高症状风险），我们通过kappa评估了观察到的与提前1至5天预测的风险类别之间的一致性，kappa范围为65%至77%。这些结果表明，基于预测温度和草花粉计数的模型在提前5天预测花粉热症状方面表现良好。
