A model to predict the incidence of allergic rhinitis based on meteorological factors.
A model to predict the incidence of allergic rhinitis based on meteorological factors.
复制标题
基于气象因素的过敏性鼻炎发病预测模型
DOI：
10.1038/s41598-017-10721-3
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发表时间：
2017-08-30
影响因子：
4.6
通讯作者：
Zhang L
中科院分区：
文献类型：
--
作者：
Ouyang Y;Li J;Zhang D;Fan E;Li Y;Zhang L
气象因素已被证明会影响吸入过敏原的生理、分布和数量。本研究的目的是建立一个模型来预测变应性鼻炎（AR）患者的发病趋势。共评估了10，914例连续AR门诊患者在4年内的每日患者访视次数。利用气象数据，通过时间序列数据和回归分析来评估气象因素与AR发病率的关系。在花粉、尘螨和霉菌敏感人群中建立AR发病率的预测模型，并使用均方根误差（RMSE）估计气象因素对AR发病率的预测性能。花粉，尘螨和霉菌敏感AR患者的发病率分别与最低温度，蒸汽压和海平面压力显着相关。综合气象参数与AR发病率的相关性高于单项气象参数与AR发病率的相关性。CMP在预测AR患者的发病率方面比单个气象参数具有更高的性能。这些研究结果表明，花粉，尘螨和霉菌敏感AR的发病率可以预测采用基于流行的气象条件的模型。
Meteorological factors have been shown to affect the physiology, distribution, and amounts of inhaled allergens. The aim of this study was to develop a model to predict the trends for onset of allergic rhinitis (AR) patients. A total of 10,914 consecutive AR outpatients were assessed for the number of daily patient visits over a period of 4 years. Meteorological data were used to assess the relationship between meteorological factors and AR incidence by time-series data and regression analysis. Predictive models for incidence of AR were established in pollen-, dust mite- and mould-sensitive groups of patients, and the predictive performances of meteorological factors on the incidence of AR were estimated using root mean squared errors (RMSEs). The incidence of pollen-, dust mites- and mould-sensitive AR patients was significantly correlated with minimum temperature, vapour pressure, and sea-level pressure, respectively. The correlation between comprehensive meteorological parametric (CMP) and incidence of AR was higher than the correlation between the individual meteorological parameters and AR incidence. CMP had higher performance than individual meteorological parameters for predicting the incidence of AR patients. These findings suggest that the incidence of pollen-, dust mites- and mould-sensitive AR can be predicted employing models based on prevailing meteorological conditions.
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影响因子：
0.9
作者：
Crispen KL;Gillespie DN;Weiler EC;Noonan CW;Hamilton RF;Ward TJ
通讯作者：
Ward TJ
影响因子：
14.2
作者：
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通讯作者：
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作者：
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通讯作者：
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