秦皇岛气象因素对儿童下呼吸道疾病就诊人数影响及预测研究

Study of the Influence of Meteorological Condition on Children Lower Respiratory Tract Infection and the Prediction Model in Qinhuangdao

呼吸系统疾病对儿童的身体健康有极大影响,其发生与气象条件有密切关系。为探讨秦皇岛地区气象条件对儿童下呼吸道疾病的影响,预测就诊人数,为医疗气象服务提供新方法,利用秦皇岛地区2015-2016年儿童下呼吸道疾病就诊人数资料和同期气象资料,分别使用逐步回归分析和BP人工神经网络建立儿童下呼吸道疾病就诊人数预测模型,并对预测效果进行评价。结果表明,气象条件对儿童下呼吸道疾病的发生有显著影响,特别是阶段性天气变化与气候异常对就诊人数影响较大。就诊人数与气温及平均相对湿度呈负相关关系,与气压、风速及前72 h气温变幅呈正相关关系,与气温相关性最好,与气压、平均相对湿度相关性次之。逐步回归法与BP人工神经网络模型的预测准确率分别为72.75%、76.30%。2种预测模型中,BP人工神经网络模型的整体表现更为出色。

Respiratory diseases greatly affected children’s health, and its occurrence was related to meteorological conditions closely. In order to analyze the effects of meteorological conditions on children’s lower respiratory diseases in Qinhuangdao, predict the number of patients and provide new method for medical meteorological service, the data of children with lower respiratory diseases from 2015 to 2016 and the meteorological data within the same time were used, prediction models were established by stepwise regression analysis and BP artificial neutral network separately, and the prediction effects were evaluated. The results show that the meteorological conditions, especially the staged weather changes and climate anomalies, had a significant effect on the patients’ number with these diseases. The number of patients was negatively correlated with air temperature and average relative humidity, and positively correlated with air pressure, wind speed and temperature fluctuation in 72 hours, and good correlation was showed between patient number and air temperature, followed by air pressure and average relative humidity. The prediction accuracy of the stepwise regression model and BP artificial neural network model was 72.75% and 76.30%, respectively. Between the two models established, the overall performance of BP artificial neural network model was better.