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
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摘要
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.
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.
引文
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[18] 付桂琴,贾小卫,刘华悦,等.河北石家庄地区气温对儿童哮喘病就诊人数的影响[J].干旱气象,2017,35(1):122-127.
[19] 王霞,车颖悟.1970—1990年黑龙江省气象因素与小儿肺炎[J].哈尔滨医科大学学报,1994(6):489-491.
[20] 刘新超,郭洁,陈婷,等.儿童呼吸系统疾病与气象要素的关系及其预测[J].高原山地气象研究,2015,35(1):92-96.
[21] 程一帆,张莹,王式功,等.人工神经网络在呼吸系统疾病急诊就诊人数预报中的应用[J].兰州大学学报(自然科学版),2014,50(1):75-79.
[22] 范佳妮,王振雷,钱锋.BP人工神经网络隐层结构设计的研究进展[J].控制工程,2005,12(10):105-109.
[23] 屈芳.环境气象因素对呼吸系统疾病影响的研究进展[J].气象科技进展,2013(6):35-44.
[24] 岳海燕,申双和.呼吸道和心脑血管疾病与气象条件关系的研究进展[J].气象与环境学报,2009,25(2):57-61.
[25] 刘新超,郭洁,申辉,等.成都市大气污染对儿童呼吸系统疾病的影响[J].高原山地气象研究,2015,35(3):84-87.
[2] 李乐,艾荣,蒋加磊.儿童重症肺炎支原体肺炎治疗新进展[J].医学综述,2016,22(5):943-946.
[3] FALAGAS M E,THEOCHARIS G,SPANOS A,et al.Effect of meteorological variables on the incidence of respiratory tract infections[J].Respiratory Medicine,2008,102(5):733-737.
[4] 夏丽花,刘铭,陈德花,等.福州市呼吸道疾病发生的气象条件分析及预报[J].气象科技,2003,31(6):385-388.
[5] 李青春,陆晨,刘彦,等.北京地区呼吸道疾病与气象条件关系的分析[J].气象,1999,25(3):8-12.
[6] 张书余,张夏琨,谢静芳,等.白山市感冒与气象条件的关系分析和预报[J].气象,2012(6):740-744.
[7] 李芳,蔡鹏,赵京峰,等.上呼吸道感染的气象条件分析及预报[J].气象科技,2017,45(5):925-929.
[8] 李娟,张志薇,于庚康,等.气象要素对南京市呼吸系统疾病的影响研究[J].气象科学,2017,37(3):409-415.
[9] 张智,崔巍.银川市腮腺炎异常流行与大气环流的关系研究[J].干旱气象,2017,35(1):116-121.
[10] 张智,雷静,蒋少萍.银川市腮腺炎流行特征及其与气象因素的关系[J].干旱气象,2015,33(1):162-166.
[11] 马守存,张书余,王宝鉴,等.气象条件对心脑血管疾病的影响研究进展[J].干旱气象,2011,29(3):350-354.
[12] 张晓云,王德征,蔡子颖,等.天津市气象要素和大气污染物对呼吸道疾病影响分析[C]//.中国气象学会.第32届中国气象学会年会气候环境变化与人体健康,2015.
[13] 霍爱梅,赵达生,方立群,等.华北地区主要呼吸道传染病与气象条件的关系[J].中国医药导报,2011,32(8):153-156.
[14] 施海龙,曲波,郭海强,等.干旱地区呼吸道传染病气象因素及发病预测[J].中国公共卫生,2006,22(4):417-418.
[15] 马盼,黎檀实,宁贵财,等.北京市上呼吸道感染与气象环境关系及其冬季天气分型初探[J].兰州大学学报(自然科学版),2015,51(1):79-86.
[16] 翟广宇,王式功,董继元,等.兰州市上呼吸道疾病与气象条件和空气质量的关联规则分析[J].兰州大学学报(自然科学版),2014,50(1):66-70.
[17] 张德山,刘燕,丁德平,等.京津地区儿童呼吸系统疾病医疗气象预报初步研究[J].气候与环境研究,2007,12(6):804-810.
[18] 付桂琴,贾小卫,刘华悦,等.河北石家庄地区气温对儿童哮喘病就诊人数的影响[J].干旱气象,2017,35(1):122-127.
[19] 王霞,车颖悟.1970—1990年黑龙江省气象因素与小儿肺炎[J].哈尔滨医科大学学报,1994(6):489-491.
[20] 刘新超,郭洁,陈婷,等.儿童呼吸系统疾病与气象要素的关系及其预测[J].高原山地气象研究,2015,35(1):92-96.
[21] 程一帆,张莹,王式功,等.人工神经网络在呼吸系统疾病急诊就诊人数预报中的应用[J].兰州大学学报(自然科学版),2014,50(1):75-79.
[22] 范佳妮,王振雷,钱锋.BP人工神经网络隐层结构设计的研究进展[J].控制工程,2005,12(10):105-109.
[23] 屈芳.环境气象因素对呼吸系统疾病影响的研究进展[J].气象科技进展,2013(6):35-44.
[24] 岳海燕,申双和.呼吸道和心脑血管疾病与气象条件关系的研究进展[J].气象与环境学报,2009,25(2):57-61.
[25] 刘新超,郭洁,申辉,等.成都市大气污染对儿童呼吸系统疾病的影响[J].高原山地气象研究,2015,35(3):84-87.
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