您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（医学版）》

山东大学学报 (医学版) ›› 2024, Vol. 62 ›› Issue (6): 96-101.doi: 10.6040/j.issn.1671-7554.0.2024.0082

• 公共卫生与管理学 • 上一篇    

基于MGWR模型的西安手足口病发病影响因素

梁珂梦1,李树芬1,倪志松1,宋思豪1,席睿1,程传龙1,左慧1,段雨琪1,刘昆2,白尧3,李秀君1   

  • 发布日期:2024-07-15
  • 通讯作者: 李秀君. E-mai:xjli@sdu.edu.cn白尧. E-mail:baiyaocdc@163.com
  • 基金资助:
    国家重点研发计划(2023YFC2604400)

Influencing factors on the incidence of hand, foot and mouth disease in Xian based on MGWR model

LIANG Kemeng1, LI Shufen1, NI Zhisong1, SONG Sihao1, XI Rui1, CHENG Chuanlong1, ZUO Hui1, DUAN Yuqi1, LIU Kun2, BAI Yao3, LI Xiujun1   

  1. 1. Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China;
    2. Department of Epidemiology, School of Military Prerention Medicine, Air Force Medical University, Xian 710032, Shaanxi, China;
    3. Department of Infection Disease Control and Prevention, Xian Center for Disease Prevention and Control, Xian 710054, Shaanxi, China
  • Published:2024-07-15

PDF (PC)

205

摘要： 目的 在5 km×5 km的空间网格尺度上探讨西安市手足口病发病与环境、社会经济因素等的关系,为区域防控措施的制定提供依据。 方法 收集2019年西安市手足口病报告发病率数据,应用空间自相关分析手足口病空间分布特征;基于多尺度地理加权回归(multiscale geographically weighted regression, MGWR)模型分析环境与社会经济因素对手足口病发病的影响,并与普通最小二乘(ordinary least squares, OLS)回归模型以及地理加权回归(geographically weighted regression, GWR)模型结果进行对比。 结果 2019年西安市手足口病年报告发病率为157.99/10万,在空间分布上存在正相关性(全局Morans I=0.349,P<0.001)。MGWR模型拟合度优于GWR模型和OLS模型(MGWR:R2=0.530; GWR:R2=0.473; OLS:R2=0.327)。各影响因素的作用尺度存在一定差异,GDP、土地城镇化水平、平均气温等作用尺度较大,归一化植被指数(normalized difference vegetation index, NDVI)作用尺度较小。GDP与手足口病报告发病率呈显著负相关,土地城镇化水平、平均气温与报告发病率呈显著正相关,NDVI在西安部分地区对手足口发病有显著负向影响。 结论 环境与社会经济因素对手足口病发病有显著影响,且各影响因素的作用存在空间差异,研究结果可为不同地区制定针对性的预防措施提供依据。

关键词: 手足口病, 空间自相关, 多尺度地理加权回归, 地理加权回归, 西安

Abstract: Objective To investigate the relationship between the incidence of hand, foot and mouth disease(HFMD)and factors related to environment and socioeconomic in Xian at a spatial grid scale of 5 km, and provide a basis for the development of regional control and prevention measures. Methods The 2019 HFMD report data in Xian were collected and analyzed by spatial autocorrelation to characterize the spatial distribution. The role of natural environmental factors and socioeconomic factors on the incidence of HFMD was analyzed based on a multiscale geographically weighted regression(MGWR)model and compared with those of the ordinary least square(OLS)regression model and the geographically weighted regression(GWR)model. Results The annual reported incidence rate of HFMD in Xian in 2019 was 157.99/100,000, with a positive correlation in spatial distribution(global Morans I=0.349, P<0.001). The MGWR model fit was better than the GWR model and the OLS model(MGWR: R2=0.530; GWR: R2=0.473; OLS: R2=0.327). On the scale of influence, the GDP, land urbanization level, and average temperature had larger scale effects, while normalized difference vegetation index(NDVI)had smaller scale influence. GDP was significantly negatively correlated with the reported incidence of HFMD, land urbanization level and average temperature were significantly positively correlated with the reported incidence, and NDVI had a significant negative effect on HFMD incidence in parts of Xian. Conclusion The influence of environmental and socioeconomic factors on the incidence of HFMD is significant and there are spatial differences in the role of each influencing factor. The results are helpful for the formulation of regional prevention and control strategies for HFMD.

Key words: Hand, foot and mouth disease, Spatial autocorrelation, Multi-scale geographically weighted regression, Geographically weighted regression, Xian

中图分类号: 

  • R181.3
[1] 郭艳立. 手足口病的流行病学及病原学特征研究进展[J]. 医疗装备, 2020, 33(10): 201-203. GUO Yanli. Research progress on epidemiology and pathogenic characteristics of hand, foot and mouth disease[J]. Medical Equipment, 2020, 33(10): 201-203.
[2] 李颉, 郑步云, 王劲峰. 2008—2018年中国手足口病时空分异特征 [J].地球信息科学学报, 2021, 23(3): 419-430. LI Jie, ZHENG Buyun, WANG Jinfeng. Spatial-temporal Heterogeneity of Hand, Foot and Mouth Disease in China from 2008 to 2018 [J]. Journal of Geo-information Science, 2021, 23(3): 419-430.
[3] 王小莉, 魏洪鑫, 贾蕾, 等. 我国手足口病经济负担研究概况[J]. 中华流行病学杂志, 2020, 41(2): 273-279. WANG Xiaoli, WEI Hongxin, JIA Lei, et al. Summary of research in economic burden of hand, foot, and mouth disease in China[J]. Chinese Journal of Epidemiology, 2020, 41(2): 273-279.
[4] 白尧, 王戬, 杨凡, 等. 2014—2019年西安市手足口病聚集性疫情流行特征分析[J]. 预防医学情报杂志, 2020, 36(12): 1566-1571. BAI Yao, WANG Jian, YANG Fan, et al. Analysis on the epidemiological characteristics of clustered cases of hand-foot-mouth disease in Xian from 2014 to 2019[J]. Journal of Preventive Medicine Information, 2020, 36(12): 1566-1571.
[5] 别芹芹, 邱冬生, 胡辉, 等. 我国手足口病时空分布特征的GIS分析[J]. 地球信息科学学报, 2010, 12(3): 380-384. BIE Qinqin, QIU Dongsheng, HU Hui, et al. Spatial and temporal distribution characteristics of hand-foot-mouth disease in China[J]. Journal of Geo-Information Science, 2010, 12(3): 380-384.
[6] 陈昕, 谢玲, 刘素红, 等. 广西手足口病时空分异及其地理环境因子探测分析[J]. 世界地理研究, 2022, 31(5): 1108-1118. CHEN Xin, XIE Ling, LIU Suhong, et al. Spatio-temporal difference and geographical environment factors of hand, foot and mouth disease in Guangxi[J]. World Regional Studies, 2022, 31(5): 1108-1118.
[7] 龚胜生, 王无为, 陈红缨, 等. 湖北省手足口病流行的地理特征及其影响因子[J].地理科学, 2020, 40(6): 999-1009. GONG Shengsheng, WANG Wuwei, CHEN Hongying, et al. Geographical characteristics and influencing factors of the prevalence of hand, foot and mouth disease in Hubei Province[J]. Scientia Geographica Sinica, 2020, 40(6): 999-1009.
[8] 王雅婷, 朋文佳, 苏华林, 等. 2011—2018年中国手足口病发病的时空特征及影响因素研究[J]. 中华流行病学杂志, 2022, 43(10): 1562-1567. WANG Yating, PENG Wenjia, SU Hualin, et al. Spatiotemporal characteristics of hand, foot and mouth disease and influencing factors in China from 2011 to 2018[J]. Chinese Journal of Epidemiology, 2022, 43(10): 1562-1567.
[9] Brunsdon C, Fotheringham AS, Charlton ME. Geographically weighted regression: a method for exploring spatial nonstationarity[J]. Geogr Anal, 1996, 28(4): 281-298.
[10] Fotheringham AS, Oshan TM, Li ZQ. Multiscale Geographically Weighted Regression[M]. Boca Raton: CRC Press, 2023. doi: 10.1201/9781003435464.
[11] 沈体雁, 于瀚辰, 周麟, 等. 北京市二手住宅价格影响机制: 基于多尺度地理加权回归模型(MGWR)的研究[J]. 经济地理, 2020, 40(3): 75-83. SHEN Tiyan, YU Hanchen, ZHOU Lin, et al. On hedonic price of second-hand houses in Beijing based on multi-scale geographically weighted regression: scale law of spatial heterogeneity[J]. Economic Geography, 2020, 40(3): 75-83.
[12] 黄颙昊, 杨新苗, 岳锦涛. 基于多尺度地理加权回归模型的城市道路骑行流量分析[J]. 清华大学学报(自然科学版), 2022, 62(7): 1132-1141. HUANG Yonghao, YANG Xinmiao, YUE Jintao. Urban street bicycle flow analysis based on multi-scale geographically weighted regression model[J]. Journal of Tsinghua University(Science and Technology), 2022, 62(7): 1132-1141.
[13] Lotfata A. Using geographically weighted models to explore obesity prevalence association with air temperature, socioeconomic factors, and unhealthy behavior in the USA[J]. J Geovisualization Spatial Anal, 2022, 6(1): 14.
[14] Oshan TM, Smith JP, Fotheringham AS. Targeting the spatial context of obesity determinants via multiscale geographically weighted regression[J]. Int J Health Geogr, 2020, 19(1): 11.
[15] Xu CD. Spatio-temporal pattern and risk factor analysis of hand, foot and mouth disease associated with under-five morbidity in the beijing-Tianjin-hebei region of China[J]. Int J Environ Res Public Health, 2017, 14(4): 416.
[16] Hu BS, Qiu WQ, Xu CD, et al. Integration of a Kalman filter in the geographically weighted regression for modeling the transmission of hand, foot and mouth disease[J]. BMC Public Health, 2020, 20(1): 479.
[17] Ren HY, Zheng L, Li QX, et al. Exploring determinants of spatial variations in the dengue fever epidemic using geographically weighted regression model: a case study in the joint Guangzhou-foshan area, China, 2014[J]. Int J Environ Res Public Health, 2017, 14(12): 1518.
[18] Yang J, Huang X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019[J]. Earth Syst Sci Data, 2021, 13(8): 3907-3925.
[19] Marshall RJ. Mapping disease and mortality rates using empirical Bayes estimators[J]. J R Stat Soc Ser C Appl Stat, 1991, 40(2): 283-294.
[20] 林静静, 张铁威, 李秀央. 疾病时空聚集分析的研究与进展[J]. 中华流行病学杂志, 2020, 41(7): 1165-1170. LIN Jingjing, ZHANG Tiewei, LI Xiuyang. Research progress on spatiotemporal clustering of disease[J]. Chinese Journal of Epidemiology, 2020, 41(7): 1165-1170.
[21] 张雷雨, 杨毅, 梁霄. 地理加权回归模型的多重共线性诊断方法[J]. 测绘与空间地理信息, 2017, 40(10): 28-31. ZHANG Leiyu, YANG Yi, LIANG Xiao. The diagnostic approach of multicollinearity in geographically weighted regression model[J]. Geomatics & Spatial Information Technology, 2017, 40(10): 28-31.
[22] Wolf LJ, Oshan TM, Fotheringham AS. Single and multiscale models of process spatial heterogeneity[J]. Geogr Anal, 2018, 50(3): 223-246.
[23] He XY, Dong SJ, Li LP, et al. Using a Bayesian spatiotemporal model to identify the influencing factors and high-risk areas of hand, foot and mouth disease(HFMD)in Shenzhen[J]. PLoS Negl Trop Dis, 2020, 14(3): e0008085. doi:10.1371/journal.pntd.0008085.
[24] Tong MX, Hansen A, Hanson-Easey S, et al. Infectious diseases, urbanization and climate change: challenges in future China[J]. Int J Environ Res Public Health, 2015, 12(9): 11025-11036.
[25] 丘文洋, 李连发, 张杰昊, 等. 利用空间聚集的贝叶斯网络评估手足口病发病风险[J]. 地球信息科学学报, 2017, 19(8): 1036-1048. QIU Wenyang, LI Lianfa, ZHANG Jiehao, et al. A Bayesian network method considering spatial cluster to evaluate health risk of hand, foot and mouth disease[J]. Journal of Geo-Information Science, 2017, 19(8): 1036-1048.
[26] Li CH, Mao JJ, Wu YJ, et al. Combined impacts of environmental and socioeconomic covariates on HFMD risk in China: a spatiotemporal heterogeneous perspective[J]. PLoS Negl Trop Dis, 2023, 17(5): e0011286. doi:10.1371/journal.pntd.0011286.
[27] Luan GJ, Liu SN, Zhang WY, et al. Estimating the influence of high temperature on hand, foot, and mouth disease incidence in China[J]. Environ Sci Pollut Res Int, 2023, 30(1): 1477-1484.
[28] Cao CX, Li GH, Zheng S, et al. Research on the environmental impact factors of Hand-Foot-Mouth Disease in Shenzhen, China using RS and GIS technologies[C] //IEEE. 2012 IEEE International Geoscience and Remote Sensing Symposium. Munich: IEEE, 2012: 7240-7243. doi:10.1109/IGARSS.2012.6351991.
[29] 罗晓风, 湛柳华, 周文, 等. 2010—2011年广州市越秀区手足口病发例数与气象因素和空气污染指数的相关性分析[J]. 中国药物经济学, 2013, 8(S3): 182-184. LUO Xiaofeng, ZHAN Liuhua, ZHOU Wen, et al. Correlation analysis between the number of cases of hand-foot-mouth disease and meteorological factors and air pollution index in Yuexiu District of Guangzhou from 2010 to 2011[J]. China Journal of Pharmaceutical Economics, 2013, 8(S3): 182-184.
[30] 梁兆毅, 孟君, 张艳炜, 等. 深圳市2008—2020年手足口病流行特征及EV71疫苗接种对其发病率影响 [J].中国公共卫生, 2023, 39(2): 249-252. LIANG Zhaoyi, MENG Jun, ZHANG Yanwei, et al. Epidemiological characteristics of hand,foot and mouth disease in Shenzhen from 2008 to 2020 and effect of inactivated EV71 vaccine on disease incidence [J]. Chin J Public Health, 2023, 39(2): 249-252.
[31] 吴晓娜, 孙瑛, 贾蕾, 等. 室内环境因素和手足口病暴发的相关性研究[J].中华疾病控制杂志, 2012, 16(3): 234-236. WU Xiaona, SUN Ying, JIA Lei, et al. Study on the relationship between indoor environment and outbreaks of hand-foot-mouth disease[J]. Chinese Journal of Disease Control & Prevention, 2012, 16(3): 234-236.
[1] 王珊,焦伯延,冯强,段延华,温红玲. 2021年济宁市柯萨奇病毒A16型流行特点及全基因组特征[J]. 山东大学学报 (医学版), 2023, 61(5): 90-95.
[2] 程传龙,韩闯,房启迪,刘盈,杨淑霞,崔峰,刘靖靖,李秀君. 基于时空地理加权回归模型探索肺癌发病的环境影响因素[J]. 山东大学学报 (医学版), 2023, 61(4): 95-102.
[3] 程传龙,杨淑霞,佘凯丽,房启迪,韩闯,刘盈,崔峰,李秀君. 淄博市2018年恶性肿瘤的流行特征及影响因素[J]. 山东大学学报 (医学版), 2022, 60(2): 102-108.
[4] 冯一平,孙大鹏,王显军,纪伊曼,刘云霞. DLNM和LSTM神经网络对临沂市手足口病发病的预测效果比较[J]. 山东大学学报 (医学版), 2022, 60(2): 96-101.
[5] 房启迪,杨淑霞,齐畅,程传龙,韩闯,刘盈,崔峰,李秀君. 基于镇街尺度的淄博市2019年脑卒中时空分布[J]. 山东大学学报 (医学版), 2022, 60(2): 81-88.
[6] 关恒云,王春荣,刘岚铮,赵怀龙,白爱英,姜慧钰,董华,李娜,单晓英,成洪旗. 济南市2012~2017年手足口病4种优势肠道病毒阳性及VP1基因型分布[J]. 山东大学学报 (医学版), 2021, 59(5): 60-67.
[7] 贾艳,李春雨,刘利利,佘凯丽,刘廷轩,朱雨辰,齐畅,张丹丹,王旭,陈恩富,李秀君. 浙江省新型冠状病毒肺炎的流行特征与空间分析[J]. 山东大学学报 (医学版), 2020, 58(10): 66-73.
[8] 关恒云,王春荣,刘岚铮,杨国樑,赵怀龙,孙洁,成洪旗. 济南市2012~2017年手足口病肠道病毒谱变化及流行特征[J]. 山东大学学报 (医学版), 2019, 57(11): 90-97.
[9] 荀换苗,胡文琦,刘羿聪,马伟. 2009~2013年广东省热带气旋对手足口病的影响[J]. 山东大学学报 (医学版), 2018, 56(8): 50-55.
[10] 刘言玉,刘志东,劳家辉,张静,姜宝法. 南宁市2008~2011年气象因素对手足口病的影响及其交互作用[J]. 山东大学学报 (医学版), 2018, 56(8): 95-100.
[11] 王纪传,刘瑞红,李东芝,薛付忠. 地理加权回归在脑卒中病因探索中的应用[J]. 山东大学学报（医学版）, 2017, 55(8): 88-94.
[12] 邓轲,刘毅,盖中涛. 间充质干细胞对肠道病毒71型的易感性及miRNA的差异表达[J]. 山东大学学报（医学版）, 2017, 55(3): 25-31.
[13] 刘红杨,肖金环,王凡,杨光,刘洪庆. 集中度和圆形分布法分析山东省2008~2014年手足口病季节性特征[J]. 山东大学学报（医学版）, 2016, 54(12): 77-81.
[14] 王浩, 张娜, 薛付忠, 王萍, 康殿民, 刘言训. 山东省艾滋病病毒感染者及艾滋病患者空间流行特征[J]. 山东大学学报（医学版）, 2015, 53(2): 81-86.
[15] 李敏, 段坤, 赵瑞霞. 2010-2013年济南市历城区414例手足口病病例分析[J]. 山东大学学报（医学版）, 2014, 52(9): 54-57.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2018 《山东大学学报(医学版)》编辑部　
地址:山东大学科技期刊社（济南市历城区山大南路27号山东大学中心校区明德楼B座721室） 电话: 0531-88366918 E-mail: xbyxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发