«上一篇/Previous Article|本期目录/Table of Contents|下一篇/Next Article»

《现代预防医学》[ISSN:1003-8507/CN:51-1365/R]

期数:

2022年22期

页码:

4033-4038

栏目:

流行病与统计方法

出版日期:

2022-11-30

文章信息/Info

Title:

Spatial-temporal clustering of scarlet fever in China from 2014 to 2018

作者:

袁璐¹; 张元元¹; 房明²; 寇增强²; 王在翔¹

1.潍坊医学院公共卫生学院，山东 潍坊 261053；
2.山东省疾病预防控制中心，山东 济南 250014

Author(s):

YUAN Lu*;  ZHANG Yuan-yuan;  FANG Ming;  KOU Zeng-qiang;  WANG Zai-xiang

*Weifang Medical University, Weifang, Shandong 261053, China

关键词:

猩红热; 流行趋势; 空间自相关; 时空聚集

Keywords:

Scarlet fever;  Epidemic trend;  Spatial autocorrelation;  Spatial-temporal cluster

分类号:

R515.1；R181.3

DOI:

10.20043/j.cnki.MPM.202204512

文献标识码:

A

摘要:

目的 分析2014—2018年中国猩红热的时空聚集性特征，探讨猩红热高发聚集特征，为各省制定猩红热防控措施提供理论依据。方法 从公共卫生科学数据中心获取2014—2018年中国猩红热报告发病数据，采用空间自相关和时空扫描方法分析我国猩红热空间相关性和时空聚集性特征。结果 猩红热发病率全年呈双峰分布，以春末夏初（5—6月）、秋末冬初（11—12月）为流行的高峰季节；2014—2018年中国猩红热存在空间正相关性（Moran I＞0，P＜0.05），“热点”地区主要位于我国华北和东北地区；时空扫描分析结果显示，一类聚集区位于我国华北和华东地区，包括北京市、天津市、河北省和山东省, 聚集时间为2018年11—12月。结论 2014—2018年中国猩红热存在显著的空间正相关性和时空聚集性，应加强对猩红热重点防制区域的防控管理。

Abstract:

Objective To analyze the spatial and temporal clustering characteristics of scarlet fever in China from 2014 to 2018 and to explore the characteristics of its high incidence, so as to provide theoretical basis for the formulation of scarlet fever prevention and control measures in various provinces. Methods The reported incidence data of scarlet fever in China from 2014 to 2018 were collected from public Health Science Data Center, and spatial autocorrelation and spatio-temporal scanning methods were used to analyze spatial correlation and spatio-temporal clustering characteristics of scarlet fever in China. Results The incidence of scarlet fever showed a bimodal distribution throughout the year, with the peak seasons of late spring and early summer (May-June) and late autumn and early winter (November-December). There was a positive spatial correlation of scarlet fever in China from 2014 to 2018 (Moran’s I>0, P<0.05), and the “hot spots” were mainly located in North and Northeast China. Spatial-temporal clustering analysis showed that the first type of agglomerations was located at North and East China, including Beijing, Tianjin, Hebei, and Shandong provinces, and the agglomerations occurred from November to December of 2018. Conclusion There is a significant spatial positive correlation and temporal and spatial clustering of scarlet fever in China from 2014 to 2018. Prevention and control management of scarlet fever in key control areas should be strengthened.

参考文献/References

[ 1 ] 张漫，杨兴华，吴双胜，等．2010 - 2016年北京市猩红热流行状况及其危险因素分析[J]．疾病监测，2018，33（7）：580 - 584.Zhang M, Yang XH, Wu SS, et al. Epidemiological characteristics of scarlet fever and risk factors in Beijing,2010 - 2016[J]. Disease Surveillance, 2018, 33(7): 580 - 584.
[ 2 ] 赵子平，许可，吴莹，等．基于深度学习的猩红热流行趋势预测模型研究[J]．南京医科大学学报:自然科学版，2022，42（2）：252 - 257, 263．Zhao ZP, Xu K, Wu Y, et al. Studying prediction model of scarlet fever epidemics based on deep learning[J]. Journal of Biomedical Research, 2022, 42(2): 252 - 257, 263.
[ 3 ] 孔德川，蒋先进，邱琪，等．猩红热发病趋势、临床特征和病原学特征的研究进展[J]．中华传染病杂志，2022，40（3）：189 - 192．Kong DC, Jiang XJ, Qiu Q, et al. Progress in the study of scarlet fever incidence trend,clinical characteristics and etiological characteristics[J]. Chinese Journal of Infectious Diseases, 2022, 40(3): 189 - 192.
[ 4 ] 李芳，宋秋月，陈佳，等．基于贝叶斯时空模型的猩红热发病影响因素研究[J]．现代预防医学，2022，49（6）：963 - 968．Li F, Song QY, Chen J, et al. Influencing factors of scarlet fever based on Bayesian spatio - temporal model[J]. Modern Preventive Medicine, 2022, 49(6): 963 - 968.
[ 5 ] 何慧敏，杨莉，黄开勇．桂林市道路交通伤害的GIS空间分析[J]．中华疾病控制杂志，2012，16（11）：995 - 997．He HM, Yang L, Huang KY. GIS spatial analysis on road traffic injuries in Guilin City[J]. Chinese Journal of Disease Control & Prevention, 2012, 16(11): 995 - 997.
[ 6 ] 张淼，暴磊，王迪，等．中国2011—2015年甲乙类法定报告传染病发病趋势及空间关联性分析[J]．中国公共卫生，2018，34（6）：786 - 790．Zhang M, Bao L, Wang D, et al. Incidence trend and spatial correlation of class A and B notifiable infectious diseases in China: 2011 – 2015[J]. Chinese Journal of Public Health, 2018, 34(6): 786 - 790.
[ 7 ] 任香，陈梦想，赵锦，等．中国大陆梅毒时空分布以及流行因素空间异质性分析——基于MGWR模型[J]．现代预防医学，2022，49（3）：390 - 396, 416．Ren X, Chen MX, Zhao J, et al. Spatial and temporal distribution and spatial heterogeneity of epidemic factors of syphilis in mainland China based on MGWR model[J]. Modern Preventive Medicine, 2022, 49(3): 390 - 396, 416.
[ 8 ] Jung I, Kulldorff M, Klassen AC. A spatial scan statistic for ordinal data[J]. Statistics in Medicine, 2007, 26(7): 1594 - 1607.
[ 9 ] 周丽君，吕强，刘磊，等．2014年四川省猩红热病例流行病学特征分析[J]．寄生虫病与感染性疾病，2016，14（1）：23 - 25．Zhou LJ, Lv Q, Liu L, et al. Epidemiological characteristics of scarlet fever cases in Sichuan in 2014[J]. Parastoses and Infectious Diseases, 2016, 14(1): 23 - 25.
[ 10 ] 李小洲，王劲峰，杨维中，等．基于不同参数的空间扫描统计量方法在传染病暴发探测中的效果比较[J]．中国卫生统计，2014，31（2）：236 - 239．Li XZ, Wang JF, Yang WZ, et al. The effect comparison of spatial scan statistic method based on different parameters in early warning of infectious disease outbreak[J]. Chinese Journal of Health Statistics, 2014, 31(2): 236 - 239.
[ 11 ] 衣贵鹏，庞振陆，李望晨，等．2012 - 2019年山东省手足口病流行特征及空间聚集性分析[J]．现代预防医学，2021，48（15）：2710 - 2715．Yi GP, Pang ZL, Li WC, et al. Epidemiological characteristics and spatial clustering of hand - foot - mouth disease in Shandong, 2012 - 2019[J]. Modern Preventive Medicine, 2021, 48(15): 2710 - 2715.
[ 12 ] Ma Y, Yin F, Zhang T, et al. Selection of the maximum spatial cluster size of the spatial scan statistic by using the maximum clustering Set - Proportion statistic[J]. PLOS One, 2016, 11(1): e0147918.
[ 13 ] 仲伟麒，朱玉良，苏怡，等．黑龙江省2010～2020年猩红热发病与气象因素的关联性研究[J]．生物信息学，2021，19（4）：292 - 296．Zhong WQ, Zhu YL, Su Y, et al. Analysis of the association between scarlet fever and meteorological factors in Heilongjiang Province from 2010 to 2020[J]. China Journal of Bioinformation, 2021, 19(4): 292 - 296.
[ 14 ] 刘维量，寇增强，毕振旺，等．山东省2011 - 2018年猩红热流行特征及空间聚集性分析[J]．中华疾病控制杂志，2020，24（4）：438 - 443．Liu WL, Kou ZQ, Bi ZW, et al. Analysis on epidemiological characteristics and spatial clusters of scarlet fever in Shandong Province from 2011 to 2018[J]. Chinese Journal of Disease Control & Prevention, 2020, 24(4): 438 - 443.
[ 15 ] 蔡东杰，杨军，黄琳，等．广州市大气NO_(2)污染对暴露人群不同疾病死亡的影响[J]．中国环境科学，2022，42（8）：3950 - 3956．Cai DJ, Yang J, Huang L, et al. The impact of NO2 on cause - specific mortality in Guangzhou[J]. China Environmental Science, 42(8): 3950 - 3956.
[ 16 ] 罗朝晨，翁顺太，谢芳钦，等．2011～2020年福建省猩红热流行特征分析[J]．预防医学论坛，2021，27（10）：784 - 786, 790．Luo CC, Weng ST, Xie FQ, et al. Analysis on epidemiological characteristics of scarlet fever, Fujian province,2011 - 2020[J]. Preventive Medicine Tribune, 2021, 27(10): 784 - 786, 790.
[ 17 ] 许峰，莫平华，宋灿磊，等．2011—2019年上海市金山区猩红热流行病学特征分析[J]．华南预防医学，2022，48（1）：58 - 60.Xu F, Mo PH, Song CL, et al. Epidemiological characteristics of scarlet fever in Jinshan District of Shanghai from 2011 to 2019[J]. South China Journal of Preventive Medicine, 2022, 48(1): 58 - 60.
[ 18 ] 王延，魏志胜，赖彩云，等．2011 - 2017年北京市海淀区猩红热流行病学特征分析[J]．现代预防医学，2019，46（16）：2895 - 2898．Wang Y, Wei ZS, Lai CY, et al. Epidemiological characteristics of scarlet fever in Haidian district of Beijing from 2011 to 2017[J]. Modern Preventive Medicine, 2019, 46(16): 2895 - 2898.

备注/Memo

备注/Memo:

基金项目：国家科技重大专项（2018ZX10713003-002）；山东省医药卫生科技发展计划项目（2019WS433）；山东省自然科学基金青年项目（ZR2020QH298）；山东省社会科学规划研究项目（21DSHJ10）
作者简介：袁璐（1996—），女，硕士在读，研究方向：公共卫生
通信作者：王在翔，E-mail: WANGZX1@126.com

常用功能

更新日期/Last Update: 2022-11-30