Abstract:Objective To analyze the epidemiological and spatial distribution characteristics of scarlet fever cases in Kaifeng City, Henan Province from 2012 to 2022 so as to provide a basis for scarlet fever prevention and control in Kaifeng City. Methods We descriptively analyzed the time, population and region of the reported cases of scarlet fever in Kaifeng City of Henan Province. ArcGIS 10.6 software was used to draw the statistical map of incidence of scarlet fever and perform the spatial autocorrelation analysis. Results A total of 678 cases of scarlet fever were reported in Kaifeng City, Henan Province during 2012-2022, with an average annual incidence rate of 1.56/100,000. The incidence rates showed a trend of first increasing and then decreasing (χ2=859.33, P<0.001). There was no statistically significant difference in the incidence rate between males and females (χ2=2.01, P>0.05). The patients' ages were mainly less than 25 years old (n=667, 98.4%), and mainly concentrated at the group aged 10-15 years (n=374, 55.2%). The incidence rate was found to be the highest in the group aged 10-15 years, and most of them were students (52.5%). The incidence rates in 0-13 years old showed an upward trend (χ2trend=6,503.22, P<0.001), but a downward trend with the increase of age (χ2trend=6,965.24, P<0.001). Time trends in the incidence of scarlet fever showed a bimodal distribution, the fourth quarter of each year was the main peak incidence, with the incidence rate being 5.55/100,000 (32.4%), and the second quarter was secondary peak, with the incidence rate of 4.44/100,000 (26.0%). The average annual incidence rate in the northwestern central area of Kaifeng City (4.56/100,000) was higher than that in the southeastern suburban area (0.39/100,000), with a statistically significant difference (χ2=864.67, P<0.001). Global spatial autocorrelation analysis revealed that there was a positive spatial correlation in the spatial distribution of incidence rates of scarlet fever in districts and counties, with spatial aggregation (Moran's I=0.129, P=0.02). Local spatial autocorrelation analysis displayed that there were spatial height-height clustering phenomena in five districts and counties. A total of 33 scarlet fever clusters and 9 outbreaks were reported in Kaifeng City from 2012 to 2022, mainly occurring in kindergartens and primary schools (83.33%), involving 247 cases of scarlet fever (36.43%), and mainly occurring in the fourth quarter (69.05%) and the second quarter (23.81%). Conclusion The regional distribution of incidence rates of scarlet fever in Kaifeng City of Henan Province in 2012-2022 showed spatial clustering characteristics, mainly involving the northwestern central urban area, and the incidence rates of scarlet fever showed a downward trend from north to south and from west to east. The results suggest that targeted preventive and control measures against scarlet fever should be conducted based on the key population in key districts.
仇恒英, 张卫文, 张洁, 王永伟, 刘继艳, 王喜. 2012—2022年河南省开封市猩红热发病空间特征分析[J]. 实用预防医学, 2024, 31(3): 270-273.
QIU Hengying, ZHANG Weiwen, ZHANG Jie, WANG Yongwei, LIU Jiyan, WANG Xi. Spatial characteristics of incidence of scarlet fever in Kaifeng City, Henan Province, 2012-2022. , 2024, 31(3): 270-273.
[1] 张榕. 793例儿童猩红热临床特征分析[D]. 北京:北京大学医学部, 2021. [2] 张佳豪, 杨若男, 贺舒凝, 等. 2016—2020年中国猩红热发病的空间聚集性分析[J]. 南方医科大学学报, 2023,43(4):644-648. [3] Looper MD. This most dreadful scourge: scarlet fever in Sydney, 1841[J]. Health History, 2022,19(2):523-524. [4] 国家卫生健康委员会. 《中国卫生健康统计年鉴(2020)》[M]. 北京: 中国协和医科大学出版社, 2020:1-20 [5] 中华人民共和国卫生部. 卫生部办公厅关于印发《国家突发公共卫生事件相关信息报告管理工作规范(试行)》的通知[J]. 中华人民共和国卫生部公报, 2006,1:44-60. [6] Cho YN, Park SE, Cho EY, et al. Distribution of a genotypes in group A Streptococcus isolates of Korean children from 2012 to 2019[J]. J Microbiol Immunol Infect, 2022,55(4):671-677. [7] Sun LF, Xiao YJ, Huang WL, et al. Prevalence and identification of antibiotic-resistant scarlet fever group AStreptococcus strains in some pediatric cases at Shenzhen, China[J]. J Glob Antimicrob Resist, 2022,30:199-204. [8] 高桂玲, 王超, 吕锡宏,等. 2014—2020年上海市松江区猩红热时空分布特征分析[J]. 实用预防医学, 2023,30(1):16-20. [9] 高尚, 赵小冬, 刘建宝,等. 济南市2014—2018年猩红热流行特征及时空聚集性分析[J]. 实用预防医学, 2020,27(3):303-306. [10] 叶明,王永斌,徐璐,等. 基于指数平滑状态空间模型预测河南省猩红热发病趋势[J]. 河南预防医学杂志, 2021,32(4):241-247. [11] 王蕾, 王文娟, 韩红,等. 2010—2020年太原市猩红热流行病学特征分析[J]. 中国公共卫生管理, 2023,39(2):265-267. [12] Wang YB, Li YY, Lu H, et al. Now- and fore-casting the secular epidemiological trends and seasonality of the comeback of scarlet fever in China: a 16-year time series analysis[J]. Biomed Environ Sci, 2022,35(6):563-567. [13] 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. [14] 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. [15] 刘建生, 张海霞, 吕建伟. 保定市2001—2014年猩红热流行特征分析[J]. 实用预防医学, 2017, 24(1):101-103. [16] 王孟颖, 张昭玉, 张金,等. 2010—2019年辽宁省沈阳市猩红热发病与气象因素的关联性分析[J]. 预防医学论坛, 2022,28(10):734-738. [17] Herdman MT, Cordery R, Karo B, et al. Clinical management and impact of scarlet fever in the modern era: findings from a cross-sectional study of cases in London, 2018-2019[J]. BMJ Open, 2021,11(12):e057772. [18] Mcdonald SA,van Wijhe M,de Gier B,et al. The dynamics of scarlet fever in the Netherlands, 1906-1920: a historical analysis[J]. R Soc Open Sci, 2022,9(11):220030. [19] Cordery R, Purba AK, Begum L, et al. Frequency of transmission, asymptomatic shedding, and airborne spread ofStreptococcus pyogenes in schoolchildren exposed to scarlet fever: a prospective, longitudinal, multicohort, molecular epidemiological, contact-tracing study in England, UK[J]. Lancet Microbe, 2022,3(5):e366-e375. [20] Dean E. What do you need to know about scarlet fever and group A strep: alarm over children's deaths from serious infections has prompted an official alert and appeals to nurses to be extra vigilant[J]. Prim Health Care, 2023,33(1):255-257. [21] 陈丹, 张晨. 2015—2020年上海市杨浦区猩红热病例流行病学特征分析[J]. 疾病监测, 2022,37(6):745-749. [22] 寇玲玲, 王国栋, 李思瑶,等. 西安市2011—2020年猩红热流行病学特征分析[J]. 现代预防医学, 2021, 48(23):4245-4248. [23] 袁璐, 张元元, 房明,等. 2014—2018年中国猩红热时空聚集性分析[J]. 现代预防医学, 2022, 49(22):4033-4038. [24] 刘诗蒙. 2004—2019年吉林省猩红热发病趋势与空间聚集性研究[D]. 长春:吉林大学, 2021. [25] Ma YX, Gao SS, Kang Z, et al. Epidemiological trend in scarlet fever incidence in China during the COVID-19 pandemic: a time series analysis[J]. Front Public Health, 2022, 10:923318.