职业与健康 ›› 2025, Vol. 41 ›› Issue (18): 2495-2499.

• 论著 • 上一篇    下一篇

2014—2023年重庆市沙坪坝区0~<15岁儿童猩红热流行病学特征及趋势预测

杨长绢, 胡倩, 张协, 吴昕锴, 徐茜, 杨连建   

  1. 重庆市沙坪坝区疾病预防控制中心传染病防制科,重庆 400038
  • 收稿日期:2024-12-27 修回日期:2025-01-13 出版日期:2025-09-15 发布日期:2025-12-13
  • 通信作者: 张协,主管医师,E-mail:1282895606@qq.com
  • 作者简介:杨长绢,女,主管医师,主要从事传染病防控工作。

Epidemiological characteristics and trend prediction of scarlet fever among children aged 0-<15 years old in Shapingba District of Chongqing from 2014 to 2023

YANG Changjuan, HU Qian, ZHANG Xie, WU Xinkai, XU Qian, YANG Lianjian   

  1. Department of Infectious Disease Control,Chongqing Shapingba District Center for Disease Control and Prevention,Chongqing 400038,China
  • Received:2024-12-27 Revised:2025-01-13 Online:2025-09-15 Published:2025-12-13
  • Contact: ZHANG Xie,Physician in charge,E-mail:1282895606@qq.com

摘要: 目的 了解重庆市沙坪坝区猩红热流行病学特征及变化趋势,为制定预防与控制措施提供依据。方法 采用描述流行病学方法对2014—2023年重庆市沙坪坝区0~<15岁儿童猩红热疫情资料进行分析。构建季节性自回归综合移动平均模型(seasonal autoregressive integrated moving average model,SARIMA)对猩红热发病趋势进行预测。结果 2014—2023年重庆市沙坪坝区0~<15岁儿童猩红热报告725例,年均发病率为54.17/10万,2019年发病率最高(119.79/10万),2014年以来发病率呈上升趋势(χ2趋势=43.448,P<0.01),存在奇数年大于偶数年的交替流行现象。疫情呈双峰分布,分别为每年的4—7月和10月—次年1月,高峰期发病数620例,占发病总数的85.38%。东部城区病例数(389例,占53.66%)明显高于西部新城区,差异有统计学意义(χ2=4.053,P<0.01)。男童发病率(59.42/10万)高于女童(48.74/10万),差异有统计学意义(χ2=7.036,P<0.05)。以3~<9岁儿童为主(627例,86.48%),人群分类以学生和幼托儿童为主(683例,94.21%)。构建模型SARIMA(2,1,3)(1,0,2)12,使用决定系数(the coefficient of determination,R2)、贝叶斯信息准则(the normalized Bayesian information criterion,BIC)和均方根误差(the root mean squared error,RMSE)来评估拟合模型的拟合度,预测值的R2BICRMSE分别为0.791、1.731和1.634,与实际值总体拟合度较好。预测结果显示2024年7月—2025年6月有2个发病高峰,峰值分别为13.44/10万和13.90/10万,高于同期水平。结论 2014年以来猩红热疫情呈明显的上升趋势,有奇数年高于偶数年的特征,3~<9岁的学生(幼托儿童)是猩红热的高发人群,春夏和秋冬之交是疫情高发期,经模型预测2025年疫情将有所上升。应根据疫情特征继续加强猩红热的监测,并采取针对性的防控措施。

关键词: 猩红热, 流行病学特征, 季节性自回归综合移动平均模型, 趋势预测

Abstract: Objective To understand the epidemiological characteristics and changing trend of scarlet fever in Shapingba District of Chongqing,provide a basis for developing prevention and control measures. Methods The descriptive epidemiological method was used to analyze the epidemic data of scarlet fever among children under 15 years old in Shapingba District of Chongqing City from 2014 to 2023. The seasonal autoregressive integrated moving average model(SARIMA) was constructed for predicting the epidemic trend of scarlet fever. Results A total of 725 cases of scarlet fever in children under 15 years old were reported in Shapingba District of Chongqing City from 2014 to 2023,with the average annual incidence rate of 54.17/100 000,and the incidence rate in 2019 was the highest(119.79/100 000). Since 2014,the incidence rate showed an upward trend(χ2trend=43.448,P<0.01). Odd years had higher incidence intensity than even years. The epidemic showed a bimodal distribution,which were April to July and October to January of the following year respectively. During the peak period,there were 620 cases,accounting for 85.38% of the total number of cases. The number of cases in the eastern urban area(389 cases,53.66%) was significantly higher than that in the western new urban area,with a statistically significant difference(χ2=4.053,P<0.01). The incidence rate of boys(59.42/100 000) was higher than that of girls(48.74/100 000),with a statistically significant difference(χ2=7.036,P<0.05). The cases were mainly children aged 3-9 years old(627 cases,86.48%). Most of cases were students and kindergarten children(683 cases,94.21%). The model of SARIMA(2,1,3)(1,0,2)12 was established. The coefficient of determination(R2),the normalized Bayesian information criterion(BIC) and the root mean squared error(RMSE) were used to evaluate the goodness-of-fit of the fitted model. The R2,BIC and RMSE of predictive value was 0.791,1.731 and 1.634,showing good fit with the actual value. The prediction results showed that there would be two peaks of incidence from July 2024 to June 2025,with peaks of 13.44/100 000 and 13.90/100 000 respectively,which were higher than the same period level. Conclusion Since 2014,the scarlet fever epidemic shows a significant upward trend,with odd years being higher than even years. The students aged 3-9 years old(preschool children) are at high risk of scarlet fever,and the seasons of late spring and early summer and late autumn and early winter are the peak periods of the epidemic. The model predicts that the epidemic will increase in 2025. It is necessary to continue to strengthen the monitoring and prevention of scarlet fever based on the characteristics of the epidemic,and take targeted prevention and control measures.

Key words: Scarlet fever, Epidemiological characteristics, Seasonal autoregressive integrated moving average model, Trend prediction

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