职业与健康 ›› 2025, Vol. 41 ›› Issue (7): 936-941.

• 论著 • 上一篇    下一篇

2020—2023年重庆市九龙坡区细菌性痢疾流行特征及时空聚集性分析

周慧娴, 姚远, 邓春燕, 吴霜, 龙前进   

  1. 重庆市九龙坡区疾病预防控制中心传染病预防控制科,重庆 400039
  • 收稿日期:2025-01-05 修回日期:2025-01-20 出版日期:2025-04-01 发布日期:2025-12-17
  • 通信作者: 龙前进,主任医师,E-mail:394112040@qq.com
  • 作者简介:周慧娴,女,医师,主要从事急性传染病防制工作。
  • 基金资助:
    重庆市公共卫生重点专科(学科)建设资助项目(渝卫办发[2023]81号); 重庆市九龙坡区公共卫生重点学科和实验室建设经费资助项目(九龙坡卫办发[2023]63号)

Epidemiological characteristics and spatio-temporal clustering analysis of bacillary dysentery in Jiulongpo District of Chongqing from 2020 to 2023

ZHOU Huixian, YAO Yuan, DENG Chunyan, WU Shuang, LONG Qianjin   

  1. Department of Infectious Disease Prevention and Control,Chongqing Jiulongpo District Center for Disease Control and Prevention,Chongqing 400039,China
  • Received:2025-01-05 Revised:2025-01-20 Online:2025-04-01 Published:2025-12-17
  • Contact: LONG Qianjin,Chief physician,E-mail:394112040@qq.com

摘要: 目的 通过对2020—2023年重庆市九龙坡区细菌性痢疾(bacillary dysentery,BD)的流行病学特征进行全方位分析,从空间和时间上识别高危区域,从而为九龙坡区BD的监测和防控提供依据。方法 从“中国疾病预防控制信息系统”中导出2020—2023年重庆市九龙坡区BD每月发病数,采用描述流行病学研究方法分析疾病流行特征,应用Joinpoint回归分析评价发病升降趋势,运用圆形分布法描述季节特征,使用空间自相关和时空扫描统计量探索发病的时空分布。结果 2020—2023年重庆市九龙坡区累计报告BD病例 1 482例,年平均报告发病率24.27/10万,发病率呈逐年下降趋势(APC=-20.30%,AAPC=-20.30%,95%CI:-24.90%~-16.19%,P<0.05)。2020—2022年存在明显季节性,高峰日主要在7—9月,高峰期最早出现在4月,最晚在12月。男女性别比为1.05 ∶ 1,发病主要影响<5岁散居儿童。2020—2023年重庆市九龙坡区BD发病率全局空间自相关和局部自相关均有统计学意义(均P<0.05),High-High聚集模式主要集中在黄桷坪街道、杨家坪街道、石坪桥街道、谢家湾街道。时空扫描分析结果显示,2020年BD聚集时间为5—10月、2021年为3—9月、2022年为1—9月、2023年为1—7月。结论 重庆市九龙坡区作为BD的重点防控区域,相关部门应进一步创造良好卫生环境、做好BD健康宣教,提高诊断准确性,并结合季节性高发和疾病谱特点以控制BD的传播与流行。

关键词: 细菌性痢疾, 流行特征, 空间自相关, 时空聚集性

Abstract: Objective To conduct a comprehensive analysis of the epidemiological characteristics of bacterial dysentery(BD) in Jiulongpo District of Chongqing from 2020 to 2023,identify high-risk areas spatially and temporally,so as to provide a basis for the monitoring,prevention and control of BD in Jiulongpo District. Methods The monthly incidence numbers of BD in Jiulongpo District of Chongqing from 2020 to 2023 were derived from the "China Disease Prevention and Control Information System". Descriptive epidemiological research methods were used to analyze the epidemiological characteristics of the disease,Joinpoint regression analysis was used to evaluate the trend of incidence rise and fall,circular distribution method was used to describe seasonal characteristics,and spatial autocorrelation and spatiotemporal scanning statistics were used to explore the spatiotemporal distribution of incidence. Results From 2020 to 2023,a cumulative total of 1 482 BD cases were reported in Jiulongpo District of Chongqing,with an average annual reported incidence rate of 24.27/100 000,and the incidence rate showed a downward trend year by year(APC=-20.30%,AAPC=-20.30%,95%CI:-24.90%- -16.19%,P<0.05). There was significant seasonality from 2020 to 2022,with peak days mainly occurring from July to September,with the earliest peak appearing in April and the latest in December. The male to female sex ratio was 1.05 ∶ 1,and the incidence of the disease mainly affected diaspora children under 5 years old. From 2020 to 2023,the global spatial autocorrelation and local autocorrelation of BD incidence rate in Jiulongpo District of Chongqing City were statistically significant(both P<0.05),and the High-High clustering pattern was mainly concentrated in Huangjueping Street,Yangjiaping Street,Shipingqiao Street and Xiejiawan Street. The spatio-temporal scanning analysis showed that the BD aggregation time in 2020 was from May to October,in 2021 it was from March to September,in 2022 it was from January to September,and in 2023 it was from January to July. Conclusion As a key prevention and control area for BD,Jiulongpo District in Chongqing should further create a good hygiene environment,carry out BD health education,improve diagnostic accuracy,and combine seasonal high incidence and disease spectrum characteristics to control the spread and prevalence of BD.

Key words: Bacillary dysentery, Epidemiological characteristics, Spatial autocorrelation, Spatiotemporal clustering

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