关键词: 结核,肺, 筛查, 学生, 时空聚类分析

Abstract:

Objective: To analyze the spatial and temporal distribution differences and epidemiological trends of the screening results of registered pulmonary tuberculosis cases and contacts in schools from 2022 to 2023, to identify the key times and regions for tuberculosis prevention and control in schools of Guizhou Province, providing scientific basis for formulating and optimizing strategies for tuberculosis prevention and control in schools of Guizhou Province. Methods: Data on tuberculosis cases reported in schools of Guizhou Province for 2022—2023 were collected using the “China Disease Prevention and Control Information System—Tuberculosis Information Management System”, while data on contacts of tuberculosis cases were gathered through the “Guizhou Disease Prevention and Control Cloud Platform—School Tuberculosis Management System”. Descriptive epidemiological methods were used to analyze the epidemiological characteristics of school tuberculosis cases and detected cases from their contacts. The trend of monthly average tuberculosis case registration rates (and detection rates from contact screening) was analyzed using Joinpoint. Global and local spatial autocorrelation analyses were conducted using ArcGIS 10.6, and spatiotemporal scan analysis was performed using SaTScan 9.7. Results: From 2022 to 2023, Guizhou Province reported 6310 school tuberculosis cases, with an annual average registration rate of 29.82/100000 (6310/21161926) and a bacteriological positivity rate of 44.83% (2829/6130). A total of 255801 school contacts were screened, identifying 288 tuberculosis cases, resulting in a detection rate of 0.11%. Joinpoint regression analysis revealed the monthly average registration rate for tuberculosis cases showed an overall increase (AAPC=0.60%, 95%CI:-8.50% to 10.60%, P=0.906) per month, while the monthly average detection rate among contacts decreased (AAPC=-17.45%, 95%CI: -31.43% to -0.62%,P=0.043) per month. Local spatial autocorrelation analysis identified 8 counties (districts) as “high-high clusters” for tuberculosis cases, including areas in Bijie Prefecture (Qixingguan District, Dafang County, Qianxi City, Hezhang County, Zhijin County, Nayong County) and Liupanshui Prefecture (Zhongshan District, Shuicheng District). For contact cases, 2 counties (districts) were identified as “high-high clusters”, located in Bijie Prefecture (Qixingguan District) and Liupanshui Prefecture (Zhongshan District). Spatiotemporal scan analysis showed that tuberculosis cases centered around Dafang County, involving 4 counties (districts)(RR=1.63, LLR=62.76, P<0.001), while contact cases centered in Xixiu District, covering 16 counties (districts)(RR=6.65, LLR=9.74, P<0.001). Conclusion: The registered incidence of tuberculosis in schools in Guizhou Province was relatively high, and the “high-high” cluster area of school tuberculosis was mainly located in Bijie Prefecture, and more tuberculosis patients were detected during tuberculosis contacts screening at the beginning of the year.

Key words: Tuberculosis,pulmonary, Screening, Students, Space-time clustering