广西壮族自治区2010-2015年传染性肺结核时空特征分析

目的 分析2010-2015年广西壮族自治区（广西）传染性肺结核的时间和空间变化趋势,为传染性肺结核防治工作提供依据。方法 从传染病报告系统收集2010-2015年广西报告的传染性肺结核病例（痰涂片和/或培养阳性病例）,采用SaTScan 7.0.2、GeoDa 1.8.12、R语言v 3.3.1和SPSS 19.0软件,运用时间序列优化模型、"Moran''s I"全局和局部空间自相关统计量、"Kulldorff''s"时空扫描统计量和基于"R语言"的聚集窗口三维可视化成像,描述和分析2010-2015年广西的传染性肺结核时空流行病学特征。结果 2010-2015年共报告传染性肺结核76 151例。经趋势χ²分析,年度报告发病率呈下降趋势（趋势χ²=3 464.53,P=0.000）。经优化筛选的Winters时间序列模型预测,2016年报告数为7 764（4 971~10 557）例,报告发病高峰在每年3月。2010-2015年的Moran''s I全局统计量均>0,在0.257~0.390之间波动,表明每年都存在传染性肺结核病例聚集。高发聚集区主要集中在广西的中、西部地区;东部地区则表现为低发聚集。"Kulldorff''s"时空扫描统计量分析提示了前期中、西部呈高发聚集,后期仅东部低发聚集的总体缓解态势。结论 广西2010-2015年传染性结核病疫情呈现春夏季高发且总体缓慢下降的时间变化趋势;时空聚集性分析为下一步探索结核分枝杆菌成簇和迁徙模式提供了重要线索。

Visual-spatial and temporal characteristics related to infectious Tuberculosis epidemics in Guangxi Zhuang Autonomous Region, 2012-2015

Objective To study the spatial and temporal mode of infectious TB transmission in Guangxi Zhuang Autonomous Region (Guangxi). Methods Data related to infectious TB case (Include smear and/or culture positive patients) in Guangxi were collected from the National Notifiable Disease Reported System (NNDRS) from 2010 to 2015. Spatial-temporal analysis and prediction were performed by SaTScan 7.0.2, GeoDa 1.8.12, R program v 3.3.1 and SPSS 19.0 software, using the time series model, Moran''s I global and local spatial autocorrelation (Empirical Bayes adjustment). Kulldorff''s space-time scan statistics displayed by R software was used to identify the temporal and spatial trend of TB. Results The total number of infectious TB cases, collected from NNDRS was 76 151, and showing a decreasing trend on annual incidence (value of Chi-square for Linear trend=3 464.53, P-value=0.000). The forecast value of TB cases in 2016 was 7 764 (4 971-10 557), with peak in March, analyzed through the Winters'' multiplicative model. The Moran''s I global Statistics was greater than 0 (0.257-0.390). TB cluster seemed to have been existed for several years. The most significant hot spots seemed to be mainly located in the central and western parts of Guangxi, shown by local spatial autocorrelation statistics and the result from space-time scanning. Counties or districts that located in the east parts of Guangxi presented the low-low relation (significant cold spots). The situation of infectious TB seemed migratory. Conclusions Our data showed an annual decreasing trend of incidence on infectious TB with temporal concentration in spring and summer. Main clusters (hot spots) were found to be located in the central and western parts of Guangxi. Hopefully, our findings can provide clues to uncover the real mode of TB transmission at the molecular-biological level.