2004－2016年我国结核病流行的时空特征分析

目的 分析2004-2016年我国结核病登记病例的时空分布特征,探测聚集区域,为结核病防控提供理论依据。方法 利用ArcGIS 10.0软件作为数据管理和呈现的平台,建立我国2004-2016年结核病空间分析数据库,对结核病疫情进行空间自相关分析,采用SaTScan 9.6软件进行时空扫描分析。结果 2004-2016年全国共登记结核病病例13 157 794例,全国年均登记率为75.90/10万(27.95/10万~180.82/10万)。全局空间自相关结果显示结核病发病呈聚集性分布,局部Moran''s I自相关分析结果表明,新疆、西藏、贵州、广西和海南(省、自治区)为高-高聚集区域,北京、河北、天津、山东、江苏、上海(省、直辖市)为低-低聚集区域;局部G统计量热点分析结果显示,全国结核病疫情存在15个"热点"区域,其中3个"正热点"区域分别为新疆、西藏和海南(省、自治区), 12个"负热点"区域分别为北京、天津、辽宁、内蒙古、河北、山东、江苏、安徽、上海、山西、河南和吉林(省、自治区、直辖市)。利用SaTScan 9.6软件进行分阶段时空扫描分析,3个阶段共探测出12个聚集区域,每个聚集区域差异均有统计学意义(均P<0.05)。结论 2004-2016年我国结核病疫情呈现逐年下降的趋势,各省(自治区、直辖市)的年均登记率并非随机分布,呈明显的空间聚集性,分阶段时空扫描聚集区域逐渐减少,结核病防治工作取得一定进展,但高风险地区仍持续存在,需重点关注并采取针对性防控措施。

Temporal-spatial distribution of tuberculosis in China, 2004-2016

Objective To analyze the spatial distribution pattern and the cluster spots of tuberculosis (TB) patients in China from 2004 to 2016, so as to provide evidence for prevention and control of the disease. Methods Using ArcGIS 10.0 software as a platform for data management and presentation, a TB spatial analysis database from 2004 to 2016 was established, and spatial autocorrelation analysis was performed based on the TB epidemics. SaTScan 9.6 software was used for spatiotemporal scanning analysis. Results From 2004 to 2016, a total of 13 157 794 cases of pulmonary tuberculosis were registered in China, with the mean annual registered incidence rate as 75.90/100 000 (range:27.95/100 000-180.82/100 000). Through Global spatial autocorrelation studies, the results showed that the distribution of TB incidence was somehow clustered. The result of local Moran''s I autocorrelation analysis showed that Xinjiang, Tibet, Guizhou, Guangxi, Hainan provinces were high-high cluster areas, and Beijing, Hebei, Tianjin, Shandong, Jiangsu, and Shanghai provinces were low-low cluster areas. Result from the Getis-Ord General G spatial autocorrelation analysis showed the existence of fifteen "hot spot" regions, of which three "positive hot spots" were Xinjiang, Tibet, and Hainan provinces, and twelve "negative hot spots"were Beijing, Tianjin, Liaoning, Inner Mongolia, Hebei, Shandong, Jiangsu, Anhui, Shanghai, Shanxi, Henan, Jilin provinces. Using the SaTScan 9.6 software, results from the Phased spatial-temporal analysis identified twelve cluster areas, with statistical significances (P<0.05) among them. Conclusions From 2004 to 2016, tuberculosis epidemics showed an annual downward trend in China. The average annual rates of notification among provinces were not randomly distributed, showing the existence of obvious spatial aggregation. Numbers of areas with clustering nature that noticed through the temporal and spatial scanning technics had gradually decreased. At the same time, progress had been made in TB control programs, despite the existence of high-risk areas. Development of more strict and targeted prevention and control measures are called for.