| Tuberculosis (TB), known as "white plague", is a severe chronic disease which is infected by Mycobacterium tuberculosis. In2010, there were an estimated8.8million incident cases of TB globally, equivalent to128cases per100000population. China is one of the22TB high-burden countries in the world and the number of tuberculosis patient ranks2nd. Previous studies on tuberculosis and other infectious diseases were limited to simple description of incidence of the disease, ignoring the geographic correlation, not doing a study on the basis of the quantitative level of the spatial distribution of diseases. However, as an infectious disease, tuberculosis is related to the local environment, population, climate, and so on. Because of its infectivity and universality, the occurrence, development and prevalence of TB is a spatial phenomenon. Several studies showed that the spatial distribution of TB was nonrandom and clustered. Therefore, it is necessary to explore the hotspots of TB from the quantitative level with the use of spatial epidemiology and statistics of the analysis, as well as space, time and space-time cluster. In this study, the distribution of registration rates is described in2005-2010, based on GIS. The spatial empirical Bayes smoothing, Moran’s I and Local Moran’s I indexes were implemented in OpenGeoDa software, in order to determine the pattern of spatial clustering. And further space and space-time scan statistic were performed using SaTScan software. A better understanding of the spatial epidemiology of TB may help health departments to provide guidance for formulating regional prevention and control strategies.Results: 1. From2005-2010, a total of35262TB cases,22604smear-positive cases (including18890new smear-positive cases) and12658smear-negative cases were registered in Linyi City.2. Global spatial autocorrelation indications were0.1860,0.1790,0.0823,0.0849,0.0586and0.1145for2005-2010, respectively. The Moran’s I was significant in2005,2006and2010,while not significant in2008-2009.3. According to LISA analysis, the high-high regions were Matou Town, Taiping Street, Tangtou Street, Lanshan Street, Yinqueshan Street, Liguan Town and Wanggou Town in2005. The high-high regions were Shuangyuehu Street, Xiawei Town, Huayuan Town, Zhongfang Town, Gangshang Town, Taoxu Town and Zhubao Town in2006. The high-high regions were Xiawei Town, Cuijiayu Town, Shizilu Town, Daxing Town and Zhubao Town in2007. The high-high regions were Gaozhuang Town, Xiawei Town, Cuijiayu Town, Mamuchi Town, Andi Town and Liguang Town in2008. The high-high regions were Xiawei Town, Zhuanggang Town, Fangqian Town, Daxing Town and Zhubao Town in2009. The high-high regions were Quanli Town, Tongjing Town, Yiwen Town, Mamuchi Town, Andi Town, Xiangdi Town, Daxing Town and Yitang Town in2010.4. The purely spatial scan statistics indicated that there was one most likely statistically significant cluster including82towns, and the RR was1.27within the window compared to outside in2005. There were one most likely cluster and two statistically significant secondary clusters in2006. The most likely cluster included3towns (Lanshan Street, Zhubao Town and Yitang Town), and the RR was1.56. There were one most likely cluster and four secondary clusters in2007. The most likely cluster included3towns (Lanshan Street, Zhubao Town and Yitang Town), and the RR was1.82. There were one most likely cluster and four secondary clusters in2008. The most likely cluster included only one town (Pingyi Street), and the RR was1.93. There were one most likely cluster and five secondary clusters in2009. The most likely cluster included only one town (Pingyi Street), and the RR was2.67. There were one most likely cluster and five secondary clusters in2010. The most likely cluster included only one town (Shizilu Street), and the RR was2.21. Space-time cluster analysis of TB in2005-2010in Linyi City showed that the most likely statistically significant cluster for high incidence of TB was found to exist at Pingyi Street, for the year2008-2010(RR=2.22, p<0.001). Nine statistically significant secondary clusters were also detected for high incidence of TB.Conclusions:1. In Linyi City, the registration rate of TB was higher in men than that in women, and the number of registration in young and old people was big. The prevalence of TB indicated some seasonal characteristics, and spring is highest season.2. The global spatial autocorrelation suggested that the registration rates of TB were positively correlated in2005,2006and2010, while the Moran’s I were not significant in2007-2009.3. The LISA analysis showed that the main high-high regions were Liguanzhuang Town, Zhubao Town, Daxing Town, Mamuchi Town, Andi Town, Xiawei Town and Cuijiayu Town. More attention should be paid to these statistically significant high-high, high-low and low-high areas.4. According to the purely spatial scan statistics, the most likely clusters were Lanshan Street, Zhubao Town and Yitang Town in2005-2007, Pingyi Street in2008-2009, and Shizilu Street in2010. When compared the clusters of the spatial autocorrelation analysis with those of the space-time scan statistic, both methods detected similar and significant high-risk clustering. However, differences also existed for these two methods basing on different criteria and indicators. Space-time scan analysis showed that the most likely statistically significant cluster was Pingyi Street, for the year2008-2010(RR=2.22, p<0.001).Therefore, space-time scan statistic methods considering time dimension should be proposed as a dynamic supplement to purely spatial statistical methods. |
