| Tuberculosis (TB), known as "white plague", is a severe chronic disease which is infected by Mycobacterium tuberculosis. For severe threat of TB, almost every country in the world pays great attention to its control and prevention. China is one of the22TB high-burden countries in the world and the total number of tuberculosis patients ranks2nd. According to the data collected from2010Fifth National Tuberculosis epidemiological survey, it is estimate that there are still4.99million patients with active tuberculosis,720000smear-positive tuberculosis patients, and1290000sputum positive TB patients.With the development of spatial epidemiology and geographic information, the spacial epidemiological analysis on infectious diseases has become more and more widely. As an infectious disease, tuberculosis is related to the local environment, population distribution, climate status, and so on. Because of its infectivity and universality, the occurrence, development and prevalence of TB is a spatial phenomenon. So the research on the spatial epidemiology of tuberculosis can be helpful to determine the area of high incidence of tuberculosis space and meaningful to control and prevent the tuberculosis.In this study, the2010-2012spatial distribution of registration rates of Jining is described based on GIS. The global and local autocorrelation were analyzed by Moran’s I statistics to explore the hot and cold spot of registration rates. And further space and space-time scan statistic were performed to describe and analyze the spatial and spatial-temporal cluster characteristic. 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. From2010-2011, a total of9375TB cases,5955smear-positive cases(including4386new smear-positive cases) and3795smear-negative cases were registered in Jining City.2. Global spatial autocorrelation Moran’s I statistics were0.0452,0.1274and0.2661for2010-2012, respectively. The Moran’s I was significant in2011and2012,whereas not significant in2010.3. According to LISA analysis, in2010, the high-high regions of active tuberculosis were Shengshuiyu Town, Fucun Street, Hanzhuang Town, Zhaomiao Town, Gaolou Town, Weishandao Town and with regards tonew case registration rate of smear positive pulonary tuberculosis,the high-high regionswere Shengshuiyu Town, Gaoyu Town of Sishui county and Fucun Street, Hanzhuang Town, Zhaomiao Town of Weishan county.In2011,the high-high regions of active tuberculosis were Chengqian Town, Zhangzhuang Town, Fucun Street, Zhaomiao Town, Gaolou Town and Weishandao Town and with regards to new case registration rate of smear positive pulonary tuberculosis,the high-high regions were Chengqian Town, Zhangzhuang Town, Fucun Street, Zhaomiao Town, Gaolou Town, Weishandao Town.In2012,the high-high regions of active tuberculosis were Zhongce Town, Jinzhuang Town, Chengqian Town, Weishandao Town and Nishan Town and with regards to new case registration rate of smear positive pulonary tuberculosis, the high-high regions were Sizhang Town, Shengshuiyu Town, Chengqian Town, Tianhuang Town, Shiqiang Town, Taiping Town, Mapo Town.4. The purely spatial scan statistics indicated that there were nine statistically significant clusters, and the most likely cluster was consist of2towns (Gaolou Town and Weishan Town), with the RR of4.22within the window compared to outside in2010. There were7statistically significant clusters, the most likely cluster was consist of4towns (Zhangzhuang Town, Tianhuang Town, Xiangcheng Town, and Chengqian Town) and the RR was2.37within the window compared to outside in2011. There were7statistically significant clusters, and the most likely cluster was consist of1town(Guoli Town), and the RR was4.44in2012.Spatial-temporal cluster analysis of TB in2010-2012in Jining City showed that the most likely statistically significant cluster for high incidence of TB was found to exist at Hanzhuang Town, Weishandao Town, Gaolou Town, FucunTown, Zhaomiao Town, Huancheng Town, Xiping Town, ZhanglouTown, Town, LiuzhuangTown, Nanyang Town, Liangcheng Town, Yutai Town, Laozhai Town, Tangma Town,GutingTown,Kanwang Town,Xiangcheng Town,Shiqiang Town,Guoli Town. There were totally593incidences in the cluster area, RR=1.61. The first secondly clusters consist of Chengqian and Tianhuang Town in2010. The second secondly clusters consist of Liangshanin2011. The third secondly clusters consist of in Wangyin, Yandian, Xinyan, Huangtun, Liuxiang, Liying, Jiezhuang, Xuzhuang, Ershilipu, Nanzhang and Shiqiao Town2010.Conclusion:1. The registration rates of Jining tend to descend in2010-2012, 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 and keep increasing.3. The LISA analysis showed that the main high-high regions were Sishui country, Weishan country and some streets in Qufu city. More attention should be paid to these statistically significant high-high areas.4. According to the purely spatial scan statistics, the most likely clusters were Gaolou Town and Weishan Town in2010, Zhangzhuang Town, Tianhuang Town, Xiangcheng Town and Chengqian Town in2011,Guoli Town in2012. 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. Spatial-temporal cluster analysis indicated that the most likely cluster occurred in2010and was consist of Hanzhuang, Weishandao, Gaolou, Fucun, Zhaomiao, Huancheng, Xiping, Zhanglou, Liuzhuang, Nanyang, Liangcheng, Laozhai, Tangma, Guting, Kanzhuang, Xiangcheng,Shiqiang and Guoli Town. |
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