| TB (tuberculosis, TB), known as "white plague", is an ancient infectious disease which has done harm to human for thousands of years in history. China is one of the 22 TB high-burden countries in the world and the number of active tuberculosis patients ranks second in the world. TB is not only a major public health issue, but is also a complex socio-economic problem. Previous studies ignored the geographic correlation, however, TB, as a communicable disease, is associated with the local environment, population, climate. And there is spatial relationship between the cases. Therefore it is necessary to take into account of geographical information and the relevant attributes to study its factor influencing the incidence of TB. In this study, we explore the spatial features of TB in Qingdao in 2005-2009 combined with Geographic Information System (GIS) with an objective for allocation of health resources, as well as prevention and control of tuberculosis.Results:(i) The spatial distribution centers of TB cases was (156.87,158) (154.26,155),(155.35,145), (157.62,138.95), (158.41,145) respectively in 2005-2009 in Qingdao, Shandong Province.Quartile spatial distances of 5 years were 27.69km, 29.39km,27.74km,28.35km and 26.89km. The spatial distribution of New TB cases was stable in five years, slightly southward trend.(ii) The average densities of TB cases in Qingdao in the five years are nearly the same. The relatively higher districts are North District, South District, Licang District and Sifang District; the lowest density of TB cases is Pingdu city, followed by Laixi City, Jiaozhou and Jimo. The distribution of newly registered TB cases was quite extensive, covering almost the entire city area.(iii) Ripley's K function analysis showed that, newly registered TB showed aggregation in five years. The peak of first clustering was about 16km; the strongest in large scale were different. the strongest clustering measure in 2007,2008 were about 16km, while the 2005,2006 and 2009, gathered the strongest measure in the about 25km. maximum aggregate size of 5 years were all longer than 48km. the largest peak of clustering in 2005 was largest, and with the smallest in 2007. After eliminating the effects of population density, the clusters of TB cases years have reduced in the scope, mainly concentrated in the urban north and south districts.(iv) In the spatial scale of villages, the the first-order spatial cluster "hot spots" were all more than 50 in 5 years. They are all statistically significant at a=0.05 level in the test via Monte Carlo simulation on the test. The hot spot spatial distribution was very extensive and more concentrated mainly in the urban areas and counties of the city center, the urban "hot spots" density was significantly higher than in rural areas.Compared with the village space scale, "hot spots" distribution in township level changed a lot. The "hot spots" mainly distributed in Jiaonan, Huangdao, Shinan, Shibei, Licang,Sifang, Chengyang, Jimo, coastal distribution concentration in southwest-northeastern direction and the north and central of Pingdu. Space "hot spots" distributed extensive, and had spatial heterogeneity. "hot spots"density in urban still significantly higher than rural areas. Because there were not statistically significant, so the township scale was not the best measure of tuberculosis space together, and village level is the best clustering scale. the scale of tuberculosis clustering was small.In the spatial scale of clustering scope of Ripley's L function, the numbers of "hot spots" in Qingdao City vary greately in 5 years in the first-order spatial cluster. The Monte Carlo simulation test showed that all of them don't have statistical significance at a 0.05 level. The "hot spots" mainly distributed in Jiaonan, Huangdao, Shinan, Shibei, Licang,Sifang, Chengyang, Jimo, coastal distribution concentration in southwest-northeastern direction in 2005,2008 and 2009. and distributed widely in 2006 and 2007, expecially in many rurul areas.(v) When adjusting the effect of population density, the numbers of "hot spots" in Qingdao City in 2006 is 149 and 106 in 2007 in the first-order scale, based on which the numbers of "hot spots" in Qingdao City in 2006 is 6 and 8 in 2007 in the second-order scale. All of them have statistical significance at a 0.05 level.Conclusions: (i) The average distribution of TB cased in Qingdao City in 2005-2009 were nearly the same. Their centers are both close to the urban area; and the distribution of TB cases in the 5 years covers a wide range of entire region of Qingdao. The distribution center showed slightly southward trend over time.(ii) The nearest neighbor clustering analysis showed the best clustering scale was village level. They were mainly distributed in the urban areas and cities and counties in the center before adjusted. After adjustment of the population density, the "hot spots" show that clusters of TB cases also occurred in rural areas, different from the results before the adjustment that clusters appear mainly in the urban area.(iii) All clustering analysis at different scales showed the "hot Spots" mainly distributed distributed in Jiaonan, Huangdao, Shinan, Shibei, Licang,Sifang, Chengyang, Jimo, coastal distribution concentration in southwest-northeastern direction4. The spatial heterogeneity of TB cases in Qingdao City is very important for the prevention and treatment of tuberculosis. This study could provide useful information for allocation of health resources, perhaps brings new hope for the current "step" of TB prevention and treatment.
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