Study On Spatial Distribution Of Tuberculosis And Influential Factors In Shandong Province

Tuberculosis (TB), known as "white plague," is a chronic infectious disease which can make serious hazard to human health. China is one of the 22 TB high-burden countries in the world and the number of tuberculosis patients ranks 2nd. TB is not only an important public health problem, but also is a complex socio-economic problem. Prevalence of TB hinders social and economic development. 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 with interaction and diffusing phenomenon. Therefore, the relevant data should be based on spatial property, considering the location information and non-location information. This reflects dynamic distribution characteristics on space and its influential factors in order to meet the needs of TB prevention and control work. In this study, the distribution of registration rates is described in 1995～1997 and 2005～2007, based on GIS. Moran's / and Local Moran's / indexes are used to estimate the spatial autocorrelation and local spatial autocorrelation through GeoDa095i, in order to determine the pattern of spatial clustering. And further spatial regression analysis is used to analysis influential factors of registration rates.Results:1. In Shandong Province, the registration rate of TB is 22.48/100000 in 1995. It increases to 28.28/100000 in 1996 and 28.54/100000 in 1997. The registration rate in 2005, in 2006 and in 2007 is 40.89/100000, 41.94/ 100000 and 44.14/100000 respectively.2. Global spatial autocorrelation indications are 0.3145, 0.4059, 0.3684, 0.3585, 0.2675 and 0.2374 in these six years. The Moran's /of six years all have the significance.3. According to LISA analysis, the registration rates of counties in Jinan, Weifang, Qingdao are in low-low region in 1995～1997. There is little change in these three years. The registration rates of counties in Linyi, Rizhao, Tai'an are in high-high region in 1995; Tai'an, Liaocheng, Jining, Heze and Linyi locate in high-high region in 1996; Tai'an, Liaocheng, Heze and Linyi remain locate in high-high region in 1997, Jinan comes into high-high region, but Jining doesn't locate in high-high region. Low-low regions of the registration rate in 2005 include Yantai, Weifang, Pingdu in Qingdao, Guangrao in Dongying and Zichuan in Zibo, these regions join into a block. In addition, Taishan in Tai'an and Shizhong and Licheng in Jinan locate in low-low region. Low-low regions decrease in 2006, remain including Yantai, Weifang, Pingdu in Qingdao, Guangrao in Dongying. Low-low regions in Jinan increase, including Lixia, Shizhong, Tianqiao, Huaiyin and Licheng. Low-low regions in 2007 are similar to it in 2005. High-high regions of the registration rates in 2005 include Linyi, Leling in Dezhou, Yangxin, Wudi, Zhanhua and Bincheng in Binzhou, Hekou and Kenli in Dongying. The registration rates of Linyi in 2006 remain locate in high-high region. Decheng, Leling and Pingyuan in Dezhou and Wudi in Binzhou also locate in high-high regions. High-high regions in 2007 remain include Linyi, leling and lixian in Dezhou and Wudi in Binzhou. Yanggu in Liaocheng and Chengwu and Shanxian in Heze enter into high-high regions.4. Through spatial Lag Model and Spatial Error Model, we find that the number of TB suspects consulting and per capita net income of rural population have influence on the registration rate of TB. The registration rate is higher in the county with more TB suspects consulting. The more per capita net income of rural population, the lower the registration rate is. But the density of population has no influence on the registration rate of TB.Conclusions:1. The registration rates of TB in 1995～1997 are low, and it increases very much in 2005-2007.2. Global spatial autocorrelation indications are all above 0 in these six years. It shows that the registration rates of TB are positively correlated.3. According to LISA analysis, the registration rates of counties in Jinan, Weifang, Qingdao locate in low-low regions in 1995～1997. There is a little change in these three years. The registration rates of counties in Yantai, Weifang and Jinan locate in low-low region in 2005～2007. Pingdu in Qingdao remain locates in low-low regions. A part of counties in Linyi locate in high-high regions all the time in these six years.4. The registration rate is higher in the county with more TB suspects consulting. The more per capita net income of rural population, the lower the registration rate is. But density of population has no influence on the registration rate of TB.