| Tuberculosis (TB) is an infectious disease caused by the bacillus Mycobacterium tuberculosis and spreads through air by a person suffering from TB. The 1990 World Health Organization (WHO) report on the Global Burden of Disease ranked TB as the seventh most morbidity-causing disease in the world, and expected it to continue in the same position up to 2020. In 2001, the WHO estimated that 1.86 billion persons (equal to 32% the world's total population ) were infected with TB. 8.74 million in the world develop TB and nearly 2 million of them die every year. Unless properly treated, an infectious pulmonary TB patient can infect on average between 10 and 15 people in a year. TB kills more adults than any other infectious disease worldwide, accounting for almost 400,000 deaths annualy. It mainly afflicts people who are in the economically productive years of their life (equal to 15-54 years). An adult suffering from TB loses on average between three to four months of working time. This equals to a loss of 20%-30% of household's annual income. This shows that it will cause large social and economic burden on a country. So TB is the biggest public heath challenge for experts and researchers.As we all know, medicine is a system of microcosmic and macroscopical comprehensive disciplines, a large number of data has characteristics of spatial distribution. Topic of Epidemiology is the distribution of space, time and population about the diseases, about 80 percent of the epidemiological data has space attributes, such as people or animals are always in the prevalence of certain space location, but the space geographical or social factors of a certain location also affect the incidence of the disease, so only the precise analysis of the spatial distribution of the disease can effectively study the cause of the disease, then we can identify the high-risk population and areas and develope measures of prevention. For instance, the incidence and prevalence of infectious diseases and endemic diseases of the distribution, many local high incidence of the disease , the distribution of medicine and public health agencies are all closely related to spatial information. Space-related characteristics of medical data is the premise of the space application of spatial statistics.According to the results of the previous research, the study of infectious diseases we can easily find always use the method of traditional statistical model, failed in the anlalysis of the correlation of space and numerical analysis. The emerging spatial statistics supports a new way for the spatial autocorrelation on the basis of spatial analysis, its core is the understanding and location-related data between the space-dependent. For example, tuberculosis and other infectious diseases researches are more 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. And tuberculosis as an infectious disease are related to the local environment, population, climate and so on, because of its infectivity and universality, qualitative study is insufficient to meet the needs of prevention and control of diseases. Therefore, we explore the hot spot of tuberculosis incidence from the quantitative level with the use of spatial statistics of the analysis, as well as space, time and space-time cluster is very necessaryObjectivesThe main objectives of this research: exploring the spatial distribution and spatio-temporal distribution of TB incidence with spatial autocorralation and spatial scan statistics method; exploring where and when the new cases happen, that is to say ,exploring the spatio-temporal hot spot to know the spatio-temporal cluster of TB, aim to supply with the theroy evidence for the policy of disease prevention and control and the reference for the same study.Case analysisWe accumulate the TB incidence data about 1997-2003 year from Ningbo Center of Disease prvention and Control center (CDC), and get the Ningbo Map of scale with 1:250000, input the region code, incidence data, population and TB incidence data each year of the 11 regions in Ningbo; translate the excel data into SHP format data.Moran's I, Moran's I scatter plot, Monter-Carlo and spatial empirical Bayes smooth method of spatial autocorrelation can be realized with software GeoDa0985i; spatial and spatio-temporal scan statistics can be realized with software SaTScan7.0; spatial cluster sketch map, spatio-temporal scan windows can be realized using Arc View GIS.Results1. Moran's I methodWe get the global spatial autocorrelation indication from 1997 to 2003 year: 0.1265, 0.2628, 0.2585, 0.1386, 0.1285, 0.0012, 0.0845, the Moran's I of 1997,1998, 1999, 2000, 2001 year have the significance, but 2002,2003 year's not, with statistical test.According to LISA analysis, TB incidence of Xiangshan locates in high-high region in 1997; Xiangshan and Ninghai locate in high-high region in 1998,but Cixi locates in low-low region; Xiangshan and Ninghai locate in high-high region in 1999, but Jiangbei and Cixi locates in low-low region; Xiangshan locates in high-high region in 2000,but Jiangbei and Cixi locate in low-low region; Xiangshan locates in high-high region, but Jiangbei in low-low region in 2001; Xiangshan locates in low-high region, but Jiangbei in low-low region in 2002; Ninghai locates in low-high region, but Jiangbei in low-low region in 2003; the outcomes have the significance ( P<0.05 ) with statistical test.2. Spatial Scan Statistics(SaTScan)The outcome of purely spatial scan statistics is including that most likely cluster includes Xiangshan (RR=1.620, P=0.01<0.05 ), the annual cases/100,000 is 45.5/100,000, secondary cluster includes Fenghua and Yinzhou (RR=1342, P=0.01<0.05 ), the annual cases/100,000 is 37.2/100,000 in 1997; most likely cluster includes Fenghua and Yinzhou (RR=1.493, P=0.001<0.05 ), the annual cases/100,000 is 35.7/100,000, secondary cluster includes Xiangshan and Ninghai (RR=1.342, P=0.01<0.05 ), the annual cases/100,000 is 37.2/100,000 in 1998; most likely cluster includes Fenghua and Yinzhou (RR=1.831, P=0.001<0.05 ), the annual cases/100,000 is 44.8/100,000, secondary cluster includes Xiangshan and Ninghai (RR=1.484, P=0.001<0.05 ), the annual cases/100,000 is 41.2/100,000 in 1999; most likely cluster includes Fenghua and Yinzhou (RR=1.364, P=0.001<0.05 ), the annual cases/100,000 is 39.0/100,000, secondary cluster includes Xiangshan and Ninghai (RR=1.311, P=0.006<0.05 ), the annual cases/100,000 is 39.4/100,000 in 2000; most likely cluster includes Fenghua and Yinzhou (RR=1.514, P=0.001<0.05 ), the annual cases/100,000 is 40.6/100,000, secondary cluster includes Xiangshan and Ninghai (RR=1.388, P=0.001<0.05 ), the annual cases/100,000 is 38.5/100,000 in 2001; most likely cluster includes Fenghua and Yinzhou (RR=1.430, P=0.001<0.05), the annual cases/100,000 is 43.5/100,000 in 2002; most likely cluster includes Fenghua and Yinzhou (RR=1.670, P=0.001<0.05 ), the annual cases/100,000 is 54.7/100,000, secondary cluster includes Xiangshan and Ninghai (RR=1.995, P=0.001<0.05 ), the annual cases/100,000 is 75.0/100,000 in 2003.The outcome of spatio-temporal scan statistics is that the time of the most likely cluster is from 2001 to 2003, and at the same time ,the most likely cluster includes Ninghai and Fenghua (RR=1.477, P=0.001<0.05 ), the annual cases/100,000 is 46.3/100,000, secondary likely cluster time is from 1999 to 2001, and the secondary cluster is Yinzhou (RR=1.9364, P=0.001<0.05 ), the annual cases/100,000 is 41.8/100,000.Conclusions1. Moran scatter plot has local autocorrelation function, that is to say, the quadrant of Moran scatter plot can indicate different cluster, and evaluate the relation of local areas. According to the Moran scatter in 1997, we can decide Xiangshan is in the first quadrant, Yuyao, Fenghua, Yinzhou and Zhenhai in the second quadrant, Cixi, Haishu, Jiangdong and Jiangbei in the third quadrant, Ninghai and Beilun in the fourth quadrant. But Moran scatter can't support the Moran's I of local areas, if we get this Moran's I, we must compute the LISA by statistical test.2. The LISA of 1997, 1998, 1999, 2000, 2001 indicate that Xiangshan in the south east and Ninghai in the south are hot spots of TB incidence in Ningbo. TB disease has diffusibility for the infectivity, and it maybe exsits the uprising trend from south to north..3. The result of Purely spatial scan statistics indicates that the most likely cluster is Xiangshan in 1997; Fenghua and Yinzhou are the most likely cluster; Xiangshan, Ninghai and Fenghua are the most likely clusters in 2003. This result is not consistant with the spatial autocorrelation, this difference maybe is based on different standard and different index. But the purely spatial scan statistics result is consistant with the result that Ninghai and Fenghua locates in high-high area and the hot spot in the south and middle of Ningbo regions.According to the spatio-temporal scan statistics, the most likely spatio-time clusters are Fenghua and Ninghai between 2001and 2003; Jiangbei is the secondary cluster; Yinzhou and Xiangshan between 1999 and 2001 are the tertiary clusters. So this method can evaluate the spatio-temporal cluster of TB incidence in Ningbo regions.4. Comparing to the Moran's I method of Spatial autocorrelation, spatial scan statistics estimates the risk between the inside and outside of circle window with likelyhood ratio method and spatio-temporal mutual interation, so this method is suitable for evaluate the spatio-temporal cluster of TB incidence in Ningbo. Moran's I tends to be consistently larger with Spatial empirical Bayes smooth method and often be limited for small spatial scale, so Moran's I method is suitable for exploring the spatial model.5. The global spatial autocorrelation of the five years from 1997 to 2001 indicates the spatial cluster in Ningbo region, but we don't decide whether this cluster is the area of high accurance; The statistical test of Moran's I of 2002 and 2003 year indicates that there is not global spatial autocorrelation. |
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