Spatial distribution and risk factors of tuberculosis in Songjiang district, Shanghai during 2006 and 2009 / 中华预防医学杂志

<p><b>OBJECTIVE</b>To identify spatial distribution and risk factors among tuberculosis (TB) cases in Songjiang district, Shanghai, 2006 - 2009.</p><p><b>METHODS</b>All active TB cases and all bacteriologically confirmed TB cases diagnosed during the period from 2006 to 2009 were recruited into the study. Spatial scan statistics were used to identify spatial clusters. Using logistic regression, we compared the demographic and clinical characteristics of TB cases in spatial clusters versus TB cases not in spatial clusters.</p><p><b>RESULTS</b>A total of 1815 active TB cases and 730 bacteriologically confirmed TB cases were recruited during 2006 - 2009. Chedun township and Xinqiao township was detected to be a spatial cluste (RR = 1.38, LLR = 16.78, P < 0.01), which was the location of the municipal industrial zone. No spatial cluster was found during 2006 - 2007, while during 2008 - 2009 Chedun township was detected to be a spatial cluster (RR = 1.70, LLR = 15.06, P < 0.01). Among resident population, the spatial cluster of TB cases was located in the southwestern part of Songjiang district, which included five townships Xinbang, Shihudang, Xiaokunshan, Maogang and Yongfeng (RR = 1.49, LLR = 10.52, P < 0.01); while among migrant population, the spatial cluster of TB cases was located in Chedun township (RR = 1.55, LLR = 15.64, P < 0.01). There were higher proportions of resident TB cases who were farmers (AOR = 4.9, 95%CI: 1.9 - 12.3) or had other occupations (AOR = 2.6, 95%CI: 1.1 - 5.9) in the spatial cluster. There were higher proportions of migrant TB cases who lived here for less than 5 years (< 1 year: AOR = 5.9, 95%CI: 1.8 - 19.5; 1 - 5 years: AOR = 3.2, 95%CI: 1.0 - 9.9) or worked at other occupations (AOR = 2.8, 95%CI: 1.5 - 5.1) and lower proportions of migrant TB cases who came from Eastern region (AOR = 0.3, 95%CI: 0.1 - 0.8) or Middle region (AOR = 0.5, 95%CI: 0.3 - 0.9) in the spatial cluster.</p><p><b>CONCLUSION</b>In Songjiang district there was a spatial cluster in TB cases, which was Chedun township. Local residents with TB who were farmers or had other occupations were more likely to be in the spatial cluster. Migrants with TB who lived here for less than 5 years or came from Western region were more likely to be in the spatial cluster.</p>

Study on the quantified indices to describe the distributional status of diseases in the spatial point pattern analysis / 中华预防医学杂志

<p><b>OBJECTIVE</b>To study the quantified indices for describing the distributional status of diseases in the spatial point pattern analysis, and provide the a statistic in disease prevention and control.</p><p><b>METHODS</b>G function, F function, J function and K function were summarized based on the inter-case distances from the view of spatial point pattern analysis. Through the introduction of the basic principles, these were used to analyze the data of acute schistosomiasis in the Guichi District, Chizhou City, Anhui province, with the study distances being from 0 to 3000 meters with 50-meter intervals. The findings were also validated by means of spatial moving scan window performed in SaTScan software.</p><p><b>RESULTS</b>A total of 83 cases of acute schistosomiasis identified in Guichi District, and the point map showed that these cases were mainly distributed around the Yangtze and Qiupu rivers. The computational methods and characteristics of the four quantified indices were obtained. These acute schistosomiasis cases were also explored by using these indices, and the results showed that C and K functions were above 95% confidence interval. While, F and J functions were below 95% confidence interval. Ml these four indices showed that spatial clustering existed in the acute cases, which was consistent with the results of spatial moving scan window method. The latter method also found a most likely cluster, the coordinate of the circle center is (30.65 N, 117.44 E), radius is 2.69 km, and relative risk is 12.78 (DIR = 32. 80, P = 0. 0001).</p><p><b>CONCLUSION</b>The quantified indices to describe the distributional status of diseases have not only solved the obstacle that spatial point pattern map which could only be analyzed qualitatively, but also supplied a theoretical foundation to deepen spatial clustering analysis.</p><p><b>OBJECTIVE</b>To study the quantified indices for describing the distributional status of</p>

Linear models for predicting of Oncomelania hupensis in the lake and marsh regions / 中华预防医学杂志

<p><b>OBJECTIVE</b>To study the prediction model of O. hupensis in the lake and marshland regions in order to provide methodological basis for quantitative study of O. hupensis.</p><p><b>METHODS</b>The research sites were randomly selected from the bottomlands along Qiupu River in the Guichi District, Anhui Province. A random and stratified sampling method was administrated according to the type of vegetation; the frame size of snail survey was 0.11 m2. Snail data was collected by crosscheck-random sampling inspection survey. Elevation, soil temperature and air temperature, height of vegetation, soil humidity and types of vegetation were measured through GPS machine, T&D Recorder for Windows, tape measure and attemperator. All the data were doubly inputted into the computer and checked. The final dataset for developing the prediction model was set up after necessary data preprocessing, such as, recoding the variable of elevation. The generalized linear models were used to develop the prediction model, and the statistics of deviance and AIC were used to determine the best model structure. Model diagnostics and model evaluation of efficiency were performed with the determined best model structure.</p><p><b>RESULTS</b>The sample size was 162, and there were 6 explanatory variable including 2 categorical variables and 4 quantitative variables. A complicated relationship was observed among all the variables. Snail was positively associated with height of vegetation (r = 0.36), while negatively associated with soil humidity (r = - 0.22), and the air temperature had a close positive relations with soil temperature (r = 0.59), and the soil temperature was negatively associated with height of vegetation (r = - 0.36), the soil humidity had negative relations with the soil and air temperature (r = -0.34 and -0.12). The best structure fitting for the liner model selected in gamma distribution was the error distribution, reciprocal as the conjunction function in mathematics, and the mean square as the variance function. The results showed that the elevation, soil humidity, soil temperature, types and the height of vegetation were statistically significant to predict the O. hupensis, while t-values were -3.202, 3.124, -1.989, 2.668 and -2.371, respectively, and P-values were 0.00166, 0.00214, 0.04849, 0.00846 and 0.01897 respectively.</p><p><b>CONCLUSION</b>The generalized linear models can be used to develop the predictive model, which could broaden the area of quantitative study for O. hupensis.</p>