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Objective To analyze the spatial distribution of tuberculosis (TB) and identify the clustering areas in Qinghai province from 2014 to 2016,and provide evidence for the prevention and control of TB.Methods The data of pulmonary TB cases confirmed by clinical and laboratory diagnosis in Qinghai during this period were collected from National Disease Reporting Information System.The visualization of annual reported incidence,three-dimensional trend analysis and local Getis-Ord G* spatial autocorrelation analysis of TB were performed by using software ArcGIS 10.2.2,and global Moran's I spatial autocorrelation analysis were analyzed by using software OpenGeoDa 1.2.0 to describe and analyze the spatial distribution characteristics and high incidence areas of TB in Qinghai from 2014 to 2016.Results A total of 20 609 pulmonary TB cases were reported in Qinghai during this period.The reported incidences were 101.16/100 000,123.26/100 000 and 128.70/100 000 respectively,an increasing trend with year was observed (trendx2=187.21,P＜0.001).The three-dimensional trend analysis showed that the TB incidence increased from northern area to southern area,and up-arch trend from the east to the west.Global Moran's I spatial autocorrelation analysis showed that annual reported TB incidence in different areas had moderate spatial clustering (Moran's I values were 0.631 3,0.605 4,and 0.587 3,P＜0.001).And local Gf analysis showed that there were some areas with high TB incidences,such as 10 counties of Yushu and Guoluo prefectures (Gande,Banma and Dari counties,etc.,located in the southwest of Qinghai),and some areas with low TB incidences,such as Huangzhong county,Chengdong district and Chengbei district of Xining city and Dachaidan county of Haixi prefecture,and the reported TB incidences in the remaining areas were moderate.Conclusion The annual reported TB incidence increased year by year in Qinghai from 2014 to 2016.The distribution of TB cases showed obvious spatial clustering,and Yushu and Guoluo prefectures were the key areas in TB prevention and control.In addition,the spatial clustering analysis could provide the important evidence for the development of TB prevention and control measures in Qinghai.
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Objective To analyze the spatial distribution of tuberculosis (TB) and identify the clustering areas in Qinghai province from 2014 to 2016,and provide evidence for the prevention and control of TB.Methods The data of pulmonary TB cases confirmed by clinical and laboratory diagnosis in Qinghai during this period were collected from National Disease Reporting Information System.The visualization of annual reported incidence,three-dimensional trend analysis and local Getis-Ord G* spatial autocorrelation analysis of TB were performed by using software ArcGIS 10.2.2,and global Moran's I spatial autocorrelation analysis were analyzed by using software OpenGeoDa 1.2.0 to describe and analyze the spatial distribution characteristics and high incidence areas of TB in Qinghai from 2014 to 2016.Results A total of 20 609 pulmonary TB cases were reported in Qinghai during this period.The reported incidences were 101.16/100 000,123.26/100 000 and 128.70/100 000 respectively,an increasing trend with year was observed (trendx2=187.21,P＜0.001).The three-dimensional trend analysis showed that the TB incidence increased from northern area to southern area,and up-arch trend from the east to the west.Global Moran's I spatial autocorrelation analysis showed that annual reported TB incidence in different areas had moderate spatial clustering (Moran's I values were 0.631 3,0.605 4,and 0.587 3,P＜0.001).And local Gf analysis showed that there were some areas with high TB incidences,such as 10 counties of Yushu and Guoluo prefectures (Gande,Banma and Dari counties,etc.,located in the southwest of Qinghai),and some areas with low TB incidences,such as Huangzhong county,Chengdong district and Chengbei district of Xining city and Dachaidan county of Haixi prefecture,and the reported TB incidences in the remaining areas were moderate.Conclusion The annual reported TB incidence increased year by year in Qinghai from 2014 to 2016.The distribution of TB cases showed obvious spatial clustering,and Yushu and Guoluo prefectures were the key areas in TB prevention and control.In addition,the spatial clustering analysis could provide the important evidence for the development of TB prevention and control measures in Qinghai.
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Objective To analyze the spatial distribution of the incidence of tuberculosis (TB)in China from 2012 to 2014 and provide evidence for the prevention and control of TB.Methods The database of TB in China from 2012 to 2014 was established by using geographical information system,the spatial distribution map was drawn,trend analysis and spatial autocorrelation analysis were conducted to explore the spatial distribution pattern of TB and identify hot areas.Results The trend surface analysis showed that the incidence of TB decreased gradually from the west to the east in China,and the U type curve could reflect the TB distribution from the south to the north;Global spatial autocorrelation analysis showed the 2012-2014 global Moran's I were 0.366,0.364 and 0.358(P＜0.01),suggesting that the incidence of TB had a spatial clustering in China;Local Getis-OrdGi spatial autocorrelation analysis by ArcGIS software showed that there was 11 cluster areas,3 high incidence areas (Xinjiang,Tibet,Qinghai) and 8 low incidence areas (Beijing,Tianjin,Shanghai,Hebei,Inner Mongolia,Shanxi,Shandong,Jiangsu).Conclusion The incidence of TB had obviously spatial clustering characteristic,the areas at high risk were mainly in the northwestern and plateau area in China.
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Objective To analyze the spatial distribution of the incidence of tuberculosis (TB)in China from 2012 to 2014 and provide evidence for the prevention and control of TB.Methods The database of TB in China from 2012 to 2014 was established by using geographical information system,the spatial distribution map was drawn,trend analysis and spatial autocorrelation analysis were conducted to explore the spatial distribution pattern of TB and identify hot areas.Results The trend surface analysis showed that the incidence of TB decreased gradually from the west to the east in China,and the U type curve could reflect the TB distribution from the south to the north;Global spatial autocorrelation analysis showed the 2012-2014 global Moran's I were 0.366,0.364 and 0.358(P＜0.01),suggesting that the incidence of TB had a spatial clustering in China;Local Getis-OrdGi spatial autocorrelation analysis by ArcGIS software showed that there was 11 cluster areas,3 high incidence areas (Xinjiang,Tibet,Qinghai) and 8 low incidence areas (Beijing,Tianjin,Shanghai,Hebei,Inner Mongolia,Shanxi,Shandong,Jiangsu).Conclusion The incidence of TB had obviously spatial clustering characteristic,the areas at high risk were mainly in the northwestern and plateau area in China.
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Under the available data gathered from a coronary study questionnaires with 10 792 cases,this article constructs a Bayesian network model based on the tabu search algorithm and calculates the conditional probability of each node,using the Maximum-likelihood.Pros and cons of the Bayesian network model are evaluated to compare against the logistic regression model in the analysis of coronary factors.Applicability of this network model in clinical study is also investigated.Results show that Bayesian network model can reveal the complex correlations among influencing factors on the coronary and the relationship with coronary heart diseases.Bayesian network model seems promising and more practical than the logistic regression model in analyzing the influencing factors of coronary heart disease.
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Under the available data gathered from a coronary study questionnaires with 10 792 cases,this article constructs a Bayesian network model based on the tabu search algorithm and calculates the conditional probability of each node,using the Maximum-likelihood.Pros and cons of the Bayesian network model are evaluated to compare against the logistic regression model in the analysis of coronary factors.Applicability of this network model in clinical study is also investigated.Results show that Bayesian network model can reveal the complex correlations among influencing factors on the coronary and the relationship with coronary heart diseases.Bayesian network model seems promising and more practical than the logistic regression model in analyzing the influencing factors of coronary heart disease.
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To observe the influence of the placental apoptosis on the expression of Bax,Bcl-2, Fas, FasLand TNF-α during the second trimester of pregnancy, mice of experimental group were intraperitoneal injected with 100 purified Toxoplasma gondii tachyzoites added in 0.2mL of PBS, while those of the control group were injected with 0.2 mL of sterile PBS (0.01 mol/L, pH 7.4) in the 8-th day of pregnancy. During the 12, 14, 16 and 18-th days of pregnancy, 5 mice both in experimental and control group were randomly killed and the expression levels of the apoptosis-related proteins Bax, Bcl-2, Fas, FasL and TNF-α in the placental tissues were determined by means of immunohistochemical methods. It was showed that the apoptosis-related protein expressed both in villus and decidua of the placenta, most of which were expressed in syneytiotrophoblast (ST). The positive cells with expression of Bax, Fas, FasL and TNF-α increased along with the increase of the pregnant days in both the experimental group and the control group, and the positive cells with expression of Bcl-2 decreased along with the increase of the pregnant days. It was also demonstrated that the positive cells with expression of Bax, Fas, FasL and TNF-α of the experimental group showed a higher percentage of expression than that of the control group on the same pregnant days, but the positive cells with Bcl-2 expression of the experimental group were fewer than that of the control group. It is concluded that the expression of apoptosis-related protein Bax, Bcl-2, Fas, FasL and TNF-α in the placenta were altered when the pregnant mice were infected with Toxoplasma gondii during the second trimester, which may induce the apoptosis through the endogenic and ectogenic pathway.
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Objective To investigate the prevalence rate of hypertension among the elderly population in Shanxi Province. Methods According to the random sampling, 41 residents committees of city blocks and villages of 9 areas (including Taiyuan, Datong, Jinzhong, Yuncheng, et al) of Shanxi Province were extracted as the investigative spots. All the people were older than 55years, and risk factors for hypertension were identified. Results A total of 3702 people were surveyed, including 1,782 men and 1,920 women. The total hypertension prevalence rate was 39.0%(the standardized rate: 39. 5 %), with 38. 7 % in men (the standardized rate: 38. 5%), 39.3 % in women (the standardized rate: 41.2%), and no statistic significance was found between men and women (χ2= 0. 143, P>0. 05). Aging, lacking of knowledge, and obesity were risk factors for hypertension. Conclusions The hypertension in Shanxi Province has a high prevalence rate among the elderly population, a comprehensive intervention should be taken in the prevention of hypertension.
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Objective To comprehend the prevalence rate,awareness rate and the risk factors of hypertension among inhabitants in Shanxi Province. Methods With cluster random sampling,41 committees and villages among nine areas of Shanxi Province were selected as the investigative spots,including Taiyuan,Datong,Jinzhong,Yuncheng and etc. In each spot,permanent residents aged above 15 yrs were investigated on the correlation study of hypertension. Results In this study,10 792 people were investigated,male 4 721 and female 6 071. The prevalent rate of hypertension was 25.4%,the standardized rate was 23.6%,28.0% in male and 23.4% in female. The awareness rate for hypertension was 44.8%,38.6% in male and 50.5% in female. The multivariate Logistic regression analysis showed the risk factors for hypertension in this population included male,the aged people,lacking of knowledge,drinking alcohol,and obesity. Conclusion There is high prevalent rate of hypertension in Shanxi Province. So we should take the measures aiming at the present situation and risk factors of hypertension.