[Epidemiological characteristics and Spatial-temporal clustering of hand, foot and mouth disease in Shanxi province, 2009-2020].

Objective: To analyze the epidemiology and spatial-temporal distribution characteristics of hand, foot and mouth disease (HFMD) in Shanxi province. Methods: The data of HFMD in Shanxi province from 2009 to 2020 were collected from notifiable disease management information system of Chinese information system for disease control and prevention and analyzed by descriptive epidemiology, Joinpoint regression, spatial autocorrelation analysis and spatio- temporal scanning analysis. Results: A total of 293 477 HFMD cases were reported in Shanxi province from 2009 to 2020, with an average annual incidence of 67.64/100 000 (293 477/433 867 454), severe disease rate of 5.36/100 000 (2 326/433 867 454), severe disease ratio of 0.79%(2 326/293 477), mortality of 0.015/100 000 (66/433 867 454), and fatality rate of 22.49/100 000 (66/293 477). The reported incidence rate, severe disease rate, mortality rate and fatality rate of HFMD showed decreasing trends. The main high-risk groups were scattered children and kindergarten children aged 0-5. The incidence of HFMD had obvious seasonal variation, with two peaks every year: the main peak was during June-July, the secondary peak was during September-October and the peak period is from April to November. A total of 13 942 laboratory cases were confirmed, with a diagnosis rate of 4.75% (13 942/293 477), including 4 438 (35.11%, 4 438/293 477) Enterovirus A71 (EV-A71) positive cases, 4 609 (33.06%, 4 609/293 477) Coxsackievirus A16 (CV-A16) positive cases, and 4 895 (31.83%, 4 895/293 477) other enterovirus positive cases. There was a spatial positive correlation (Moran's I ranged from 0.12 to 0.58, all P<0.05) and the spatial clustering was obvious. High-risk regions were mainly distributed in Taiyuan in central Shanxi province, Linfen and Yuncheng in southern Shanxi province, and Changzhi in southeastern Shanxi province. Spatial-temporal scanning analysis revealed 1 the most likely cluster and 8 secondary likely clusters, of which the most likely cluster (RR=2.65, LLR=22 387.42, P<0.001) located in Taiyuan and Jinzhong city, Shanxi province, including 12 counties (districts), and accumulated from April 1, 2009 to November 30, 2018. Conclusions: There was obvious spatial-temporal clustering of HFMD in Shanxi province, and the epidemic situation was in decline. The key areas were the districts in urban areas and the counties adjacent to it. Meanwhile, the monitoring and classification of other enterovirus types of HFMD should be strengthened.

[Spatial distribution characteristics of tuberculosis and its visualization in Qinghai province, 2014-2016].

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(i)(*) 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 (trend χ(2)=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 G(i)(*) 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.

[Analysis on spatial distribution of tuberculosis in China, 2012-2014].

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-OrdG(i) 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.

[Using the Tabu-search-algorithm-based Bayesian network to analyze the risk factors of coronary heart diseases].

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.