Spatial-temporal analysis on pulmonary tuberculosis in Beijing during 2005-2015.
10.3760/cma.j.issn.0254-6450.2018.06.023
- Author:
S H SUN
1
,
2
;
Z D GAO
2
;
F ZHAO
3
,
4
;
W Y ZHANG
5
;
X ZHAO
2
;
Y Y LI
2
;
Y M LI
2
;
F HONG
2
;
X X HE
2
;
S Y ZHAN
6
Author Information
1. The Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
2. Beijing Research Institute for Tuberculosis Control, Beijing 100035, China.
3. Beijing Research Institute for Tuberculosis Control, Beijing 100035, China
4. National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
5. Institute of Disease Control and Prevention of the People's Liberation Army, Beijing 100071, China.
6. The Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
- Publication Type:Journal Article
- Keywords:
Pulmonary;
Spatial autocorrelation;
Spatial-temporal clustering;
Tuberculosis
- MeSH:
Beijing;
China;
Cluster Analysis;
Humans;
Incidence;
Spatial Analysis;
Spatio-Temporal Analysis;
Tuberculosis;
Tuberculosis, Pulmonary/ethnology*
- From:
Chinese Journal of Epidemiology
2018;39(6):816-820
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To analyze the spatial distribution and identify the high risk areas of pulmonary tuberculosis at the township level in Beijing during 2005-2015. Methods: Data on pulmonary tuberculosis cases was collected from the tuberculosis information management system. Global autocorrelation analysis, local indicators of spatial association and Kulldorff's Scan Statistics were applied to map the spatial distribution and detect the space-time clusters of the pulmonary tuberculosis cases during 2005-2015. Results: Spatial analysis on the incidence of pulmonary tuberculosis at the township level demonstrated that the spatial autocorrelation was positive during the study period. The values of Moran's I ranged from 0.224 3 to 0.291 8 with all the P values less than 0.05. Hotspots were primarily distributed in 8 towns/streets as follows: Junzhuang, Wangping, Yongding and Tanzhesi in Mentougou district, Yancun in Fangshan district, Wangzuo town in Fengtai district, Tianqiao street in Xicheng district and Tianzhu town in Shunyi district. Spatiotemporal clusters across the entire study period were identified by using Kulldorff's spatiotemporal scan statistic. The primary cluster was located in Chaoyang and Shunyi districts, including 17 towns/streets, as follows: Cuigezhuang, Maizidian, Dongfeng, Taiyanggong, Zuojiazhuang, Hepingjie, Xiaoguan, Xiangheyuan, Dongba, Jiangtai, Wangjing, Jinzhan, Jiuxianqiao, Laiguangying, Sunhe towns/streets in Chaoyang district, Houshayu and Tianzhu town in Shunyi district, during January to December 2005. Conclusion: Incidence rates of pulmonary tuberculosis displayed spatial and temporal clusterings at the township level in Beijing during 2005-2015, with high risk areas relatively concentrated in the central and southern parts of Beijing.
