›› 2013, Vol. 33 ›› Issue (3): 340-.doi: 10.3969/j.issn.1674-8115.2013.03.018

• Original article (Preventive medicine) • Previous Articles     Next Articles

Temporal-spatial distribution study on tuberculosis epidemiology in Chongqing between 1998 and 2009

ZHOU Ze-wen1, HU Dai-yu2, LI Qin3, MA Jing-jing1, WANG Run-hua1, YI Jing1   

  1. 1.Department of Health Statistics, School of Public Health and Management, Chongqing Medical University, Chongqing 400016, China； 2.Institute of Tuberculosis Prevention and Treatment of Chongqing, Chongqing 400050, China； 3.Chongqing Center for Disease Control and Prevention, Chongqing 400042, China
  • Online:2013-03-28 Published:2013-03-29
  • Supported by:

    National Natural Science Foundation of China, 30872160；Chongqing Science and Technology Committee Foundation, 2009BB5415

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Abstract:

Objective To describe and analyze the tendency and spatial clustering of tuberculosis in Chongqing between 1998 and 2009, and provide scientific basis for regional public health decisionmaking. Methods The data on prevalences of tuberculosis and population in 40 districts and counties of Chongqing between 1998 and 2009 were collected, and linear regression and clustering analysis were used to describe the spatial and temporal patterns of tuberculosis. Results ① Time trend: The prevalences of tuberculosis in Chongqing exhibited a range of fluctuation, rising from 1998 to 2005, then steadying from 2005. ② Spatial clustering: All the districts and counties were divided into four categories according to the prevalences, and the areas with highest prevalences were Pengshui, Fengdu, Zhongxian, Fuling, Wushan, Wulong, Yuzhong and Wanzhou. One-way ANOVA demonstrated a good clustering effect (F=33.68, P<0.000 1). Conclusion Tuberculosis is still very grim in Chongqing, and the geographic location and economic levels have great impact on the prevalences. The government should increase the financial investment and improve the health resource allocation proportion at high-risk areas.

Key words: tuberculosis, time trend, clustering analysis, space-time analysis