Spatial analyses of typhoid fever in Jiangsu province, People’s Republic of China

  • Yue-Jia Cheng Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.
  • Fen-Yang Tang Jiangsu Province Center for Disease Control and Prevention, Nanjing, China.
  • Chang-Jun Bao Jiangsu Province Center for Disease Control and Prevention, Nanjing, China.
  • Ye-Fei Zhu Jiangsu Province Center for Disease Control and Prevention, Nanjing, China.
  • Qi Liang Jiangsu Province Center for Disease Control and Prevention, Nanjing, China.
  • Jian-Li Hu Jiangsu Province Center for Disease Control and Prevention, Nanjing, China.
  • Wen-Dong Liu Jiangsu Province Center for Disease Control and Prevention, Nanjing, China.
  • Ying Wu Jiangsu Province Center for Disease Control and Prevention, Nanjing, China.
  • Kathleen H. Reilly Tulane University Health Sciences Center, School of Public Health and Tropical Medicine, New Orleans, LA, United States.
  • Tong-Qian Shen Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.
  • Yang Zhao Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.
  • Zhi-Hang Peng | zhihangpeng@njmu.edu.cn Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.
  • Rong-Bin Yu Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.
  • Hua Wang Jiangsu Province Center for Disease Control and Prevention, Nanjing, China.
  • Hong-Bing Shen Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.
  • Feng Chen Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.

Abstract

An analysis of the geographical distribution of typhoid incidence rates, based on various statistical approaches such as trend surface, spatial autocorrelation, spatial correlation and spatial regression, was carried out at the county level in Jiangsu province, People’s Republic of China. Temperature, moisture content, proximity to water bodies and the normalized difference vegetation index in the autumn were the four underlying factors found to contribute the most to the development of the epidemic. Typhoid infection was most severe in the south-eastern region of Jiangsu and a significant hotspot with high positive autocorrelation was detected in Taicang county in the south-east of the province. To improve the typhoid situation, intervention efforts should be concentrated in the south-eastern region of the province, targeting the hotspot and include reduction of lake pollution.

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Published
2013-05-01
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Original Articles
Keywords:
geographical information systems, typhoid fever, trend surface analysis, spatial analysis, spatial regression, People’s Republic of China.
Statistics
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How to Cite
Cheng, Y.-J., Tang, F.-Y., Bao, C.-J., Zhu, Y.-F., Liang, Q., Hu, J.-L., Liu, W.-D., Wu, Y., Reilly, K. H., Shen, T.-Q., Zhao, Y., Peng, Z.-H., Yu, R.-B., Wang, H., Shen, H.-B., & Chen, F. (2013). Spatial analyses of typhoid fever in Jiangsu province, People’s Republic of China. Geospatial Health, 7(2), 279-288. https://doi.org/10.4081/gh.2013.86

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