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Spatiotemporal patterns and ecological factors of tuberculosis notification: A spatial panel data analysis in Guangxi, China

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  • Zhezhe Cui
  • Dingwen Lin
  • Virasakdi Chongsuvivatwong
  • Jinming Zhao
  • Mei Lin
  • Jing Ou
  • Jinghua Zhao

Abstract

Background: Guangxi is one of the provinces having the highest notification rate of tuberculosis in China. However, spatial and temporal patterns and the association between environmental diversity and tuberculosis notification are still unclear. Objective: To detect the spatiotemporal pattern of tuberculosis notification rates from 2010 to 2016 and its potential association with ecological environmental factors in Guangxi Zhuang autonomous region, China. Methods: We performed a spatiotemporal analysis with prediction using time series analysis, Moran’s I global and local spatial autocorrelation statistics, and space-time scan statistics to detect temporal and spatial clusters of tuberculosis notifications in Guangxi between 2010 and 2016. Spatial panel models were employed to identify potential associating factors. Results: The number of reported cases peaked in spring and summer and decreased in autumn and winter. The predicted number of reported cases was 49,946 in 2017. Moran's I global statistics were greater than 0 (0.363–0.536) during the study period. The most significant hot spots were mainly located in the central area. The eastern area exhibited a low-low relation. By the space-time scanning, the clusters identified were similar to those of the local autocorrelation statistics, and were clustered toward the early part of 2016. Duration of sunshine, per capita gross domestic product, the treatment success rate of tuberculosis and participation rate of the new cooperative medical care insurance scheme in rural areas had a significant negative association with tuberculosis notification rates. Conclusion: The notification rate of tuberculosis in Guangxi remains high, with the highest notification cluster located in the central region. The notification rate is associated with economic level, treatment success rate and participation in the new cooperative medical care insurance scheme.

Suggested Citation

  • Zhezhe Cui & Dingwen Lin & Virasakdi Chongsuvivatwong & Jinming Zhao & Mei Lin & Jing Ou & Jinghua Zhao, 2019. "Spatiotemporal patterns and ecological factors of tuberculosis notification: A spatial panel data analysis in Guangxi, China," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-15, May.
    • Handle: RePEc:plo:pone00:0212051
    DOI: 10.1371/journal.pone.0212051
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    References listed on IDEAS

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    1. Xufeng Fei & Zhaohan Lou & George Christakos & Qingmin Liu & Yanjun Ren & Jiaping Wu, 2016. "A Geographic Analysis about the Spatiotemporal Pattern of Breast Cancer in Hangzhou from 2008 to 2012," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-13, January.
    2. Millo, Giovanni & Piras, Gianfranco, 2012. "splm: Spatial Panel Data Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i01).
    3. Kulldorff, M. & Athas, W.F. & Feuer, E.J. & Miller, B.A. & Key, C.R., 1998. "Evaluating cluster alarms: A space-time scan statistic and brain cancer in Los Alamos, New Mexico," American Journal of Public Health, American Public Health Association, vol. 88(9), pages 1377-1380.
    4. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    5. Aaron Cohen & Sumi Mehta, 2007. "Pollution and Tuberculosis: Outdoor Sources," PLOS Medicine, Public Library of Science, vol. 4(3), pages 1-1, March.
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    1. Zhezhe Cui & Dingwen Lin & Virasakdi Chongsuvivatwong & Edward A. Graviss & Angkana Chaiprasert & Prasit Palittapongarnpim & Mei Lin & Jing Ou & Jinming Zhao, 2019. "Hot and Cold Spot Areas of Household Tuberculosis Transmission in Southern China: Effects of Socio-Economic Status and Mycobacterium tuberculosis Genotypes," IJERPH, MDPI, vol. 16(10), pages 1-18, May.
    2. Syed Ali Asad Naqvi & Muhammad Sajjad & Liaqat Ali Waseem & Shoaib Khalid & Saima Shaikh & Syed Jamil Hasan Kazmi, 2021. "Integrating Spatial Modelling and Space–Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan," IJERPH, MDPI, vol. 18(22), pages 1-30, November.

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