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Association between Multidrug-Resistant Tuberculosis and Risk Factors in China: Applying Partial Least Squares Path Modeling

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  • Yun-Xia Liu
  • Chun-Kun Pang
  • Yanxun Liu
  • Xiu-Bin Sun
  • Xin-Xu Li
  • Shi-Wen Jiang
  • Fuzhong Xue

Abstract

Background: Multidrug-resistant tuberculosis (MDR-TB) resulting from various factors has raised serious public health concerns worldwide. Identifying the ecological risk factors associated with MDR-TB is critical to its prevention and control. This study aimed to explore the association between the development of MDR-TB and the risk factors at the group-level (ecological risk factors) in China. Methods: Data on MDR-TB in 120 counties were obtained from the National Tuberculosis Information Management System, and data on risk-factor variables were extracted from the Health Statistical Yearbook, provincial databases, and the meteorological bureau of each province (municipality). Partial Least Square Path Modeling was used to detect the associations. Results: The median proportion of MDR-TB in new TB cases was 3.96% (range, 0–39.39%). Six latent factors were extracted from the ecological risk factors, which explained 27.60% of the total variance overall in the prevalence of MDR-TB. Based on the results of PLS-PM, TB prevention, health resources, health services, TB treatment, TB detection, geography and climate factors were all associated with the risk of MDR-TB, but socioeconomic factors were not significant. Conclusions: The development of MDR-TB was influenced by TB prevention, health resources, health services, TB treatment, TB detection, geography and climate factors. Such information may help us to establish appropriate public health intervention strategies to prevent and control MDR-TB and yield benefits to the entire public health system in China.

Suggested Citation

  • Yun-Xia Liu & Chun-Kun Pang & Yanxun Liu & Xiu-Bin Sun & Xin-Xu Li & Shi-Wen Jiang & Fuzhong Xue, 2015. "Association between Multidrug-Resistant Tuberculosis and Risk Factors in China: Applying Partial Least Squares Path Modeling," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-13, May.
    • Handle: RePEc:plo:pone00:0128298
    DOI: 10.1371/journal.pone.0128298
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    Cited by:

    1. Kai Cao & Kun Yang & Chao Wang & Jin Guo & Lixin Tao & Qingrong Liu & Mahara Gehendra & Yingjie Zhang & Xiuhua Guo, 2016. "Spatial-Temporal Epidemiology of Tuberculosis in Mainland China: An Analysis Based on Bayesian Theory," IJERPH, MDPI, vol. 13(5), pages 1-8, May.

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