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. 2018 Oct 10:147:e25.
doi: 10.1017/S0950268818002765.

Analysis of spatial-temporal distribution and influencing factors of pulmonary tuberculosis in China, during 2008-2015

Y Zhang  1 , X L Wang  1 , T Feng  1 , C Z Fang  1
Affiliations

Analysis of spatial-temporal distribution and influencing factors of pulmonary tuberculosis in China, during 2008-2015

Y Zhang et al. Epidemiol Infect. .

Abstract

At present, the number of people with tuberculosis in China is second only to India and ranks second in the world. Under such a severe case of tuberculosis in China, prevention and control of pulmonary tuberculosis are urgently needed. This study aimed to study the temporal and geographical relevance of the pathogenesis of pulmonary tuberculosis and the factors affecting the incidence of tuberculosis. Spatial autocorrelation model was used to study the spatial distribution characteristics of pulmonary tuberculosis from a quantitative level. The research results showed that the overall incidence of pulmonary tuberculosis (IPT) in China was low in the east, high in the west and had certain seasonal characteristics. We use Spatial Lag Model to explore influencing factors of pulmonary tuberculosis. It indicates that the IPT is high in areas with underdeveloped economics, poor social services and low average smoking ages. Additionally, the IPT is high in areas with high AIDS prevalence. Also, compared with Classical Regression Model and Spatial Error Model, our model has smaller values of Akaike information criterion and Schwarz criterion. Besides, our model has bigger values of coefficient of determination (R2) and log-likelihood (log L) than the other two models. Apart from that, it is more significant than Spatial Error Models in the spatial dependence test for the IPT.

Keywords: Incidence; influencing factors; pulmonary tuberculosis; spatial aggregation.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Distribution of mean incidence of pulmonary tuberculosis in China during 2008–2015.
Fig. 2.
Fig. 2.
Incidence of pulmonary tuberculosis and number of persons with pulmonary tuberculosis in China during 2008–2015.
Fig. 3.
Fig. 3.
Time trend. (a) Number of persons with pulmonary tuberculosis in 12 months during 2008–2015. (b) Time series of incidence of pulmonary tuberculosis during 2008–2015.
Fig. 4.
Fig. 4.
Results of local Moran index visualisation analysis in 2014.
Fig. 5.
Fig. 5.
Results of local G coefficient visualisation analysis in 2014.
Fig. 6.
Fig. 6.
Results of the hot and cold spot areas in 2014.
See this image and copyright information in PMC

References

    1. WH Organization (2015) Global tuberculosis Report 2015. France: World Health Organization, p. 28.
    1. Xu M et al. (2017) Survey on tuberculosis patients in rural areas in China: tracing the role of stigma in psychological distress. International Journal of Environmental Research and Public Health 14, 10. - PMC - PubMed
    1. Li L et al. (2012) Screening of patients with tuberculosis for diabetes mellitus in China. Trop Medicine & International Health 17, 1294–1301. - PubMed
    1. Ding Y (2016) Research progress on influencing factors and nursing countermeasures of quality of life of patients with pulmonary tuberculosis. Chinese Nursing Research 30, 3601–3603.
    1. Li L, Xi Y and Ren F (2016) Spatio-temporal distribution characteristics and trajectory similarity analysis of tuberculosis in Beijing, China. International Journal of Environmental Research and Public Health 13, 3. - PMC - PubMed