Spatiotemporal patterns and ecological factors of tuberculosis notification: A spatial panel data analysis in Guangxi, China
China
Ecology
Science
Q
R
3. Good health
03 medical and health sciences
Spatio-Temporal Analysis
0302 clinical medicine
8. Economic growth
Medicine
Tuberculosis
Research Article
DOI:
10.1371/journal.pone.0212051
Publication Date:
2019-05-02T13:38:58Z
AUTHORS (7)
ABSTRACT
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.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.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.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.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.
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