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. 2016 Mar 7;13(3):291.
doi: 10.3390/ijerph13030291.

Spatio-Temporal Distribution Characteristics and Trajectory Similarity Analysis of Tuberculosis in Beijing, China

Lan Li  1   2   3 , Yuliang Xi  4   5   6 , Fu Ren  7   8   9   10
Affiliations

Spatio-Temporal Distribution Characteristics and Trajectory Similarity Analysis of Tuberculosis in Beijing, China

Lan Li et al. Int J Environ Res Public Health. .

Abstract

Tuberculosis (TB) is an infectious disease with one of the highest reported incidences in China. The detection of the spatio-temporal distribution characteristics of TB is indicative of its prevention and control conditions. Trajectory similarity analysis detects variations and loopholes in prevention and provides urban public health officials and related decision makers more information for the allocation of public health resources and the formulation of prioritized health-related policies. This study analysed the spatio-temporal distribution characteristics of TB from 2009 to 2014 by utilizing spatial statistics, spatial autocorrelation analysis, and space-time scan statistics. Spatial statistics measured the TB incidence rate (TB patients per 100,000 residents) at the district level to determine its spatio-temporal distribution and to identify characteristics of change. Spatial autocorrelation analysis was used to detect global and local spatial autocorrelations across the study area. Purely spatial, purely temporal and space-time scan statistics were used to identify purely spatial, purely temporal and spatio-temporal clusters of TB at the district level. The other objective of this study was to compare the trajectory similarities between the incidence rates of TB and new smear-positive (NSP) TB patients in the resident population (NSPRP)/new smear-positive TB patients in the TB patient population (NSPTBP)/retreated smear-positive (RSP) TB patients in the resident population (RSPRP)/retreated smear-positive TB patients in the TB patient population (RSPTBP) to detect variations and loopholes in TB prevention and control among the districts in Beijing. The incidence rates in Beijing exhibited a gradual decrease from 2009 to 2014. Although global spatial autocorrelation was not detected overall across all of the districts of Beijing, individual districts did show evidence of local spatial autocorrelation: Chaoyang and Daxing were Low-Low districts over the six-year period. The purely spatial scan statistics analysis showed significant spatial clusters of high and low incidence rates; the purely temporal scan statistics showed the temporal cluster with a three-year period from 2009 to 2011 characterized by a high incidence rate; and the space-time scan statistics analysis showed significant spatio-temporal clusters. The distribution of the mean centres (MCs) showed that the general distributions of the NSPRP MCs and NSPTBP MCs were to the east of the incidence rate MCs. Conversely, the general distributions of the RSPRP MCs and the RSPTBP MCs were to the south of the incidence rate MCs. Based on the combined analysis of MC distribution characteristics and trajectory similarities, the NSP trajectory was most similar to the incidence rate trajectory. Thus, more attention should be focused on the discovery of NSP patients in the western part of Beijing, whereas the northern part of Beijing needs intensive treatment for RSP patients.

Keywords: Beijing; scan statistics; spatial autocorrelation; trajectory similarity; tuberculosis (TB).

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Figures

Figure 1
Figure 1
Map of the administrative districts of Beijing according to their different urban functions and their locations in China.
Figure 2
Figure 2
Map of the IRs and average IRs of TB from 2009 to 2014 at the district level in Beijing.
Figure 3
Figure 3
The average IRs of TB overall across the districts of Beijing from 2009 to 2014.
Figure 4
Figure 4
LISA cluster map of Beijing from 2009 to 2014.
Figure 5
Figure 5
High and low IR spatial clusters in Beijing from 2009 to 2014.
Figure 6
Figure 6
High and low IR spatio-temporal clusters in Beijing from 2009 to 2014.
Figure 7
Figure 7
The distributions of IR and NSPRP MCs in Beijing from 2010 to 2014.
Figure 8
Figure 8
The distributions of IR and NSPTBP MC in Beijing from 2010 to 2014.
Figure 9
Figure 9
The distributions of IR and RSPRP MC in Beijing from 2010 to 2014.
Figure 10
Figure 10
The distributions of IR and RSPTBP MC in Beijing from 2010 to 2014.
See this image and copyright information in PMC

References

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