Using the Kulldorff's scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009-2016

Although the incidence of tuberculosis (TB) in most parts of China are well under control now, in less developed areas such as Qinghai, TB still remains a major public health problem. This study aims to reveal the spatio-temporal patterns of TB in the Qinghai province, which could be helpful in the...

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Bibliographic Details
Published in:BMC infectious diseases 2017-08, Vol.17 (1), p.578-578, Article 578
Main Authors: Rao, Huaxiang, Shi, Xinyu, Zhang, Xi
Format: Article
Language:English
Subjects:
Analysis
China - epidemiology
Cluster Analysis
Control
Data Interpretation, Statistical
Disease control
Epidemiology
Humans
Incidence
Infectious diseases
Information systems
Malaria
Measurement methods
Population
Prevention
Public health
Qinghai
Retrospective Studies
Risk factors
Risk management
SaTScan
Scan statistics
Space-time clustering
Spacetime
Spatial distribution
Spatio-Temporal Analysis
Statistical analysis
Temporal distribution
Tuberculosis
Tuberculosis - epidemiology
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Summary:Although the incidence of tuberculosis (TB) in most parts of China are well under control now, in less developed areas such as Qinghai, TB still remains a major public health problem. This study aims to reveal the spatio-temporal patterns of TB in the Qinghai province, which could be helpful in the planning and implementing key preventative measures. We extracted data of reported TB cases in the Qinghai province from the China Information System for Disease Control and Prevention (CISDCP) during January 2009 to December 2016. The Kulldorff's retrospective space-time scan statistics, calculated by using the discrete Poisson probability model, was used to identify the temporal, spatial, and spatio-temporal clusters of TB at the county level in Qinghai. A total of 48,274 TB cases were reported from 2009 to 2016 in Qinghai. Results of the Kulldorff's scan revealed that the TB cases in Qinghai were significantly clustered in spatial, temporal, and spatio-temporal distribution. The most likely spatio-temporal cluster (LLR = 2547.64, RR = 4.21, P 
ISSN:1471-2334
1471-2334
DOI:10.1186/s12879-017-2643-y