Using the Kulldorff's scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009-2016.
BMC Infect Dis
; 17(1): 578, 2017 08 21.
Article
in En
| MEDLINE
| ID: mdl-28826399
BACKGROUND: 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. METHODS: 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. RESULTS: 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 < 0.001) was mainly concentrated in the southwest of Qinghai, covering seven counties and clustered in the time frame from September 2014 to December 2016. CONCLUSION: This study identified eight significant space-time clusters of TB in Qinghai from 2009 to 2016, which could be helpful in prioritizing resource assignment in high-risk areas for TB control and elimination in the future.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Tuberculosis
/
Spatio-Temporal Analysis
Type of study:
Incidence_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Country/Region as subject:
Asia
Language:
En
Journal:
BMC Infect Dis
Journal subject:
DOENCAS TRANSMISSIVEIS
Year:
2017
Document type:
Article
Affiliation country:
China
Country of publication:
United kingdom