Abstract:

Objective To analyze the spatial-temporal distribution characteristics of active pulmonary tuberculosis (PTB) in Jinshan District, Shanghai from 2013 to 2017, and to explore the critical regions of TB prevention and control. Methods The basic information of TB patients was collected from the Tuberculosis Management Information System. SaTScan 9.6 software was used to analyze the space-time clusters of the TB patient registration rate at the town level, and the spatial-temporal clusters areas were displayed by ArcGIS 10.4. Results A total of 1021 new active PTB cases were registered during 2013-2017, and the annual PTB registration rates for the whole, for the household registered population and the non-household registered population were 25.64/100000 (1021/3981892), 28.68/100000 (757/2639117) and 19.66/100000 (264/1342775) respectively. In terms of spatial distribution, the town with the highest registration rates were Zhangyan for the whole, and Zhangyan, Caojing and Jinshan Industrial Zone for the household registered population and Langxia and Shihua Street for the non-household registered population. Temporal scan showed that the aggregation time was both 2014-2015 for the whole and the household registered population. Spatial cluster was located in Zhangyan for the whole. Spatial clusters for household registered population were located in the four towns in the northeast of Jinshan District (Tinglin, Caojing, Zhangyan and Jinshan Industrial Zone). The spatial-temporal scan indicated the primary clusters of PTB in Jinshan District (relative risk (RR)=1.5, log-likelihood ratio (LRR)=10.35, P<0.01) and household registered population (RR=1.72, LRR=13.29, P<0.01) were both clustered in Tinglin, Caojing, Zhangyan and Jinshan Industrial Zone during 2014-2015. Conclusion Incidence of PTB displayed certain spatial-temporal clustering in Jinshan District, which is mainly affected by the household registered population, with high risk areas relatively concentrated in the four towns in the northeast of Jinshan District.

Key words: Tuberculosis,pulmonary, Registers, Spatial-temporal clustering analysis, Small-area analysis, Epidemiologic studies