Y Liu1, X Li, W Wang, Z Li, M Hou, Y He, W Wu, H Wang, H Liang, X Guo. 1. Department of Epidemiology and Health Statistics, School of Public Health and Family Medicine, Capital Medical University, Beijing Key Laboratory of Epidemiology, Beijing, China.
Abstract
OBJECTIVE: To characterise the geographic and spatiotemporal distribution of confirmed tuberculosis (TB) cases in Beijing between 2005 and 2009. DESIGN: The yearly notification rate maps were used to describe the distribution of confirmed adult TB patients. Spatial autocorrelation (Moran's I) and hot-spot analysis were adopted to detect the clusters and hot spots of TB. RESULTS: The TB incidence rate (cases per 100,000 population) in Beijing increased from 29.8 in 2005 to 35.0 in 2009. The incidence rate was significantly higher in the Urban Development New District and the Ecologic Reservation Development District (>30/100,000) than in the other districts. There was a significant spatial autocorrelation throughout the city (u = 2.58, P = 0.01). Evident clusters were observed in the Capital Functional Core District and the Urban Function Extension District (G(i)* > 1). CONCLUSION: Spatial autocorrelation and hot-spot analysis may serve as efficient tools to detect space-time clusters and geospatial hot spots of TB incidence. Between 2005 and 2009, TB incidence in Beijing showed population density and mobility-dependent and eco-social status-dependent space-time clusters and geospatial hot spots.
OBJECTIVE: To characterise the geographic and spatiotemporal distribution of confirmed tuberculosis (TB) cases in Beijing between 2005 and 2009. DESIGN: The yearly notification rate maps were used to describe the distribution of confirmed adult TB patients. Spatial autocorrelation (Moran's I) and hot-spot analysis were adopted to detect the clusters and hot spots of TB. RESULTS: The TB incidence rate (cases per 100,000 population) in Beijing increased from 29.8 in 2005 to 35.0 in 2009. The incidence rate was significantly higher in the Urban Development New District and the Ecologic Reservation Development District (>30/100,000) than in the other districts. There was a significant spatial autocorrelation throughout the city (u = 2.58, P = 0.01). Evident clusters were observed in the Capital Functional Core District and the Urban Function Extension District (G(i)* > 1). CONCLUSION: Spatial autocorrelation and hot-spot analysis may serve as efficient tools to detect space-time clusters and geospatial hot spots of TB incidence. Between 2005 and 2009, TB incidence in Beijing showed population density and mobility-dependent and eco-social status-dependent space-time clusters and geospatial hot spots.
Authors: Gehendra Mahara; Chao Wang; Kun Yang; Sipeng Chen; Jin Guo; Qi Gao; Wei Wang; Quanyi Wang; Xiuhua Guo Journal: Int J Environ Res Public Health Date: 2016-11-04 Impact factor: 3.390
Authors: Li Huang; Xin-Xu Li; Eniola Michael Abe; Lin Xu; Yao Ruan; Chun-Li Cao; Shi-Zhu Li Journal: Infect Dis Poverty Date: 2017-03-24 Impact factor: 4.520