Dynamic spatiotemporal analysis of indigenous dengue fever at street-level in Guangzhou city, China

作者：Liu, Kangkang; Zhu, Yanshan; Xia, Yao; Zhang, Yingtao; Huang, Xiaodong; Huang, Jiawei; Nie, Enqiong; Jing, Qinlong; Wang, Guoling; Yang, Zhicong^*; Hu, Wenbiao^*; Lu, Jiahai^*

来源：PLoS Neglected Tropical Diseases, 2018, 12(3): e0006318.

DOI：10.1371/journal.pntd.0006318

• METEOROLOGICAL FACTORS
• GUANGDONG PROVINCE
• VIRUS-INFECTION
• QUEENSLAND
• PREVALENCE
• KNOWLEDGE
• BRAZIL

Background @@@ This study aimed to investigate the spatiotemporal clustering and socio-environmental factors associated with dengue fever (DF) incidence rates at street level in Guangzhou city, China. @@@ Methods @@@ Spatiotemporal scan technique was applied to identify the high risk region of DF. Multiple regression model was used to identify the socio-environmental factors associated with DF infection. A Poisson regression model was employed to examine the spatiotemporal patterns in the spread of DF. @@@ Results @@@ Spatial clusters of DF were primarily concentrated at the southwest part of Guangzhou city. Age group (65+ years) (Odd Ratio (OR) = 1.49, 95% Confidence Interval (CI) = 1.13 to 2.03), floating population (OR = 1.09, 95% CI = 1.05 to 1.15), low-education (OR = 1.08, 95% CI = 1.01 to 1.16) and non-agriculture (OR = 1.07, 95% CI = 1.03 to 1.11) were associated with DF transmission. Poisson regression results indicated that changes in DF incidence rates were significantly associated with longitude (beta = -5.08, P<0.01) and latitude (beta = -1.99, P<0.01). @@@ Conclusions @@@ The study demonstrated that social-environmental factors may play an important role in DF transmission in Guangzhou. As geographic range of notified DF has significantly expanded over recent years, an early warning systems based on spatiotemporal model with socio-environmental is urgently needed to improve the effectiveness and efficiency of dengue control and prevention.

• 出版日期2018-3
• 单位广州市疾病预防控制中心; 中山大学