Open AccessDynamic spatiotemporal analysis of indigenous dengue fever at street-level in Guangzhou city, China
Author(s) -
Kangkang Liu,
Yanshan Zhu,
Yao Xia,
Yingtao Zhang,
Xiaodong Huang,
Jiawei Huang,
Enqiong Nie,
Qinlong Jing,
Guoling Wang,
Zhicong Yang,
Wenbiao Hu,
Jiahai Lu
Publication year - 2018
Publication title -
plos neglected tropical diseases
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.99
H-Index - 135
eISSN - 1935-2735
pISSN - 1935-2727
DOI - 10.1371/journal.pntd.0006318
Subject(s) - poisson regression , dengue fever , confidence interval , geography , incidence (geometry) , demography , china , transmission (telecommunications) , population , environmental health , medicine , immunology , mathematics , geometry , archaeology , sociology , engineering , electrical engineering
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 (β = -5.08, P <0.01) and latitude (β = -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.