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

PLoS Negl Trop Dis. 2018 Mar 21;12(3):e0006318. doi: 10.1371/journal.pntd.0006318. eCollection 2018 Mar.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Child
  • Child, Preschool
  • China / epidemiology
  • Cities
  • Cluster Analysis
  • Dengue / epidemiology*
  • Dengue / transmission
  • Disease Outbreaks / prevention & control
  • Humans
  • Incidence
  • Infant
  • Infant, Newborn
  • Middle Aged
  • Regression Analysis
  • Risk Factors
  • Spatio-Temporal Analysis*
  • Young Adult

Grants and funding

The work was supported by the National Natural Science Foundation of China (grant No. 81373050, URLS: http://www.nsfc.gov.cn/, JL). The Municipal Healthcare Joint-Innovation Major Project of Guangzhou, China (grant No. 201604020011) and the Science and Technology Program of Guangzhou, China (grant No. 201508020062, URLs: http://www.gzsi.gov.cn/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.