Spatial patterns and socioecological drivers of dengue fever transmission in Queensland, Australia

Environ Health Perspect. 2012 Feb;120(2):260-6. doi: 10.1289/ehp.1003270. Epub 2011 Oct 20.

Abstract

Background: Understanding how socioecological factors affect the transmission of dengue fever (DF) may help to develop an early warning system of DF.

Objectives: We examined the impact of socioecological factors on the transmission of DF and assessed potential predictors of locally acquired and overseas-acquired cases of DF in Queensland, Australia.

Methods: We obtained data from Queensland Health on the numbers of notified DF cases by local government area (LGA) in Queensland for the period 1 January 2002 through 31 December 2005. Data on weather and the socioeconomic index were obtained from the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. A Bayesian spatial conditional autoregressive model was fitted at the LGA level to quantify the relationship between DF and socioecological factors.

Results: Our estimates suggest an increase in locally acquired DF of 6% [95% credible interval (CI): 2%, 11%] and 61% (95% CI: 2%, 241%) in association with a 1-mm increase in average monthly rainfall and a 1°C increase in average monthly maximum temperature between 2002 and 2005, respectively. By contrast, overseas-acquired DF cases increased by 1% (95% CI: 0%, 3%) and by 1% (95% CI: 0%, 2%) in association with a 1-mm increase in average monthly rainfall and a 1-unit increase in average socioeconomic index, respectively.

Conclusions: Socioecological factors appear to influence the transmission of DF in Queensland, but the drivers of locally acquired and overseas-acquired DF may differ. DF risk is spatially clustered with different patterns for locally acquired and overseas-acquired cases.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem
  • Dengue / epidemiology*
  • Dengue / transmission
  • Hot Temperature
  • Humans
  • Incidence
  • Poisson Distribution
  • Queensland / epidemiology
  • Rain*
  • Regression Analysis
  • Seasons
  • Socioeconomic Factors
  • Travel*