Dengue epidemic typology and risk factors for extensive epidemic in Amazonas state, Brazil, 2010-2011

BMC Public Health. 2018 Mar 15;18(1):356. doi: 10.1186/s12889-018-5251-x.

Abstract

Background: Dengue is the most prevalent arboviral disease affecting humans. The frequency and magnitude of dengue epidemic have significantly increased over recent decades. This study aimed to identify dengue epidemic types and risk factors for the extensive epidemics that occurred in 2010-2011, across the municipalities of Amazonas state, Brazil.

Methods: Using an ecological approach, secondary data were obtained from the dengue fever surveillance system. Epidemic waves were classified according to three indices: duration, intensity, and coverage. A hierarchical model of multiple logistic regression was used for the identification of risk factors, with the occurrence of extensive dengue epidemic.

Results: During the study period, dengue virus affected 49 of the 62 Amazonas municipalities. In 22 of these, the epidemics were of high intensity, wide range, and long time span, and therefore categorized as "extensive epidemics". The final multivariable model revealed a significant association between extensive dengue epidemics occurrence and the average number of days with precipitation (adjusted OR = 1.40, 95% CI: 1.01-1.94) and the number of years with infestation (adjusted OR = 1.53, 95% CI: 1.18-1.98).

Conclusions: Our results indicate that it is crucial to integrate vector control, case management, epidemiological investigation, and health education, in order to respond to the growing threat of multiple mosquito-borne diseases, such as dengue, Zika and chikungunya, which are highly prevalent in the South America region.

Keywords: Amazon; Climate; Dengue epidemics classification; Environment; Socioeconomic.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Brazil / epidemiology
  • Cities
  • Dengue / epidemiology*
  • Dengue Virus / isolation & purification
  • Epidemics / classification*
  • Epidemics / statistics & numerical data*
  • Humans
  • Multivariate Analysis
  • Rain
  • Risk Factors
  • Time Factors