Climatic variables associated with dengue incidence in a city of the Western Brazilian Amazon region

Abstract INTRODUCTION: This study aimed to examine the impact of climate variability on the incidence of dengue fever in the city of Rio Branco, Brazil. METHODS: The association between the monthly incidence of dengue fever and climate variables such as precipitation, temperature, humidity, and the...

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Bibliographic Details
Published in:Revista da Sociedade Brasileira de Medicina Tropical
Main Authors: Juliana Lúcia Duarte, Fredi Alexander Diaz-Quijano, Antônio Carlos Batista, Leandro Luiz Giatti
Format: Article in Journal/Newspaper
Language:English
Published: Sociedade Brasileira de Medicina Tropical (SBMT) 2019
Subjects:
Dengue
Climate
Climate change
Amazonian ecosystem
Brazil
Arctic medicine. Tropical medicine
RC955-962
Arctic
Rio Branco
Online Access:https://doi.org/10.1590/0037-8682-0429-2018
https://doaj.org/article/b50948e13db94eb89ed8faac3bc56d97
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Summary:Abstract INTRODUCTION: This study aimed to examine the impact of climate variability on the incidence of dengue fever in the city of Rio Branco, Brazil. METHODS: The association between the monthly incidence of dengue fever and climate variables such as precipitation, temperature, humidity, and the Acre River level was evaluated, using generalized autoregressive moving average models with negative binomial distribution. Multiple no-lag, 1-month lag, and 2-month lag models were tested. RESULTS: The no-lag model showed that the incidence of dengue fever was associated with the monthly averages of the Acre River level (incidence rate ratio [IRR]: 1.09; 95% confidence interval [CI]: 1.02-1.17), compensated temperature (IRR: 1.54; 95% CI: 1.22-1.95), and maximum temperature (IRR: 0.68; 95% CI: 0.58-0.81). The 1-month lag model showed that the incidence of dengue fever was predicted by the monthly averages of total precipitation (IRR: 1.21; 95% CI: 1.06-1.39), minimum temperature (IRR: 1.54; 95% CI: 1.24-1.91), compensated relative humidity (IRR: 0.90; 95% CI: 0.82-0.99), and maximum temperature (IRR: 0.76; 95% CI: 0.59-0.97). The 2-month lag model showed that the incidence of dengue fever was predicted by the number of days with precipitation (IRR: 1.03; 95% CI: 1.00-1.06) and maximum temperature (IRR: 1.23; 95% CI: 1.05-1.44). CONCLUSIONS: Considering the impact of global climate change on the region, these findings can help to predict trends in dengue fever incidence.

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