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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

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

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Autor(es):
Duarte, Juliana Lucia [1] ; Diaz-Quijano, Fredi Alexander [2] ; Batista, Antonio Carlos [3] ; Giatti, Leandro Luiz [4]
Número total de Autores: 4
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Fac Saude Publ, Progama Posgrad Scrietu Sensu Ciencias, Sao Paulo, SP - Brazil
[2] Univ Sao Paulo, Fac Saude Publ, Dept Epidemiol, Sao Paulo, SP - Brazil
[3] Univ Fed Parana, Dept Engn Florestal, Curitiba, Parana - Brazil
[4] Univ Sao Paulo, Fac Saude Publ, Dept Saude Ambiemal, Sao Paulo, SP - Brazil
Número total de Afiliações: 4
Tipo de documento: Artigo Científico
Fonte: Revista da Sociedade Brasileira de Medicina Tropical; v. 52, 2019.
Citações Web of Science: 1
Resumo

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. (AU)

Processo FAPESP: 15/50132-6 - Resiliência e vulnerabilidade quanto ao nexo urbano de alimentos, água, energia e ambiente (ResNexus)
Beneficiário:Leandro Luiz Giatti
Linha de fomento: Auxílio à Pesquisa - Temático