Duarte Juliana Lúcia, Diaz-Quijano Fredi Alexander, Batista Antônio Carlos, Giatti Leandro Luiz
Programa de Pós-Graduação Scrictu Sensu em Ciências, Faculdade de Saúde Pública da Universidade de São Paulo, São Paulo, SP, Brasil.
Departamento de Epidemiologia, Faculdade de Saúde Pública da Universidade de São Paulo, São Paulo, SP, Brasil.
Rev Soc Bras Med Trop. 2019 Feb 21;52:e20180429. doi: 10.1590/0037-8682-0429-2018.
This study aimed to examine the impact of climate variability on the incidence of dengue fever in the city of Rio Branco, Brazil.
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.
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).
Considering the impact of global climate change on the region, these findings can help to predict trends in dengue fever incidence.
本研究旨在考察气候变率对巴西里奥布兰科市登革热发病率的影响。
采用负二项分布的广义自回归移动平均模型，评估登革热月发病率与降水、温度、湿度及阿克里河水位等气候变量之间的关联。对多个无滞后、1个月滞后和2个月滞后模型进行了检验。
无滞后模型显示，登革热发病率与阿克里河水位月平均值（发病率比[IRR]：1.09；95%置信区间[CI]：1.02 - 1.17）、补偿温度（IRR：1.54；95% CI：1.22 - 1.95）和最高温度（IRR：0.68；95% CI：0.58 - 0.81）相关。1个月滞后模型显示，登革热发病率可由总降水量月平均值（IRR：1.21；95% CI：1.06 - 1.39）、最低温度（IRR：1.54；9% CI：1.24 - 1.91）、补偿相对湿度（IRR：0.90；95% CI：0.82 - 0.99）和最高温度（IRR：0.76；95% CI：0.59 - 0.97）预测。2个月滞后模型显示，登革热发病率可由降水天数（IRR：1.03；95% CI：1.00 - 1.06）和最高温度（IRR：1.23；95% CI：1.05 - 1.44）预测。
考虑到全球气候变化对该地区的影响，这些发现有助于预测登革热发病率趋势。
