Re-Model the Relation of Vector Indices, Meteorological Factors and Dengue Fever

Abstract

Background: Dengue is the most rapidly expanding and spreading mosquito-borne viral disease in tropical and subtropical countries. In Taiwan, dengue incidence clustered in Southern part, especially Kaohsiung in the past decade. Aim: The spatial and temporal patterns of dengue transmission in Taiwan from 2005 to 2012 were examined to investigate the occurrence of dengue fever (DF) patients and its association with immature and adult mosquito indices, and its interaction with meteorological factors and household density. Methods: Three databases were spatially and temporally linked, including the comprehensive chart records of DF cases and vector surveillance data in Kaohsiung, as well as the meteorological and environmental information from 2005 to 2012. A case-crossover study design was used to explore the effects of mosquito indices and weather on the risks of DF, and conditional logistic regression was applied to estimate the odds ratios (OR). Results: Results showed immature mosquito indices had significant positive association with DF in the medium and high household density areas (e.g., adjusted ORs of Breteau index were 1.04, 95% CI=[1.02, 1.06] and 1.06, CI=[1.04, 1.08] respectively), while adult mosquito index was significant to all low/med/high household densities (adjusted ORs of Aedes aegypti index were 1.29, CI=[1.23,1.36]; 1.49, CI=[1.37,1.61] and 1.3, CI=[1.21,1.39] respectively). Meanwhile, combination with 2-week lag rainfall, 2-month lag rainfall, 2-week lag temperature and relative humidity, resulted better prediction of DF incidence. Conclusion: Meteorological conditions affect DF occurrence in a nonlinear way, and a single time-point rainfall variable is insufficient to fit it. Our study suggested that short-lag (last 2 weeks) conditions of moderate rainfall, moderate temperature and high humidity, in combination with a long-lag heavy rainfall were related to higher probability of DF incidence. BI and CI are useful predictors for DF occurrence in medium and high household density areas, but not in the low density areas.