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01.12.2017 | Research article | Ausgabe 1/2017 Open Access

BMC Infectious Diseases 1/2017

Seasonal patterns of dengue fever and associated climate factors in 4 provinces in Vietnam from 1994 to 2013

Zeitschrift:
BMC Infectious Diseases > Ausgabe 1/2017
Autoren:
Hu Suk Lee, Hung Nguyen-Viet, Vu Sinh Nam, Mihye Lee, Sungho Won, Phuc Pham Duc, Delia Grace

Abstract

Background

In Vietnam, dengue fever (DF) is still a leading cause of hospitalization. The main objective of this study was to evaluate the seasonality and association with climate factors (temperature and precipitation) on the incidences of DF in four provinces where the highest incidence rates were observed from 1994 to 2013 in Vietnam.

Methods

Incidence rates (per 100,000) were calculated on a monthly basis from during the study period. The seasonal-decomposition procedure based on loess (STL) was used in order to assess the trend and seasonality of DF. In addition, a seasonal cycle subseries (SCS) plot and univariate negative binomial regression (NBR) model were used to evaluate the monthly variability with statistical analysis. Lastly, a generalized estimating equation (GEE) was used to assess the relationship between monthly incidence rates and weather factors (temperature and precipitation).

Results

We found that increased incidence rates were observed in the second half of each year (from May through December) which is the rainy season in each province. In Hanoi, the final model showed that 1 °C rise of temperature corresponded to an increase of 13% in the monthly incidence rate of DF. In Khanh Hoa, the final model displayed that 1 °C increase in temperature corresponded to an increase of 17% while 100 mm increase in precipitation corresponded to an increase of 11% of DF incidence rate. For Ho Chi Minh City, none of variables were significant in the model. In An Giang, the final model showed that 100 mm increase of precipitation in the preceding and same months corresponded to an increase of 30% and 22% of DF incidence rate.

Conclusion

Our findings provide insight into understanding the seasonal pattern and associated climate risk factors.
Literatur
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