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01.12.2018 | Research article | Ausgabe 1/2018 Open Access

BMC Public Health 1/2018

Meteorological factors affecting dengue incidence in Davao, Philippines

Zeitschrift:
BMC Public Health > Ausgabe 1/2018
Autoren:
Jesavel A. Iguchi, Xerxes T. Seposo, Yasushi Honda
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s12889-018-5532-4) contains supplementary material, which is available to authorized users.

Abstract

Background

Dengue fever is a major public health concern in the Philippines, and has been a significant cause of hospitalizations and deaths among young children. Previous literature links climate change to dengue, and with increasingly unpredictable changing climate patterns, there is a need to understand how these meteorological variables affect dengue incidence in a highly endemic area.

Methods

Weekly dengue incidences (2011–2015) in Davao Region, Philippines were obtained from the Department of Health. Same period of weekly local meteorological variables were obtained from the National Climatic Data Center (NCDC) and the National Oceanic and Atmospheric Administration (NOAA). Wavelet coherence analysis was used to determine the presence of non-stationary relationships, while a quasi-Poisson regression combined with distributed lag nonlinear model (DLNM) was used to analyze the association between meteorological variables and dengue incidences.

Results

Significant periodicity was detected in the 7 to 14-week band between the year 2011–2012 and a 26-week periodicity from the year 2013–2014. Overall cumulative risks were particularly high for rainfall at 32 mm (RR: 1.67, 95% CI: 1.07–2.62), while risks were observed to increase with increasing dew point. On the other hand, lower average temperature of 26 °C has resulted to an increased RR of dengue (RR: 1.96, 95% CI: 0.47–8.15) while higher temperature from 27 °C to 31 °C has lower RR.

Conclusions

The observed possible threshold levels of these meteorological variables can be integrated into an early warning system to enhance dengue prediction for better vector control and management in the future.
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