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Hydroclimatological variability and dengue transmission in Dhaka, Bangladesh: a time-series study.


ABSTRACT:

Background

While floods can potentially increase the transmission of dengue, only few studies have reported the association of dengue epidemics with flooding. We estimated the effects of river levels and rainfall on the hospital admissions for dengue fever at 11 major hospitals in Dhaka, Bangladesh.

Methods

We examined time-series of the number of hospital admissions of dengue fever in relation to river levels from 2005 to 2009 using generalized linear Poisson regression models adjusting for seasonal, between-year variation, public holidays and temperature.

Results

There was strong evidence for an increase in dengue fever at high river levels. Hospitalisations increased by 6.9% (95% CI: 3.2, 10.7) for each 0.1 metre increase above a threshold (3.9 metres) for the average river level over lags of 0-5?weeks. Conversely, the number of hospitalisations increased by 29.6% (95% CI: 19.8, 40.2) for a 0.1 metre decrease below the same threshold of the average river level over lags of 0-19?weeks.

Conclusions

Our findings provide evidence that factors associated with both high and low river levels increase the hospitalisations of dengue fever cases in Dhaka.

SUBMITTER: Hashizume M 

PROVIDER: S-EPMC3528427 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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Hydroclimatological variability and dengue transmission in Dhaka, Bangladesh: a time-series study.

Hashizume Masahiro M   Dewan Ashraf M AM   Sunahara Toshihiko T   Rahman M Ziaur MZ   Yamamoto Taro T  

BMC infectious diseases 20120424


<h4>Background</h4>While floods can potentially increase the transmission of dengue, only few studies have reported the association of dengue epidemics with flooding. We estimated the effects of river levels and rainfall on the hospital admissions for dengue fever at 11 major hospitals in Dhaka, Bangladesh.<h4>Methods</h4>We examined time-series of the number of hospital admissions of dengue fever in relation to river levels from 2005 to 2009 using generalized linear Poisson regression models ad  ...[more]

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