Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2458-14-658
Title: Spatial epidemiology and climatic predictors of paediatric dengue infections captured via sentinel site surveillance, Phnom Penh Cambodia 2011-2012
Authors: Lover, A.A 
Buchy, P
Rachline, A
Moniboth, D
Huy, R
Meng, C.Y
Leo, Y.S 
Yuvatha, K
Sophal, U
Chantha, N
Bunthin, Y
Duong, V
Goyet, S
Brett, J.L
Tarantola, A
Cavailler, P.
Keywords: adolescent
Cambodia
child
climate
cluster analysis
dengue
female
forecasting
hospital
human
male
preschool child
prospective study
sentinel surveillance
statistical model
Adolescent
Cambodia
Child
Child, Preschool
Climate
Dengue
Female
Forecasting
Hospitals, Pediatric
Humans
Male
Models, Statistical
Prospective Studies
Sentinel Surveillance
Small-Area Analysis
Issue Date: 2014
Publisher: BioMed Central Ltd.
Citation: Lover, A.A, Buchy, P, Rachline, A, Moniboth, D, Huy, R, Meng, C.Y, Leo, Y.S, Yuvatha, K, Sophal, U, Chantha, N, Bunthin, Y, Duong, V, Goyet, S, Brett, J.L, Tarantola, A, Cavailler, P. (2014). Spatial epidemiology and climatic predictors of paediatric dengue infections captured via sentinel site surveillance, Phnom Penh Cambodia 2011-2012. BMC Public Health 14 (1) : 658. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2458-14-658
Abstract: Background: Dengue is a major contributor to morbidity in children aged twelve and below throughout Cambodia; the 2012 epidemic season was the most severe in the country since 2007, with more than 42,000 reported (suspect or confirmed) cases. Methods. We report basic epidemiological characteristics in a series of 701 patients at the National Paediatric Hospital in Cambodia, recruited during a prospective clinical study (2011-2012). To more fully explore this cohort, we examined climatic factors using multivariate negative binomial models and spatial clustering of cases using spatial scan statistics to place the clinical study within a larger epidemiological framework. Results: We identify statistically significant spatial clusters at the urban village scale, and find that the key climatic predictors of increasing cases are weekly minimum temperature, median relative humidity, but find a negative association with rainfall maximum, all at lag times of 1-6 weeks, with significant effects extending to 10 weeks. Conclusions: Our results identify clustering of infections at the neighbourhood scale, suggesting points for targeted interventions, and we find that the complex interactions of vectors and climatic conditions in this setting may be best captured by rising minimum temperature, and median (as opposed to mean) relative humidity, with complex and limited effects from rainfall. These results suggest that real-time cluster detection during epidemics should be considered in Cambodia, and that improvements in weather data reporting could benefit national control programs by allow greater prioritization of limited health resources to both vulnerable populations and time periods of greatest risk. Finally, these results add to the increasing body of knowledge suggesting complex interactions between climate and dengue cases that require further targeted research. © 2014 Lover et al.; licensee BioMed Central Ltd.
Source Title: BMC Public Health
URI: https://scholarbank.nus.edu.sg/handle/10635/174301
ISSN: 14712458
DOI: 10.1186/1471-2458-14-658
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