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Open Access Research article

Spatial epidemiology and climatic predictors of paediatric dengue infections captured via sentinel site surveillance, Phnom Penh Cambodia 2011–2012

Andrew A Lover1*, Philippe Buchy2, Anne Rachline3, Duch Moniboth4, Rekol Huy5, Chour Y Meng6, Yee Sin Leo7, Kdan Yuvatha4, Ung Sophal8, Ngan Chantha5, Bunthin Y2, Veasna Duong2, Sophie Goyet3, Jeremy L Brett9, Arnaud Tarantola10 and Philippe Cavailler11

Author Affiliations

1 Infectious Diseases Programme, Saw Swee Hock School of Public Health, National University of Singapore, MD3, 16 Medical Drive, Singapore 117597, Singapore

2 Virology Unit, Institut Pasteur in Cambodia, Phnom Penh, Cambodia

3 Formerly- Regional Emerging Diseases Intervention (REDI) Centre, Singapore, Singapore

4 Infectious Diseases Unit, National Paediatric Hospital, Phnom Penh, Cambodia

5 National Dengue Control Program (NDCP), National Center for Parasitological, Entomology and Malaria Control, Ministry of Health, Phnom Penh, Cambodia

6 Ministry of Health, Phnom Penh, Cambodia

7 Tan Tock Seng Hospital, Centers for Disease Control, Singapore, Singapore

8 Emergency Unit, National Paediatric Hospital, Phnom Penh, Cambodia

9 Medical Affairs Department, Sanofi Pasteur, Singapore Regional Office, Singapore, Singapore

10 Epidemiology Unit, Institut Pasteur in Cambodia, Phnom Penh, Cambodia

11 Médecins Sans Frontières, Operational Centre, Geneva, Switzerland

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BMC Public Health 2014, 14:658  doi:10.1186/1471-2458-14-658

Published: 28 June 2014

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.