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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-06-28

AUTHORS

Andrew A Lover, Philippe Buchy, Anne Rachline, Duch Moniboth, Rekol Huy, Chour Y Meng, Yee Sin Leo, Kdan Yuvatha, Ung Sophal, Ngan Chantha, Bunthin Y, Veasna Duong, Sophie Goyet, Jeremy L Brett, Arnaud Tarantola, Philippe Cavailler

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. More... »

PAGES

658-658

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2458-14-658

DOI

http://dx.doi.org/10.1186/1471-2458-14-658

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1006047312

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24972712


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24 schema:description 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.
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32 Cambodia 2011
33 National Pediatric Hospital
34 Penh Cambodia 2011
35 Phnom Penh Cambodia 2011
36 association
37 basic epidemiological characteristics
38 binomial model
39 body
40 body of knowledge
41 cases
42 characteristics
43 children
44 climate
45 climatic conditions
46 climatic factors
47 climatic predictors
48 clinical studies
49 cluster detection
50 clustering
51 clustering of infection
52 clusters
53 cohort
54 complex interactions
55 conditions
56 contributor
57 control programs
58 countries
59 data reporting
60 dengue
61 dengue cases
62 dengue infection
63 detection
64 effect
65 epidemic
66 epidemic season
67 epidemiological characteristics
68 epidemiological framework
69 epidemiology
70 factors
71 framework
72 greater prioritization
73 greater risk
74 health resources
75 hospital
76 humidity
77 improvement
78 infection
79 interaction
80 intervention
81 key climatic predictors
82 knowledge
83 lag time
84 larger epidemiological framework
85 limited effect
86 limited health resources
87 major contributor
88 maximum
89 median relative humidity
90 minimum temperature
91 model
92 morbidity
93 multivariate negative binomial model
94 national control program
95 negative association
96 negative binomial model
97 neighborhood scale
98 patients
99 pediatric dengue infection
100 pediatric hospital
101 period
102 point
103 population
104 predictors
105 prioritization
106 program
107 prospective clinical study
108 rainfall
109 rainfall maximum
110 real-time cluster detection
111 relative humidity
112 reporting
113 research
114 resources
115 results
116 risk
117 scale
118 scan statistic
119 season
120 sentinel site surveillance
121 series
122 setting
123 significant effect
124 significant spatial clusters
125 site surveillance
126 spatial clustering
127 spatial clusters
128 spatial epidemiology
129 spatial scan statistic
130 statistics
131 study
132 surveillance
133 targeted research
134 temperature
135 time
136 time period
137 urban village scale
138 vector
139 village scale
140 vulnerable populations
141 weather data reporting
142 weekly minimum temperature
143 weeks
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