An integrated approach for risk profiling and spatial prediction of Schistosoma mansoni-hookworm coinfection

Raso, G, Vounatsou, P, Singer, BH, N'Goran, EK, Tanner, M and Utzinger, J (2006) An integrated approach for risk profiling and spatial prediction of Schistosoma mansoni-hookworm coinfection. Proceedings of The National Academy of Sciences of The United States of America, 103 18: 6934-6939. doi:10.1073/pnas.0601559103


Author Raso, G
Vounatsou, P
Singer, BH
N'Goran, EK
Tanner, M
Utzinger, J
Title An integrated approach for risk profiling and spatial prediction of Schistosoma mansoni-hookworm coinfection
Journal name Proceedings of The National Academy of Sciences of The United States of America   Check publisher's open access policy
ISSN 0027-8424
Publication date 2006
Sub-type Article (original research)
DOI 10.1073/pnas.0601559103
Open Access Status Not Open Access
Volume 103
Issue 18
Start page 6934
End page 6939
Total pages 6
Place of publication Washington
Publisher Natl Acad Sciences
Language eng
Abstract Multiple-species parasitic infections are pervasive in the developing world, yet resources for their control are scarce. We present an integrated approach for risk profiling and spatial prediction of coinfection with Schistosoma mansoni and hookworm for western Cote d'Ivoire. Our approach combines demographic, environmental, and socioeconomic data; incorporates them into a geographic information system; and employs spatial statistics. Demographic and socioeconomic data were obtained from education registries and from a questionnaire administered to schoolchildren. Environmental data were derived from remotely sensed satellite images and digitized ground maps. Parasitologic data, obtained from fecal examination by using two different diagnostic approaches, served as the outcome measure. Bayesian variogram models were used to assess risk factors and spatial variation of S. mansoni-hookworm coinfection in relation to demographic, environmental, and socioeconomic variables. Coinfections were found in 680 of 3,578 schoolchildren (19.0%) with complete data records. The prevalence of monoinfections with either hookworm or S. mansoni was 24.3% and 24.1%, respectively. Multinomial Bayesian spatial models showed that age, sex, socioeconomic status, and elevation were good predictors for the spatial distribution of S. mansoni-hookworm coinfection. We conclude that our integrated approach, employing a diversity of data sources, geographic information system and remote sensing technologies, and Bayesian spatial statistics, is a powerful tool for risk profiling and spatial prediction of S. mansoni-hookworm coinfection. More generally, this approach facilitates risk mapping and prediction of other parasite combinations and multiparasitism, and hence can guide integrated disease control programs in resource-constrained settings.
Keyword Multidisciplinary Sciences
multinomial Bayesian geostatistical models
risk mapping and prediction
geographic information system
remote sensing
Cote d'Ivoire
Western Cote-divoire
Millennium Development Goals
Neglected Tropical Diseases
Model-based Geostatistics
Day-to-day
Helminth Infections
Information-systems
Poly-parasitism
Malaria
Community
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Public Health Publications
 
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Created: Fri, 25 Jan 2008, 16:50:42 EST