Spatially-explicit risk profiling of Plasmodium falciparum infections at a small scale: a geostatistical modelling approach

Silue, Kigbafori D., Raso, Giovanna, Yapi, Ahoua, Vounatsou, Penelope, Tanner, Marcel, N'Goran, Eliezer K. and Utzinger, Jurg (2008) Spatially-explicit risk profiling of Plasmodium falciparum infections at a small scale: a geostatistical modelling approach. Malaria Journal, 7 Article Number: 111. doi:10.1186/1475-2875-7-111


Author Silue, Kigbafori D.
Raso, Giovanna
Yapi, Ahoua
Vounatsou, Penelope
Tanner, Marcel
N'Goran, Eliezer K.
Utzinger, Jurg
Title Spatially-explicit risk profiling of Plasmodium falciparum infections at a small scale: a geostatistical modelling approach
Journal name Malaria Journal   Check publisher's open access policy
ISSN 1475-2875
Publication date 2008-06-23
Year available 2008
Sub-type Article (original research)
DOI 10.1186/1475-2875-7-111
Open Access Status DOI
Volume 7
Start page Article Number: 111
Total pages 10
Editor Marcel Hommel
Place of publication London, U.K..
Publisher BioMed Central Ltd.
Language eng
Subject C1
920109 Infectious Diseases
110803 Medical Parasitology
1108 Medical Microbiology
Abstract Background. There is a renewed political will and financial support to eradicate malaria. Spatially-explicit risk profiling will play an important role in this endeavour. Patterns of Plasmodium falciparum infection prevalence were examined among schoolchildren in a highly malaria-endemic area. Methods. A questionnaire was administered and finger prick blood samples collected from 3,962 children, aged six to 16 years, attending 55 schools in a rural part of western Côte d'Ivoire. Information was gathered from the questionnaire on children's socioeconomic status and the use of bed nets for the prevention of malaria. Blood samples were processed with standardized, quality-controlled methods for diagnosis of Plasmodium spp. infections. Environmental data were obtained from satellite images and digitized maps. Bayesian variogram models for spatially-explicit risk modelling of P. falciparum infection prevalence were employed, assuming for stationary and non-stationary spatial processes. Findings. The overall prevalence of P. falciparum infection was 64.9%, ranging between 34.0% and 91.9% at the unit of the school. Risk factors for a P. falciparum infection included age, socioeconomic status, not sleeping under a bed net, distance to health care facilities and a number of environmental features (i.e. normalized difference vegetation index, rainfall and distance to rivers). After taking into account spatial correlation only age remained significant. Non-stationary models performed better than stationary models. Conclusion. Spatial risk profiling of P. falciparum prevalence data provides a useful tool for targeting malaria control intervention, and hence will play a role in the quest of local elimination and ultimate eradication of the disease.
Formatted abstract
Background
There is a renewed political will and financial support to eradicate malaria. Spatially-explicit risk profiling will play an important role in this endeavour. Patterns of Plasmodium falciparum infection prevalence were examined among schoolchildren in a highly malaria-endemic area.
Methods
A questionnaire was administered and finger prick blood samples collected from 3,962 children, aged six to 16 years, attending 55 schools in a rural part of western Côte d'Ivoire. Information was gathered from the questionnaire on children's socioeconomic status and the use of bed nets for the prevention of malaria. Blood samples were processed with standardized, quality-controlled methods for diagnosis of Plasmodium spp. infections. Environmental data were obtained from satellite images and digitized maps. Bayesian variogram models for spatially-explicit risk modelling of P. falciparum infection prevalence were employed, assuming for stationary and non-stationary spatial processes.
Findings
The overall prevalence of P. falciparum infection was 64.9%, ranging between 34.0% and 91.9% at the unit of the school. Risk factors for a P. falciparum infection included age, socioeconomic status, not sleeping under a bed net, distance to health care facilities and a number of environmental features (i.e. normalized difference vegetation index, rainfall and distance to rivers). After taking into account spatial correlation only age remained significant. Non-stationary models performed better than stationary models.
Conclusion
Spatial risk profiling of P. falciparum prevalence data provides a useful tool for targeting malaria control intervention, and hence will play a role in the quest of local elimination and ultimate eradication of the disease.
Keyword western Côte d'Ivoire
Malaria
Health care
School Children
Predictiomalaria transmission
Plasmodium falciparum
Africa
Coinfection
Blood sampling
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes © 2008 Silué et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

 
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Created: Thu, 09 Apr 2009, 01:46:33 EST by Elmari Louise Whyte on behalf of Faculty Of Health Sciences