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

doi:10.1186/1475-2875-7-111

Malaria Journal

Volume 7

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Silué KD
Raso G
Yapi A
Vounatsou P
Tanner M
N'Goran EK
Utzinger J

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Raso G
Yapi A
Vounatsou P
Tanner M
N'Goran EK
Utzinger J
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Spatially-explicit risk profiling of Plasmodium falciparum infections at a small scale: a geostatistical modelling approach

Kigbafori D Silué¹^,2^* , Giovanna Raso³^,4^* , Ahoua Yapi¹ , Penelope Vounatsou⁵ , Marcel Tanner⁵ , Eliézer K N'Goran¹^,2 and Jürg Utzinger⁵

¹UFR Biosciences, Université de Cocody-Abidjan, 22 BP 770, Abidjan 22, Côte d'Ivoire

²Centre Suisse de Recherches Scientifiques, 01 BP 1303, Abidjan 01, Côte d'Ivoire

³School of Population Health, University of Queensland, Herston Road, Brisbane, QLD 4006, Australia

⁴Molecular Parasitology Laboratory, Queensland Institute of Medical Research, 300 Herston Road, Brisbane, QLD 4006, Australia

⁵Department of Public Health and Epidemiology, Swiss Tropical Institute, P.O. Box, CH-4002 Basel, Switzerland

author email corresponding author email* Contributed equally

Malaria Journal 2008, 7:111doi:10.1186/1475-2875-7-111


Published:	23 June 2008

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.