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

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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 ofPlasmodium falciparuminfection 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 ofPlasmodiumspp. infections. Environmental data were obtained from satellite images and digitized maps. Bayesian variogram models for spatially-explicit risk modelling ofP. falciparuminfection prevalence were employed, assuming for stationary and non-stationary spatial processes.Findings: The overall prevalence ofP. falciparuminfection was 64.9%, ranging between 34.0% and 91.9% at the unit of the school. Risk factors for aP. falciparuminfection 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 ofP. falciparumprevalence 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.