Spatial risk profiling of Plasmodium falciparum parasitaemia in a high endemicity area in Côte d'Ivoire

Abstract

Background: The objective of this study was to identify demographic, environmental and socioeconomic risk factors and spatial patterns of Plasmodium falciparum parasitaemia in a high endemicity area of Africa, and to specify how this information can facilitate improved malaria control at the district level.

Methods: A questionnaire was administered to about 4,000 schoolchildren in 55 schools in western Côte d'Ivoire to determine children's socioeconomic status and their habit of sleeping under bed nets. Environmental data were obtained from satellite images, digitized ground maps and a second questionnaire addressed to school directors. Finger prick blood samples were collected and P. falciparum parasitaemia determined under a microscope using standardized, quality-controlled methods. Bayesian variogram models were utilized for spatial risk modelling and mapping of P. falciparum parasitaemia at non-sampled locations, assuming stationary and non-stationary underlying spatial dependence.

Results: Two-thirds of the schoolchildren were infected with P. falciparum and the mean parasitaemia among infected children was 959 parasites/microl of blood. Age, socioeconomic status, not sleeping under a bed net, coverage rate with bed nets and environmental factors (e.g., normalized difference vegetation index, rainfall, land surface temperature and living in close proximity to standing water) were significantly associated with the risk of P. falciparum parasitaemia. After accounting for spatial correlation, age, bed net coverage, rainfall during the main malaria transmission season and distance to rivers remained significant covariates.

Conclusion: It is argued that a massive increase in bed net coverage, particularly in villages in close proximity to rivers, in concert with other control measures, is necessary to bring malaria endemicity down to intermediate or low levels.

Figure 1

Mean P. falciparum parasitaemia among 3,962 schoolchildren from 55 sampled schools in the region of Man, western Côte d'Ivoire during the school year 2001/2002. The normalized difference vegetation index (NDVI) is displayed in the background.

Figure 2

Smoothed map of P. falciparum parasitaemia derived from a stationary negative binomial regression model using Bayesian in the region of Man, western Côte d'Ivoire.

Figure 3

Standard deviation map of the predicted P. falciparum parasitaemia derived from a stationary negative binomial regression model using Bayesian kriging in the region of Man, western Côte d'Ivoire.

Figure 4

Smoothed map of P. falciparum parasitaemia derived from a non-stationary negative binomial regression model with ecologic subregions using Bayesian kriging in the region of Man, western Côte d'Ivoire.

Figure 5

Standard deviation map of the predicted P. falciparum parasitaemia derived from a non-stationary negative binomial regression model with ecologic subregions using Bayesian kriging in the region of Man, western Côte d'Ivoire.

Figure 6

Smoothed map of P. falciparum parasitaemia derived from a non-stationary negative binomial regression model with fixed subregions using Bayesian kriging in the region of Man, western Côte d'Ivoire.

Figure 7

Standard deviation of the predicted P. falciparum parasitaemia derived from a non-stationary negative binomial regression model with fixed subregions using Bayesian kriging in the region of Man, western Côte d'Ivoire.