Bayesian risk maps for Schistosoma mansoni and hookworm mono-infections in a setting where both parasites co-exist

Abstract

There is growing interest in the use of Bayesian geostatistical models for predicting the spatial distribution of parasitic infections, including hookworm, Schistosoma mansoni and co-infections with both parasites. The aim of this study was to predict the spatial distribution of mono-infections with either hookworm or S. mansoni in a setting where both parasites co-exist. School-based cross-sectional parasitological and questionnaire surveys were carried out in 57 rural schools in the Man region, western Côte d'Ivoire. A single stool specimen was obtained from each schoolchild attending grades 3-5. Stool specimens were processed by the Kato-Katz technique and an ether concentration method and examined for the presence of hookworm and S. mansoni eggs. The combined results from the two diagnostic approaches were considered for the infection status of each child. Demographic data (i.e. age and sex) were obtained from readily available school registries. Each child's socio-economic status was estimated, using the questionnaire data following a household-based asset approach. Environmental data were extracted from satellite imagery. The different data sources were incorporated into a geographical information system. Finally, a Bayesian spatial multinomial regression model was constructed and the spatial patterns of S. mansoni and hookworm mono-infections were investigated using Bayesian kriging. Our approach facilitated the production of smooth risk maps for hookworm and S. mansoni mono-infections that can be utilized for targeting control interventions. We argue that in settings where S. mansoni and hookworm co-exist and control efforts are under way, there is a need for both mono- and co-infection risk maps to enhance the cost-effectiveness of control programmes.

Figure 1

Conceptual framework for an area co-endemic for S. mansoni and hookworm, where several rounds of mass drug administration take place. It highlights that treating all individuals in a co-endemic area with praziquantel and albendazole decreases the prevalence of S. mansoni and hookworm and consequently also the prevalence of co-infection. As a result, an ever growing proportion of individuals will be treated unnecessarily with one or even both drugs.

Figure 2

Distribution of S. mansoni single infections (a) and S. mansoni mono-infections (b) among schoolchildren at the unit of school in the Man region, western Côte d’Ivoire.

Figure 2

Distribution of S. mansoni single infections (a) and S. mansoni mono-infections (b) among schoolchildren at the unit of school in the Man region, western Côte d’Ivoire.

Figure 3

Distribution of hookworm single infections (a) and hookworm mono-infections (b) among schoolchildren at the unit of school in the Man region, western Côte d’Ivoire.

Figure 3

Distribution of hookworm single infections (a) and hookworm mono-infections (b) among schoolchildren at the unit of school in the Man region, western Côte d’Ivoire.

Figure 4

Predicted S. mansoni mono-infection prevalence using Bayesian kriging based on the multinomial regression model (a) and standard error of the predicted S. mansoni mono-infection prevalence (b).

Figure 4

Predicted S. mansoni mono-infection prevalence using Bayesian kriging based on the multinomial regression model (a) and standard error of the predicted S. mansoni mono-infection prevalence (b).

Figure 5

Predicted hookworm mono-infection prevalence using Bayesian kriging based on the multinomial regression model (a) and standard error of the predicted hookworm mono-infection prevalence (b).

Figure 5

Predicted hookworm mono-infection prevalence using Bayesian kriging based on the multinomial regression model (a) and standard error of the predicted hookworm mono-infection prevalence (b).