A high-intensity cluster of Schistosoma mansoni infection around Mbita causeway, western Kenya: a confirmatory cross-sectional survey

In Kenya, communities residing along the shores and islands of Lake Victoria bear a substantial burden ofschistosomiasis. Although there is a school-based deworming program in place, the transmission of Schistosomamansoni varies even at a fine scale. Given the focal nature of schistosomes’ transmission, we aim to identify areaswith high intensity of S. mansoni infection in Mbita, Homabay County, western Kenya, for prioritized integratedcontrol measures. Our findings confirm a high intensity of S. mansoni infection cluster around Mbita causeway.While the current efforts to curtail morbidity due to schistosomiasis through preventive chemotherapy in schoolsare crucial, fine-scale mapping of risk areas is necessary for specific integrated control measures

Spatial distribution and risk factors of Schistosoma haematobium and hookworm infections among schoolchildren in Kwale, Kenya

Background: Large-scale schistosomiasis control programs are implemented in regions with diverse social and economic environments. A key epidemiological feature of schistosomiasis is its small-scale heterogeneity. Locally profiling disease dynamics including risk factors associated with its transmission is essential for designing appropriate control programs. To determine spatial distribution of schistosomiasis and its drivers, we examined schoolchildren in Kwale, Kenya. Methodology/Principal findings: We conducted a cross-sectional study of 368 schoolchildren from six primary schools. Soil-transmitted helminths and Schistosoma mansoni eggs in stool were evaluated by the Kato-Katz method. We measured the intensity of Schistosoma haematobium infection by urine filtration. The geometrical mean intensity of S. haematobium was 3.1 eggs/10 ml urine (school range, 1.4?9.2). The hookworm geometric mean intensity was 3.2 eggs/g feces (school range, 0?17.4). Heterogeneity in the intensity of S. haematobium and hookworm infections was evident in the study area. To identify factors associated with the intensity of helminth infections, we utilized negative binomial generalized linear mixed models. The intensity of S. haematobium infection was associated with religion and socioeconomic status (SES), while that of hookworm infection was related to SES, sex, distance to river and history of anthelmintic treatment. Conclusions/Significance: Both S. haematobium and hookworm infections showed micro-geographical heterogeneities in this Kwale community. To confirm and explain our observation of high S. haematobium risk among Muslims, further extensive investigations are necessary. The observed small scale clustering of the S. haematobium and hookworm infections might imply less uniform strategies even at finer scale for efficient utilization of limited resources

Map of the study area, Kwale, Kenya.

<p>Dotted red circles indicate the catchment area from which children attend each school. The position of the participants’ houses is indicated by white circles. The river network is shown by blue lines while the main road is represented by black lines. Altitude (meters): highest, white background; lowest, dark green background.</p

Negative binomial generalized linear mixed model (NB-GLMM) for intensity of <i>S</i>. <i>haematobium</i> infection among schoolchildren in Kwale, Kenya.

<p>Negative binomial generalized linear mixed model (NB-GLMM) for intensity of <i>S</i>. <i>haematobium</i> infection among schoolchildren in Kwale, Kenya.</p

Negative binomial generalized linear mixed model (NB-GLMM) for intensity of hookworm infection among schoolchildren in Kwale, Kenya.

<p>Negative binomial generalized linear mixed model (NB-GLMM) for intensity of hookworm infection among schoolchildren in Kwale, Kenya.</p

Number (%) of schoolchildren infected with three parasite species in Kwale, Kenya.

<p>Number (%) of schoolchildren infected with three parasite species in Kwale, Kenya.</p

Clustering of <i>S</i>. <i>haematobium</i> and hookworm infections in the study area.

<p>The intensity was expressed as log₁₀ (N + 1). In the left panel, <i>S</i>. <i>haematobium</i> was categorized based on WHO guidelines as: negative, light (1–49 eggs/10 ml urine) and heavy (≥50 eggs/10 ml urine) represented by white, yellow and red dots respectively. The red and white cycles show high and low risk clusters respectively. In the right panel, hookworm was grouped into negative, light (1–1999) and moderate (2000–3999) indicated by white, yellow and brown dots respectively. High risk cluster shown by red circle while the large white cycle represents the low infection cluster.</p

Bivariate negative binomial generalized linear model (NB-GLM) for intensity of hookworm infection among schoolchildren in Kwale, Kenya.

<p>Bivariate negative binomial generalized linear model (NB-GLM) for intensity of hookworm infection among schoolchildren in Kwale, Kenya.</p