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. 2011 Jun;5(6):e1200.
doi: 10.1371/journal.pntd.0001200. Epub 2011 Jun 7.

Mapping helminth co-infection and co-intensity: geostatistical prediction in ghana

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Mapping helminth co-infection and co-intensity: geostatistical prediction in ghana

Ricardo J Soares Magalhães et al. PLoS Negl Trop Dis. 2011 Jun.

Abstract

Background: Morbidity due to Schistosoma haematobium and hookworm infections is marked in those with intense co-infections by these parasites. The development of a spatial predictive decision-support tool is crucial for targeting the delivery of integrated mass drug administration (MDA) to those most in need. We investigated the co-distribution of S. haematobium and hookworm infection, plus the spatial overlap of infection intensity of both parasites, in Ghana. The aim was to produce maps to assist the planning and evaluation of national parasitic disease control programs.

Methodology/principal findings: A national cross-sectional school-based parasitological survey was conducted in Ghana in 2008, using standardized sampling and parasitological methods. Bayesian geostatistical models were built, including a multinomial regression model for S. haematobium and hookworm mono- and co-infections and zero-inflated Poisson regression models for S. haematobium and hookworm infection intensity as measured by egg counts in urine and stool respectively. The resulting infection intensity maps were overlaid to determine the extent of geographical overlap of S. haematobium and hookworm infection intensity. In Ghana, prevalence of S. haematobium mono-infection was 14.4%, hookworm mono-infection was 3.2%, and S. haematobium and hookworm co-infection was 0.7%. Distance to water bodies was negatively associated with S. haematobium and hookworm co-infections, hookworm mono-infections and S. haematobium infection intensity. Land surface temperature was positively associated with hookworm mono-infections and S. haematobium infection intensity. While high-risk (prevalence >10-20%) of co-infection was predicted in an area around Lake Volta, co-intensity was predicted to be highest in foci within that area.

Conclusions/significance: Our approach, based on the combination of co-infection and co-intensity maps allows the identification of communities at increased risk of severe morbidity and environmental contamination and provides a platform to evaluate progress of control efforts.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Observed Schistosoma haematobium and hookworm mono- and co-infections in children aged 5–19 years in Ghana, 2008.
Data were collected prior to the inception of the Ghana Health Service control program for neglected tropical diseases.
Figure 2
Figure 2. Spatial heterogeneity of observed Schistosoma haematobium and hookworm egg counts in children aged 5–19 years in Ghana, 2008.
Figure 3
Figure 3. Predicted prevalence of Schistosoma haematobium and hookworm mono-and co-infections in boys aged 15–19 years in Ghana, 2008.
Figure 4
Figure 4. Mapped outputs for Schistosoma haematobium and hookworm egg count models, boys aged 15–19 years in Ghana, 2008.
Egg counts for S. haematobium are as per 10 mL urine; egg counts for hookworm are as per gram of faeces.
Figure 5
Figure 5. Predicted areas of co-intensity for Schistosoma haematobium and hookworm, boys aged 15–19 years in Ghana, 2008.

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