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

Soares Magalhaes, Ricardo J., Biritwum, Nana-Kwadwo, Gyapong, John O., Brooker, Simon, Zhang, Yaobi, Blair, Lynsey, Fenwick, Alan and Clements, Archie C. A. (2011) Mapping helminth co-infection and co-intensity: Geostatistical prediction in ghana. PLoS Neglected Tropical Diseases, 5 6: e1200.1-e1200.13. doi:10.1371/journal.pntd.0001200

Author Soares Magalhaes, Ricardo J.
Biritwum, Nana-Kwadwo
Gyapong, John O.
Brooker, Simon
Zhang, Yaobi
Blair, Lynsey
Fenwick, Alan
Clements, Archie C. A.
Title Mapping helminth co-infection and co-intensity: Geostatistical prediction in ghana
Journal name PLoS Neglected Tropical Diseases   Check publisher's open access policy
ISSN 1935-2727
Publication date 2011-06
Sub-type Article (original research)
DOI 10.1371/journal.pntd.0001200
Open Access Status DOI
Volume 5
Issue 6
Start page e1200.1
End page e1200.13
Total pages 13
Place of publication San Francisco, CA, United States
Publisher Public Library of Science
Collection year 2012
Language eng
Formatted abstract
Background: Morbidity due to Schistosoma haematobium and hookworm infections is marked in those with intense coinfections 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.
Keyword Bayesian Spatial-Analysis
Schistosomiasis Control
Urinary Schistosomiasis
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Received June 10, 2010; Accepted April 25, 2011; Published June 7, 2011

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2012 Collection
School of Public Health Publications
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