Predictive vs. empiric assessment of schistosomiasis: implications for treatment projections in Ghana.

• Published in: PLoS Neglected Tropical Diseases
• Main Authors: Achille Kabore, Nana-Kwadwo Biritwum, Philip W Downs, Ricardo J Soares Magalhaes, Yaobi Zhang, Eric A Ottesen
• Format: Article in Journal/Newspaper
• Language: English
• Published: Public Library of Science (PLoS) 2013
• Subjects: Arctic medicine. Tropical medicine; RC955-962; Public aspects of medicine; RA1-1270; Arctic
• Online Access: https://doi.org/10.1371/journal.pntd.0002051; https://doaj.org/article/75ade1175e264347afd3bab38551c322

BACKGROUND: Mapping the distribution of schistosomiasis is essential to determine where control programs should operate, but because it is impractical to assess infection prevalence in every potentially endemic community, model-based geostatistics (MBG) is increasingly being used to predict prevalence and determine intervention strategies. METHODOLOGY/PRINCIPAL FINDINGS: To assess the accuracy of MBG predictions for Schistosoma haematobium infection in Ghana, school surveys were evaluated at 79 sites to yield empiric prevalence values that could be compared with values derived from recently published MBG predictions. Based on these findings schools were categorized according to WHO guidelines so that practical implications of any differences could be determined. Using the mean predicted values alone, 21 of the 25 empirically determined 'high-risk' schools requiring yearly praziquantel would have been undertreated and almost 20% of the remaining schools would have been treated despite empirically-determined absence of infection - translating into 28% of the children in the 79 schools being undertreated and 12% receiving treatment in the absence of any demonstrated need. CONCLUSIONS/SIGNIFICANCE: Using the current predictive map for Ghana as a spatial decision support tool by aggregating prevalence estimates to the district level was clearly not adequate for guiding the national program, but the alternative of assessing each school in potentially endemic areas of Ghana or elsewhere is not at all feasible; modelling must be a tool complementary to empiric assessments. Thus for practical usefulness, predictive risk mapping should not be thought of as a one-time exercise but must, as in the current study, be an iterative process that incorporates empiric testing and model refining to create updated versions that meet the needs of disease control operational managers.