PLoS Neglected Tropical Diseases (Jan 2012)
Field evaluation of a coproantigen detection test for fascioliasis diagnosis and surveillance in human hyperendemic areas of Andean countries.
Abstract
BackgroundEmergence of human fascioliasis prompted a worldwide control initiative including a pilot study in a few countries. Two hyperendemic areas were chosen: Huacullani, Northern Altiplano, Bolivia, representing the Altiplanic transmission pattern with high prevalences and intensities; Cajamarca valley, Peru, representing the valley pattern with high prevalences but low intensities. Coprological sample collection, transport and study procedures were analyzed to improve individual diagnosis and subsequent treatments and surveillance activities. Therefore, a coproantigen-detection technique (MM3-COPRO ELISA) was evaluated, using classical techniques for egg detection for comparison.Methodology and findingsA total of 436 and 362 stool samples from schoolchildren of Huacullani and Cajamarca, respectively, were used. Positive samples from Huacullani were 24.77% using the MM3-COPRO technique, and 21.56% using Kato-Katz. Positive samples from Cajamarca were 11.05% using MM3-COPRO, and 5.24% using rapid sedimentation and Kato-Katz. In Huacullani, using Kato-Katz as gold standard, sensitivity and specificity were 94.68% and 98.48%, respectively, and using Kato-Katz and COPRO-ELISA test together, they were 95.68% and 100%. In Cajamarca, using rapid sedimentation and Kato-Katz together, results were 94.73% and 93.58%, and using rapid sedimentation, Kato-Katz and copro-ELISA together, they were 97.56% and 100%, respectively. There was no correlation between coproantigen detection by optical density (OD) and infection intensity by eggs per gram of feces (epg) in Cajamarca low burden cases (ConclusionsThe coproantigen-detection test allows for high sensitivity and specificity, fast large mass screening capacity, detection in the chronic phase, early detection of treatment failure or reinfection in post-treated subjects, and usefulness in surveillance programs. However, this technique falls short when evaluating the fluke burden on its own.