Evaluating survey designs for targeting preventive chemotherapy against Schistosoma haematobium and Schistosoma mansoni across sub-Saharan Africa: a geostatistical analysis and modelling study

• Published in: Parasites & vectors Vol. 13; no. 1; p. 555
• Main Authors: Fornace, Kimberly M, Fronterrè, Claudio, Fleming, Fiona M, Simpson, Hope, Zoure, Honorat, Rebollo, Maria, Mwinzi, Pauline, Vounatsou, Penelope, Pullan, Rachel L
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 18-11-2020; BioMed Central; BMC
• Subjects: Accuracy; Chemotherapy; Cost effectiveness; Costs; Disease prevention; Evaluation; Geostatistics; Heterogeneity; Identification; Infection control; Mapping; Patchiness; Polls & surveys; Prevention; Prophylaxis; Public health administration; Random variables; Sampling; Schistosoma haematobium; Schistosoma mansoni; Schistosomiasis; Schools; Simulation; Spatial distribution; Spatial heterogeneity; Surveying; Surveys; Tropical diseases; Sub-Saharan Africa; Uganda; Africa
• ISSN: 1756-3305; 1756-3305
• DOI: 10.1186/s13071-020-04413-7

Schistosomiasis control programmes primarily use school-based surveys to identify areas for mass drug administration of preventive chemotherapy. However, as the spatial distribution of schistosomiasis can be highly focal, transmission may not be detected by surveys implemented at districts or larger spatial units. Improved mapping strategies are required to accurately and cost-effectively target preventive chemotherapy to remaining foci across all possible spatial distributions of schistosomiasis.
Here, we use geostatistical models to quantify the spatial heterogeneity of Schistosoma haematobium and S. mansoni across sub-Saharan Africa using the most comprehensive dataset available on school-based surveys. Applying this information to parameterise simulations, we assess the accuracy and cost of targeting alternative implementation unit sizes across the range of plausible schistosomiasis distributions. We evaluate the consequences of decisions based on survey designs implemented at district and subdistrict levels sampling different numbers of schools. Cost data were obtained from field surveys conducted across multiple countries and years, with cost effectiveness evaluated as the cost per correctly identified school.
Models identified marked differences in prevalence and spatial distributions between countries and species; however, results suggest implementing surveys at subdistrict level increase the accuracy of treatment classifications across most scenarios. While sampling intensively at the subdistrict level resulted in the highest classification accuracy, this sampling strategy resulted in the highest costs. Alternatively, sampling the same numbers of schools currently recommended at the district level but stratifying by subdistrict increased cost effectiveness.
This study provides a new tool to evaluate schistosomiasis survey designs across a range of transmission settings. Results highlight the importance of considering spatial structure when designing sampling strategies, illustrating that a substantial proportion of children may be undertreated even when an implementation unit is correctly classified. Control programmes need to weigh the increased accuracy of more detailed mapping strategies against the survey costs and treatment priorities.