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PLoS Negl Trop Dis. 2017 May 26;11(5):e0005599. doi: 10.1371/journal.pntd.0005599. eCollection 2017 May.

Optimising cluster survey design for planning schistosomiasis preventive chemotherapy.

Author information

Schistosomiasis Control Initiative, Department of Infectious Disease Epidemiology, Imperial College London, St. Mary's Campus, Norfolk Place, London, United Kingdom.
The Royal Veterinary College, Hawkshead Lane, Hatfield, Hertfordshire, United Kingdom.
London Centre for Neglected Tropical Disease Research, London, United Kingdom.
Global Health Group, University of California San Francisco, San Francisco, California, United States of America.
Department of Infectious Disease Epidemiology, Imperial College London, St. Mary's Campus, Norfolk Place, London, United Kingdom.
Ministry of Health, Capital City, Lilongwe 3, Malawi.
Ministry of Health and Social Welfare of Côte d'Ivoire, Abidjan, Côte d'Ivoire.
Fliarial Programme Support Unit, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, United Kingdom.
Neglected Tropical and Non Communicable Diseases Program, Ministry of Health and Social Welfare, Monrovia 10, Liberia.
National Foundation for Educational Research, Upton Park, Slough, United Kingdom.



The cornerstone of current schistosomiasis control programmes is delivery of praziquantel to at-risk populations. Such preventive chemotherapy requires accurate information on the geographic distribution of infection, yet the performance of alternative survey designs for estimating prevalence and converting this into treatment decisions has not been thoroughly evaluated.


We used baseline schistosomiasis mapping surveys from three countries (Malawi, Côte d'Ivoire and Liberia) to generate spatially realistic gold standard datasets, against which we tested alternative two-stage cluster survey designs. We assessed how sampling different numbers of schools per district (2-20) and children per school (10-50) influences the accuracy of prevalence estimates and treatment class assignment, and we compared survey cost-efficiency using data from Malawi. Due to the focal nature of schistosomiasis, up to 53% simulated surveys involving 2-5 schools per district failed to detect schistosomiasis in low endemicity areas (1-10% prevalence). Increasing the number of schools surveyed per district improved treatment class assignment far more than increasing the number of children sampled per school. For Malawi, surveys of 15 schools per district and 20-30 children per school reliably detected endemic schistosomiasis and maximised cost-efficiency. In sensitivity analyses where treatment costs and the country considered were varied, optimal survey size was remarkably consistent, with cost-efficiency maximised at 15-20 schools per district.


Among two-stage cluster surveys for schistosomiasis, our simulations indicated that surveying 15-20 schools per district and 20-30 children per school optimised cost-efficiency and minimised the risk of under-treatment, with surveys involving more schools of greater cost-efficiency as treatment costs rose.

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