Optimising cluster survey design for planning schistosomiasis preventive chemotherapy
Background: 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 preva...
| Main Authors: | , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Journal article |
| Published: |
Public Library of Science
2017
|
| _version_ | 1826270596805165056 |
|---|---|
| author | Knowles, S Sturrock, H Turner, H Whitton, J Gower, C Jemu, S Phillips, A Meite, A Thomas, B Kollie, K Thomas, C Rebollo, M Styles, B Clements, M Fenwick, A Harrison, W Fleming, F |
| author_facet | Knowles, S Sturrock, H Turner, H Whitton, J Gower, C Jemu, S Phillips, A Meite, A Thomas, B Kollie, K Thomas, C Rebollo, M Styles, B Clements, M Fenwick, A Harrison, W Fleming, F |
| author_sort | Knowles, S |
| collection | OXFORD |
| description | Background: 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. Methodology/Principal findings: 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. Conclusions/Significance: 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. |
| first_indexed | 2024-03-06T21:43:20Z |
| format | Journal article |
| id | oxford-uuid:48b26cfd-5ac4-409d-af40-032d69d81eff |
| institution | University of Oxford |
| last_indexed | 2024-03-06T21:43:20Z |
| publishDate | 2017 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | oxford-uuid:48b26cfd-5ac4-409d-af40-032d69d81eff2022-03-26T15:27:17ZOptimising cluster survey design for planning schistosomiasis preventive chemotherapyJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:48b26cfd-5ac4-409d-af40-032d69d81effSymplectic Elements at OxfordPublic Library of Science2017Knowles, SSturrock, HTurner, HWhitton, JGower, CJemu, SPhillips, AMeite, AThomas, BKollie, KThomas, CRebollo, MStyles, BClements, MFenwick, AHarrison, WFleming, FBackground: 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. Methodology/Principal findings: 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. Conclusions/Significance: 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. |
| spellingShingle | Knowles, S Sturrock, H Turner, H Whitton, J Gower, C Jemu, S Phillips, A Meite, A Thomas, B Kollie, K Thomas, C Rebollo, M Styles, B Clements, M Fenwick, A Harrison, W Fleming, F Optimising cluster survey design for planning schistosomiasis preventive chemotherapy |
| title | Optimising cluster survey design for planning schistosomiasis preventive chemotherapy |
| title_full | Optimising cluster survey design for planning schistosomiasis preventive chemotherapy |
| title_fullStr | Optimising cluster survey design for planning schistosomiasis preventive chemotherapy |
| title_full_unstemmed | Optimising cluster survey design for planning schistosomiasis preventive chemotherapy |
| title_short | Optimising cluster survey design for planning schistosomiasis preventive chemotherapy |
| title_sort | optimising cluster survey design for planning schistosomiasis preventive chemotherapy |
| work_keys_str_mv | AT knowless optimisingclustersurveydesignforplanningschistosomiasispreventivechemotherapy AT sturrockh optimisingclustersurveydesignforplanningschistosomiasispreventivechemotherapy AT turnerh optimisingclustersurveydesignforplanningschistosomiasispreventivechemotherapy AT whittonj optimisingclustersurveydesignforplanningschistosomiasispreventivechemotherapy AT gowerc optimisingclustersurveydesignforplanningschistosomiasispreventivechemotherapy AT jemus optimisingclustersurveydesignforplanningschistosomiasispreventivechemotherapy AT phillipsa optimisingclustersurveydesignforplanningschistosomiasispreventivechemotherapy AT meitea optimisingclustersurveydesignforplanningschistosomiasispreventivechemotherapy AT thomasb optimisingclustersurveydesignforplanningschistosomiasispreventivechemotherapy AT kolliek optimisingclustersurveydesignforplanningschistosomiasispreventivechemotherapy AT thomasc optimisingclustersurveydesignforplanningschistosomiasispreventivechemotherapy AT rebollom optimisingclustersurveydesignforplanningschistosomiasispreventivechemotherapy AT stylesb optimisingclustersurveydesignforplanningschistosomiasispreventivechemotherapy AT clementsm optimisingclustersurveydesignforplanningschistosomiasispreventivechemotherapy AT fenwicka optimisingclustersurveydesignforplanningschistosomiasispreventivechemotherapy AT harrisonw optimisingclustersurveydesignforplanningschistosomiasispreventivechemotherapy AT flemingf optimisingclustersurveydesignforplanningschistosomiasispreventivechemotherapy |