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


Journal

Parasites & vectors
ISSN: 1756-3305
Titre abrégé: Parasit Vectors
Pays: England
ID NLM: 101462774

Informations de publication

Date de publication:
18 Nov 2020
Historique:
received: 19 06 2020
accepted: 21 10 2020
entrez: 18 11 2020
pubmed: 19 11 2020
medline: 24 7 2021
Statut: epublish

Résumé

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.

Sections du résumé

BACKGROUND BACKGROUND
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.
METHODS METHODS
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.
RESULTS RESULTS
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.
CONCLUSIONS CONCLUSIONS
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.

Identifiants

pubmed: 33203463
doi: 10.1186/s13071-020-04413-7
pii: 10.1186/s13071-020-04413-7
pmc: PMC7672864
doi:

Substances chimiques

Anthelmintics 0
Praziquantel 6490C9U457

Types de publication

Evaluation Study Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

555

Subventions

Organisme : World Health Organization
ID : 001
Pays : International

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Auteurs

Kimberly M Fornace (KM)

Department of Disease Control, London School of Hygiene and Tropical Medicine, London, UK. Kimberly.Fornace@lshtm.ac.uk.

Claudio Fronterrè (C)

Centre for Health Informatics, Computing and Statistics (CHICAS), Lancaster University, Lancaster, UK.

Fiona M Fleming (FM)

Schistosomiasis Control Initiative, London, UK.

Hope Simpson (H)

Department of Disease Control, London School of Hygiene and Tropical Medicine, London, UK.

Honorat Zoure (H)

Expanded Special Project of the Elimination of Neglected Tropical Diseases (ESPEN), Africa Regional Office of the World Health Organisation, Brazzaville, Congo.

Maria Rebollo (M)

Expanded Special Project of the Elimination of Neglected Tropical Diseases (ESPEN), Africa Regional Office of the World Health Organisation, Brazzaville, Congo.

Pauline Mwinzi (P)

Expanded Special Project of the Elimination of Neglected Tropical Diseases (ESPEN), Africa Regional Office of the World Health Organisation, Brazzaville, Congo.

Penelope Vounatsou (P)

Swiss Tropical and Public Health Institute, Basel, Switzerland.

Rachel L Pullan (RL)

Department of Disease Control, London School of Hygiene and Tropical Medicine, London, UK.

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