ArticleInfectious diseases of poverty2023
Schistosomiasis control in Senegal: results from community data analysis for optimizing preventive chemotherapy intervention with praziquantel.
Article in Infectious diseases of poverty, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Systems analysis unravels a common rural-urban gradient in immunological profile, function, and metabolic dependencies.Science advances · 2025Article
- Schistosomiasis transmission in Zimbabwe: Modelling based on machine learning.Infectious Disease Modelling · 2024Article
- Development of an automated artificial intelligence-based system for urogenital schistosomiasis diagnosis using digital image analysis techniques and a robotized microscope.PLoS neglected tropical diseases · 2024Article
- Correction: Schistosomiasis control in Senegal: results from community data analysis for optimizing preventive chemotherapy intervention with praziquantel.Infectious diseases of poverty · 2024Article
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Corrections and comments
- Erratum issued
Authors and funding
15 authors at 5 institutions in 5 countries.
Funding
Abstract
backgroundOver the past two decades, preventive chemotherapy (PC) with praziquantel (PZQ) is the major strategy for controlling schistosomiasis in Senegal. The objective of this analysis was to update the endemicity of schistosomiasis at community level for better targeting mass treatment with PZQ in Senegal.
methodsDemographic and epidemiological data from 1610 community health areas were analyzed using the schistosomiasis community data analysis tool of Expanded Special Project for Elimination of Neglected Tropical Diseases which developed by World Health Organization/Africa Office (WHO/AFRO). The tool uses a WHO/AFRO decision tree for areas without epidemiological data to determine whether mass treatment should be continued at community level. Descriptive analysis was performed.
resultsOverall, the endemicity of 1610 community health areas were updated based on the data from the district endemicity (33.5%) and the form of Join request for selected PC medicine (40.5%). Up to 282 (17.5%) and 398 (24.7%) of community health areas were classified as moderate and high endemicity. 41.1% of communities were non endemic. High endemicity was more important in Tambacounda, Saint Louis, Matam, Louga and Kedougou. A change in endemicity category was observed when data was disagregted from district level to community level. Implementation units classified non endemic were more important at community level (n = 666) compared to district level (n = 324). Among 540 areas previously classified high endemic at district level, 392 (72.6%) remained high prevalence category, while 92 (17.0%) became moderate, 43 (8.0%) low and 13 (2.4%) non-endemics at community level. Number of implementation units requiring PC was more important at district level (1286) compared to community level (944). Number of school aged children requiring treatment was also more important at district level compared to community level.
conclusionsThe analysis to disaggregate data from district level to community level using the WHO/AFRO schistosomiasis sub-district data optimization tool provide an update of schistosomiasis endemicity at community level. This study has allowed to better target schistosomiasis interventions, optimize use of available PZQ and exposed data gaps.
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