Abstract
Abstract
Background
Over 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.
Methods
Demographic 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.
Results
Overall, 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.
Conclusions
The 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.
Publisher
Springer Science and Business Media LLC
Subject
Infectious Diseases,Public Health, Environmental and Occupational Health,General Medicine
Reference31 articles.
1. French MD, Evans D, Fleming FM, Secor WE, Biritwum N-K, Brooker SJ, et al. Schistosomiasis in Africa: improving strategies for long-term and sustainable morbidity control. PLoS Negl Trop Dis. 2018;12(6):e0006484. https://doi.org/10.1371/journal.pntd.0006484.
2. WHO. Schistosomiasis and soil-transmitted helminthiases: number of people treated in 2016. Wkly Epidemiol Rec. 201;92(49):749–60.
3. Talla I, Kongs A, Verlé P. Preliminary study of the prevalence of human schistosomiasis in Richard-Toll (the Senegal river basin). Trans R Soc Trop Med Hyg. 1992;86(2):182. https://doi.org/10.1016/0035-9203(92)90562-q.
4. Meurs L, Mbow M, Vereecken K, Menten J, Mboup S, Polman K. Epidemiology of mixed Schistosoma mansoni and Schistosoma haematobium infections in northern Senegal. Int J Parasitol. 2012;42(3):305–11. https://doi.org/10.1016/j.ijpara.2012.02.002.
5. Ernould JC. Épidémiologie des schistosomoses humaines dans le delta du fleuve Sénégal: phénomène récent de compétition entre Schistosoma haematobium Sambon, 1907 et S. mansoni (Bilharz, 1852) [PhD thesis]. Université de Paris 12: Val de Marne, Médecine Parasitologie, 1996, 602 p.
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