03357nas a2200373   4500000000100000008004100001260001200042653002800054653001500082653001700097653002000114653001200134100001100146700001200157700001100169700001000180700001300190700000900203700001200212700001200224700001400236700001200250700001200262700001300274700001300287700001200300700001100312245014800323856008400471300000900555490000700564520239800571022001402969       2023                            d  c11/202310aCommunity data analysis10aEndemicity10aPraziquantel10aschistosomiasis10aSenegal1 aDiop B1 aSylla K1 aKane N1 aBoh O1 aGuèye B1 aBa M1 aTalla I1 aMané M1 aMonteil R1 aKinvi B1 aZoure H1 aOrtega J1 aMwinzi P1 aSacko M1 aFaye B00aSchistosomiasis control in Senegal: results from community data analysis for optimizing preventive chemotherapy intervention with praziquantel.  uhttps://idpjournal.biomedcentral.com/counter/pdf/10.1186/s40249-023-01155-3.pdf  a1-140 v123 a<p><strong>Background: </strong>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.</p>

<p><strong>Methods: </strong>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.</p>

<p><strong>Results: </strong>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.</p>

<p><strong>Conclusions: </strong>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.</p>
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