Logistic Regression analysis of heavy S. mansoni infection detectable by parasitological method.

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Bibliographic Details
Main Author:	Fred Besigye (13268282) (author)
Other Authors:	Candia Rowel (21540168) (author), Moses Adriko (233688) (author), Fredrick J. Muyodi (21453722) (author), John Joseph Kisakye (21453725) (author), Rosemary Nalwanga (7959695) (author), Birgitte J. Vennervald (12456462) (author), Fred Nuwaha (169425) (author), Edridah M. Tukahebwa (7245155) (author), Shona Wilson (78276) (author)
Published:	2025
Subjects:	Neuroscience Biotechnology Ecology Cancer Science Policy Infectious Diseases Biological Sciences not elsewhere classified two primary schools twice per annum schistosoma mansoni </ open defecation occurs one stool sample intermediate snail hosts baseline parasitology data questionnaire data indicates strong school effect moderate transmission area integrated control strategies achieve improved control study also assessed kaiso landing site endemic communities exist persistent hotspot classification high transmission area heavy infection ). neither school met increased treatment frequency increased frequency persistent hotspot high transmission heavy infection disconnect indicates persistent hotspots vector control transmission sites morbidity control xlink "> whether widening water collection wash improvements treatment guidelines smaller numbers river hoimo preventative chemotherapy katz examination infection levels current recommendation 700 participants
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Logistic Regression analysis of heavy <i>S. mansoni</i> infection detectable by parasitological method.