Jane Jemeli Rutto1, Julia Wairimu Karuga. 1. Kenya Agricultural Research Institute-Trypanosomiasis Research Centre, Kikuyu, Kenya. jjrutto@yahoo.co.uk
Abstract
BACKGROUND & OBJECTIVES: In Kenya, sleeping sickness (SS) caused by Trypanosoma brucei rhodesiense is confined to the Nyanza and Western Provinces tsetse belts. Over the last two decades, the disease has exhibited great spatial variability in its spread and distribution. The objectives of the study were to map the spatial and temporal distribution of SS and determine possible risk factors associated with the disease in western Kenya. METHODS: Geographical coordinates of villages were obtained using a Global Positioning System (GPS). SS data were analyzed retrospectively and the mapping of villages was done using MapInfo Software. Epidemiological data of villages affected by SS were then correlated to human and cattle population. RESULTS: SS has spread northwards affecting the western parts of Busia, Teso, and of Bungoma districts in the late 1990s. Most of the SS cases were reported between March and June. The mainly affected age groups were from 20 to 49 years. SS was highest in areas with low human population density, ranging from 0-340/km2 and high livestock population, ranging from 5000 to 10,000 cattle. INTERPRETATION & CONCLUSION: There was a shift of SS occurrence from the old foci into new foci occurring at low transmission levels and causing occasional epidemic outbreaks. The study concludes that seasons influenced disease incidences with higher numbers of SS cases being recorded during the wet seasons. Gender and age determined the disease occurrence with most productive age groups being at higher risk. Areas with high livestock populations had low human population densities and had higher SS cases.
BACKGROUND & OBJECTIVES: In Kenya, sleeping sickness (SS) caused by Trypanosoma brucei rhodesiense is confined to the Nyanza and Western Provinces tsetse belts. Over the last two decades, the disease has exhibited great spatial variability in its spread and distribution. The objectives of the study were to map the spatial and temporal distribution of SS and determine possible risk factors associated with the disease in western Kenya. METHODS: Geographical coordinates of villages were obtained using a Global Positioning System (GPS). SS data were analyzed retrospectively and the mapping of villages was done using MapInfo Software. Epidemiological data of villages affected by SS were then correlated to human and cattle population. RESULTS: SS has spread northwards affecting the western parts of Busia, Teso, and of Bungoma districts in the late 1990s. Most of the SS cases were reported between March and June. The mainly affected age groups were from 20 to 49 years. SS was highest in areas with low human population density, ranging from 0-340/km2 and high livestock population, ranging from 5000 to 10,000 cattle. INTERPRETATION & CONCLUSION: There was a shift of SS occurrence from the old foci into new foci occurring at low transmission levels and causing occasional epidemic outbreaks. The study concludes that seasons influenced disease incidences with higher numbers of SS cases being recorded during the wet seasons. Gender and age determined the disease occurrence with most productive age groups being at higher risk. Areas with high livestock populations had low human population densities and had higher SS cases.
Authors: Beatrix von Wissmann; Noreen Machila; Kim Picozzi; Eric M Fèvre; Barend M deC Bronsvoort; Ian G Handel; Susan C Welburn Journal: PLoS Negl Trop Dis Date: 2011-01-18
Authors: Winnie A Okeyo; Norah P Saarman; Michael Mengual; Kirstin Dion; Rosemary Bateta; Paul O Mireji; Sylvance Okoth; Johnson O Ouma; Collins Ouma; Joel Ochieng; Grace Murilla; Serap Aksoy; Adalgisa Caccone Journal: Parasit Vectors Date: 2017-10-10 Impact factor: 3.876