Epidemiology of malaria in irrigated parts of Tana River County, Kenya
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Muriuki, J., Kitala, P., Muchemi, G. and Bett, B. 2014. Epidemiology of malaria in irrigated parts of Tana River County, Kenya. Poster prepared for the 9th Biennial Scientific Conference and Exhibition of the Faculty of Veterinary Medicine, University of Nairobi, 3-5 September 2014. Nairobi, Kenya: University of Nairobi.
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Irrigation schemes introduced in areas of high malaria endemicity often amplify malaria burden especially if no mitigation or adaptation measures are implemented (Renshaw et al., 1998). Thias study was conducted in Bura and Hola irrigation schemes in Tana River County to (i) understand the knowledge, attitude and practices of the community in relation to malaria control and transmission, (ii) determine malaria prevalence and the associated risk factors of infection and (iii) develop and validate a transmission model for analyzing the effects of irrigation on malaria burden. A cross sectional survey was conducted in 48 households where 160 people were screened for malaria parasites using Rapid Diagnostic Test. A deterministic model was developed and validated using field data. The community demonstrated good knowledge on causes, symptoms, transmission and control of malaria. The main malaria control measure was use of bed nets where one net was shared by two people. Only 12% of the households practice environmental management to control malaria. Treatment of malaria was mainly based on Artemether-lumefantrine (AL) which is freely available in the government health facilities. The prevalence of malaria was 5% with the clinical records showing a declining trend of malaria cases. Households located ≤5kms to the nearest facility had lower risk of malaria infection (OR=0.104, p-value=0.013) than those located >5kms. Household size was also associated with malaria infection (OR=1.685, p-value=0.022). The model predicted the observed prevalence data. The high usage of bed nets and AL could have led to the observed decrease in malaria prevalence despite the intensification of irrigated agriculture. The model developed could be used to predict the prevalence of malaria in this area enabling decision makers to implement appropriate control measures in good time.