Participatory probabilistic assessment of the risk to human health associated with cryptosporidiosis from urban dairying in Dagoretti, Nairobi, Kenya
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Grace, D., Monda, J., Karanja, N., Randolph, T.F. and Kang'ethe, E.K. 2012. Participatory probabilistic assessment of the risk to human health associated with cryptosporidiosis from urban dairying in Dagoretti, Nairobi, Kenya. Tropical Animal Health and Production 44(Suppl 1): S33-S40.
Permanent link to cite or share this item: http://hdl.handle.net/10568/21690
We carried out a participatory risk assessment to estimate the risk (negative consequences and their likelihood) from zoonotic Cryptosporidium originating in dairy farms in urban Dagoretti, Nairobi to dairy farm households and their neighbours. We selected 20 households at high risk for Cryptosporidium from a larger sample of 300 dairy households in Dagoretti based on risk factors present. We then conducted a participatory mapping of the flow of the hazard from its origin (cattle) to human potential victims. This showed three main exposure pathways (food and water borne, occupational and recreational). This was used to develop a fault tree model which we parameterised using information from the study and literature. A stochastic simulation was used to estimate the probability of exposure to zoonotic cryptosporidiosis originating from urban dairying. Around 6 % of environmental samples were positive for Cryptosporidium. Probability of exposure to Cryptosporidium from dairy cattle ranged from 0.0055 for people with clinical acquired immunodeficiency syndrome in non-dairy households to 0.0102 for children under 5 years from dairy households. Most of the estimated health burden was born by children. Although dairy cattle are the source of Cryptosporidium, the model suggests consumption of vegetables is a greater source of risk than consumption of milk. In conclusion, by combining participatory methods with quantitative microbial risk assessment, we were able to rapidly, and with appropriate ‘imprecision’, investigate health risk to communities from Cryptosporidium and identify the most vulnerable groups and the most risky practices.