dc.description.abstract | Coccidiosis is a protozoan infection that affects most domestic species: canine, feline, equine,
porcine, bovine, ovine, lagomorphs and avian. The overall objective of this study was to
determine the prevalence and spatial distribution of coccidia infection in cattle, and
associated factors in different production systems in Busia, Bungoma and Siaya Counties
Kenya. Specific objectives were to determine the prevalence and spatial distribution of
coccidia infection in cattle as well as assess the factors associated with the infection in these
Counties Kenya.
The three Counties covered by the study were purposively selected for this study on
prevalence of coccidia infection in cattle and associated factors in „unorganised‟ production
systems given that studies done in Kenya have concentrated on zero grazing and dairy
production units. Also, being that this was part of a larger project in these areas (People
Animals and their Zoonoses –PAZ); the three Counties were logistically favourable. The nonanimal
factors considered included: season, rainfall, housing, geographical location hygiene,
nutrition, activity of the animals, and keeping of various species and veterinary attention.
Animal related factors considered were: breed, age, gender, and physiological status
(pregnant/lactating), body condition, and other infections.
Households were then selected randomly using ArcMap 2.0 software to generate random
points (and a back-up for each random point) within the study area. From the random point,
the closest homestead within 300 m was selected for sampling. Spatial distribution of
coccidian infection in these Counties was done using QGIS 2.0.1 software for mapping.
Data on nutrition, seasons, housing, disease occurrence, veterinary attention, herd profile and
use of the animals were obtained using questionnaire interviews. From the clinical examination, physiological status (lactating, pregnant), concurrent infections and presence of
other endoparasites and ectoparasites were ruled in or out. Faecal samples (at least 5 gm)
were collected directly from the rectum via digital extraction using lubricated gloves.
Samples were analysed using McMaster and Kato Katz techniques. R Statistical software was
used for univariate and multivariate logistic regression analysis of prevalence of coccidiosis
in relation to various factors at p=0.1 and p=0.05 respectively.
A total of 983 cattle were sampled from 416 households in all 3 Counties. Of this study
population, 66% were female; about 273 cows had calved before in their life time at the time
of study, 42.6% of these had calved within one year from the time of study. Prevalence of
coccidia infection obtained using McMaster technique was 32.76% while Kato Katz
technique indicted a prevalence of 8.75%. Given that these tests used different principles, the
agreement was very weak with a calculated Kappa of 0.272 and a standard error of 0.029
(p=0.05).
All significant variables at p=0.1 were then modelled in logistic regression model at p=0.05
with a backward elimination approach. These variables included: age, sex/gender,
prophylactic treatment, regular vet visits, ploughing, treatment, lice and tick infestation. Only
age was found to be statistically significant in explaining coccidia infection in cattle. Older
animals were 0.71 times less likely to suffer from coccidiosis relative to younger animals.
Clustering of bovine coccidia infection in space was evident with the resulting heat map
indicating hotspots in South of Busia and North of Busia. Relationship between these clusters
and geographical features revealed increased disease occurrence observed along river basins.
This study showed that coccidia infection in cattle is prevalent in Busia, Bungoma and Siaya
Counties at 32.76%. However coccidiosis in these three Counties presents in the subclinical
form and thus no characteristic /overt clinical signs. | en_US |
dc.description.department | a
Department of Psychiatry, University of Nairobi, ; bDepartment of Mental Health, School of Medicine,
Moi University, Eldoret, Kenya | |