Quantifying the effects of individual animal characteristics and climatological factors on faecal worm egg count shedding in donkeys

Citation

Christopher J Corbett, Sandy Love, Giles T. Innocent, Ian McKendrick, Jacqui. B. Matthews, Faith A. Burden, Matthew Denwood. Quantifying the effects of individual animal characteristics and climatological factors on faecal worm egg count shedding in donkeys. Presented at British Society for Parasitology Spring Meeting.

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Date presented: 
Monday 7 April 2014
Abstract

Cyathostomins, the predominant parasitic nematodes of equids, have developed varying degrees of resistance to all three classes of anthelmintic licensed for use in horses. It is essential that the effectiveness of alternative methods of control for these pathogens are quantified, including incorporating climatic data and the commonly advocated practice of removal of faeces from pasture. Here, we obtained monthly faecal worm egg counts (FWEC, n=4,460 individual counts) from 803 donkeys based at The Donkey Sanctuary (Devon, UK). The dataset also included age, sex, field, FWEC history and previous anthelmintic administrations in each individual, as well as the pasture hygiene management method applied in the field where the donkey was grazed. FWEC were analysed alongside local climatic data using a generalised linear mixed model to assess associations between these variables and each observed monthly FWEC. The preferred model was identified using a model selection algorithm based on penalised likelihoods, and associated a 2.1% decrease in FWEC per day with air frost two calendar months ago (p<0.001) and a 38% lower FWEC in groups with twice weekly manual faecal removal compared to those with no faecal removal (p=0.004). Other weather effects, both alone and as interaction terms with the average FWEC of the field were included in the model, alongside individual FWEC history with anthelmintic administration as interaction terms and date as a single term. Our study identifies factors that may be useful as part of on-going predictive modelling based methods of improving targeted selective therapy.

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