P R Burton

University of Leicester, Leiscester, England, United Kingdom

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Publications (2)4.19 Total impact

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    ABSTRACT: To assess the prevalence and patterns of bacterial isolates, cultures were made from the dry mammary glands of dairy cows in six commercial dairy herds in the UK. Milk samples were taken from all four quarters of 480 cows at drying off and at weekly intervals from 14 days before to seven days after calving. A major mastitis pathogen was isolated from at least one quarter of 220 (45.8 per cent) of the cows and from more than one quarter of 90 (18.8 per cent) of them. During the late dry to calving period, of the 957 quarters with three culture results, a major mastitis pathogen was cultured from 236 (24.7 per cent) quarters of 186 (38.8 [corrected] per cent) cows. The most commonly isolated major pathogen was Escherichia coli, followed by Streptococcus uberis and coagulase-positive staphylococci. There were significant differences between the patterns of isolates from different farms and in different calving months, suggesting that the rate of infection was partially dependent on external conditions. The isolation of E. coli, S. uberis or coagulase-positive staphylococci from a cow during the late dry/periparturient period was associated with an increased risk of that cow being culled in the next lactation. Bayesian general linear mixed models were used to assess the associations between the different bacterial species. The probability of isolating either E. coli or S. uberis was significantly greater when the other organism was cultured in a milk sample; this was also true of coagulase-positive staphylococci and S. uberis. When Corynebacterium species were isolated from a milk sample, the probability of isolating coagulase-positive staphylococci or S. uberis decreased significantly, and when coagulase-negative staphylococci were isolated the probability of isolating coagulase-positive staphylococci was reduced.
    The Veterinary record 02/2005; 156(3):71-7. · 1.80 Impact Factor
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    ABSTRACT: Two analytical approaches were used to investigate the relationship between somatic cell concentrations in monthly quarter milk samples and subsequent, naturally occurring clinical mastitis in three dairy herds. Firstly, cows with clinical mastitis were selected and a conventional matched analysis was used to compare affected and unaffected quarters of the same cow. The second analysis included all cows, and in order to overcome potential bias associated with the correlation structure, a hierarchical Bayesian generalised linear mixed model was specified. A Markov chain Monte Carlo (MCMC) approach, that is Gibbs sampling, was used to estimate parameters. The results of both the matched analysis and the hierarchical modelling suggested that quarters with a somatic cell count (SCC) in the range 41,000-100,000 cells/ml had a lower risk of clinical mastitis during the next month than quarters <41,000 cell/ml. Quarters with an SCC >200,000 cells/ml were at the greatest risk of clinical mastitis in the next month. There was a reduced risk of clinical mastitis between 1 and 2 months later in quarters with an SCC of 81,000-150,000 cells/ml compared with quarters below this level. The hierarchical modelling analysis identified a further reduced risk of clinical mastitis between 2 and 3 months later in quarters with an SCC 61,000-150,000 cells/ml, compared to other quarters. We conclude that low concentrations of somatic cells in milk are associated with increased risk of clinical mastitis, and that high concentrations are indicative of pre-existing immunological mobilisation against infection. The variation in risk between quarters of affected cows suggests that local quarter immunological events, rather than solely whole cow factors, have an important influence on the risk of clinical mastitis. MCMC proved a useful tool for estimating parameters in a hierarchical Bernoulli model. Model construction and an approach to assessing goodness of model fit are described.
    Preventive Veterinary Medicine 07/2004; 64(2-4):157-74. · 2.39 Impact Factor