Management Style and Its Association with Bulk Milk Somatic Cell Count and Incidence Rate of Clinical Mastitis

Barkema, Herman W.; Ploeg, J.D. van der; Schukken, Y.H.; Lam, T.J.G.M.; Benedictus, G.; Brand, A.

doi:10.3168/jds.s0022-0302(99)75394-4

Cited by 150 publications

(130 citation statements)

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“…However, Dohoo (1993) stated that using SCC results before 9 DIM could overestimate the prevalence of infected heifers in the herd. On the other hand, Barkema et al (1999a) suggested that SCC results from a minimum of 2 DIM can be used for the determination of the udder health status. Because no absolute agreement exists on the number of days at the start of lactation that have to be excluded and because the higher SCC at the start of lactation seemed to stabilize within 4 DIM in our data, we decided to exclude only test-day results that were obtained in the first 4 d after calving.…”

Section: Discussionmentioning

confidence: 99%

“…Heifers having a first elevated SCC after 100 DIM likely got infected during lactation and in those heifers, risk factors are probably similar to those for multiparous cows with SCLM. In addition, test-day results obtained within the first 4 d after calving were excluded from the analyses because an elevated SCC in the first days of lactation could have a physiological background rather than being caused by mastitis (Dohoo, 1993;Barkema et al, 1999a).…”

Section: Definitionsmentioning

confidence: 99%

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Incidence of subclinical mastitis in Dutch dairy heifers in the first 100 days in lactation and associated risk factors

Santman-Berends

Riekerink

Sampimon

et al. 2012

Journal of Dairy Science

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Heifer mastitis is a problem and risk factors may differ between heifers and older cows. The aim of this study was to estimate the heifer subclinical mastitis (HSCLM) incidence based on elevated somatic cell count (SCC) in the first 100 d in lactation and the associated risk factors in Dutch dairy herds. In 2008, 173 farmers filled in a questionnaire regarding housing and herd management factors potentially related to udder health. In addition, monthly milk production and SCC data from all cattle were provided by the Dutch Royal Cattle Syndicate (CRV, Arnhem, the Netherlands). Heifer subclinical mastitis incidence was calculated at the herd level as the number of heifer cases divided by the number of heifers at risk in the first 100 d in milk. Linear regression models were used for the analyses. On average, 25.5% [95% confidence interval (CI): 23.9 to 27.0%] of the heifers had subclinical mastitis. Heifers with a high SCC (>150,000 cells/mL) on the first test day after calving that returned to SCC levels below the cut-off continued to have a higher SCC throughout the study period compared with heifers with a low SCC (≤150,000 cells/mL) at the first test day after calving. Housing heifers together with lactating cows close to calving was protective from HSCLM incidence compared with separate housing (−4.5%; 95% CI: −8.7 to −0.2%). In addition, herds in which the farmer removed supernumerary teats of calves had a 7.0% (95% CI: 2.8 to 11.3%) lower HSCLM incidence and day and night grazing was also protective (−5.9%; 95% CI: −10.6 to −1.3%). Herds that were milked with an automatic milking system had, on average, a 6.9% (95% CI: 2.2 to 11.5%) higher HSCLM incidence and submitting milk samples for bacteriological culturing in the previous year was also associated with a higher HSCLM incidence (4.1%; 95% CI: 1.1 to 7.1%). Heifer subclinical mastitis is prevalent in all dairy herds, with a large variation in incidence. A high SCC in heifers at the first test day after calving appears to indicate a prolonged effect on udder health. Several management factors were found to be associated with HSCLM incidence that may help in reducing HSCLM.

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Section: Discussionmentioning

confidence: 99%

Section: Definitionsmentioning

confidence: 99%

Incidence of subclinical mastitis in Dutch dairy heifers in the first 100 days in lactation and associated risk factors

Santman-Berends

Riekerink

Sampimon

et al. 2012

Journal of Dairy Science

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“…Various studies investigated risk factors in herds and their associations with BTMSCC (Erskine et al, 1987;Schukken et al, 1991;Wilson et al, 1995Wilson et al, , 1997Barkema et al, 1998a;Barkema et al, 1999;Khaitsa et al, 2000;Peeler et al, 2000;McDougall, 2003;Barnouin et al, 2004;Rodrigues et al, 2005;van Schaik et al, 2005;O'Reilly et al, 2006). Attention was also focused on the correlation between the incidence of subclinical or clinical mastitis and BTMSCC (Barkema et al, 1998b;McDougall, 2003;Lukas et al, 2005).…”

Section: Discussionmentioning

confidence: 99%

Bulk tank milk somatic cell count and sources of raw milk contamination with mastitis pathogens

Rysanek¹,

Babák²,

Zouharová³

2007

Vet. Med. - Czech

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The objective of this study was to probe the relationship between prevalence of selected principal mastitis pathogens and somatic cell counts in bulk tank milk samples. The sources of milk contamination were evaluated. The samples were collected from 298 dairy herds (with approximately 32 000 dairy cows). Only 48.3% of the bulk tank milk samples were free of contamination of pathogens of interest. Approximately 38.9% of the milk samples were contaminated with only one, 12.4% with two and 0.3% with three pathogens. The arithmetic mean of logarithmically transformed data of bulk tank milk somatic cell count rise in order: pathogen free, Pseudomonas aeruginosa, Streptococcus uberis, Escherichia coli and Staphylococcus aureus (5.381; 5.413; 5.495; 5.518; 5.563, respectively). The arithmetic mean differences between bulk tank milk somatic cell counts in pathogen-free and single-pathogen contaminated samples have revealed a significance for the Escherichia coli and Staphylococcus aureus groups (P < 0.01). Using binary logistic regression, a statistically highly significant relationship (P < 0.001) has been found between the number of contaminations of bulk tank milk samples with mastitis pathogens and bulk tank milk somatic cell counts. The relationship allows the determination of the probability of finding relevant mastitis pathogens in bulk tank milk samples with different levels of bulk tank milk SCC. A 63% probability can be defined at a cell count level of 400 000/ml and 20% at a cell count level of 100 000/ml. Analysis may reveal the potential sources of the bulk tank milk sample contamination, i.e. infected mammary glands or the environment. The presence of high levels of contamination along with a low bulk tank SCC may suggest an environmental source of contamination. The study clarified that a potential source of bulk tank milk contamination by relevant pathogens (the environment or the mammary gland) may be elucidated and the probability of the contamination of bulk tank milk samples with mastitis pathogens predicted by the analysis of relationship between the bulk tank milk somatic cell counts and the number of mastitis pathogen contaminations.

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“…Another choice is the use of a negative binomial distribution instead of the Poisson distribution [4,69,75]. The elementary estimation of the overdispersion in a Poisson regression [4,5,28] does not allow an accurate control of the significance of the tests concerning the factor effects. An analysis of variance, carried out from the ranks of the herds classified according to CMAST incidence (Kruskal-Wallis test) [34], is less precise in terms of risk factor identification, since independence hypotheses are still necessary for its use.…”

Section: At the Herd Levelmentioning

confidence: 99%

Statistical modelling for clinical mastitis in the dairy cow: problems and solutions

Gasqui¹,

Barnouin²

2003

Vet. Res.

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-Modelling case occurrence and risk factors for clinical mastitis, as a key multifactorial disease in the dairy cow, requires statistical models. The type of model used depends on the choice of perception or the study level: herd, lactation, animal, udder and quarter. The validity of the tests that are performed through these models is especially ensured when hypotheses of independence between statistical units are respected, and when the model adjustments do not involve overdispersion faced with the observed data. In the article, the main sources of overdispersion are identified according to the different levels of perception of mastitis risk. Then, the proposed solutions to control for overdispersion at each study level are discussed and the difficulty to compare the study results is highlighted through a variety of methodological choices of the authors. Two main categories of models are used for modelling clinical mastitis, i.e. generalist exploratory models and explanatory designed models. The contribution of the explanatory models to improve modelling accuracy and relevance is documented through the two main published methodological approaches, the first one being based on a states model, and the second on a survival model. The integration and optimisation of such explanatory modelling methods should be possible in the future in order to develop a more global explanatory model including herd risk factors, which could pertinently predict udder infections (both clinical and subclinical) at the cow, lactation, or even udder and quarter levels.clinical mastitis / dairy cow / epidemiology / modelling / overdispersion

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Management Style and Its Association with Bulk Milk Somatic Cell Count and Incidence Rate of Clinical Mastitis

Cited by 150 publications

References 19 publications

Incidence of subclinical mastitis in Dutch dairy heifers in the first 100 days in lactation and associated risk factors

Incidence of subclinical mastitis in Dutch dairy heifers in the first 100 days in lactation and associated risk factors

Bulk tank milk somatic cell count and sources of raw milk contamination with mastitis pathogens

Statistical modelling for clinical mastitis in the dairy cow: problems and solutions

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