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

Gasqui, Patrick; Barnouin, Jacques

doi:10.1051/vetres:2003029

Cited by 9 publications

(4 citation statements)

References 81 publications

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“…An additional list of codes was developed to capture cases of mastitis recorded under the more general events "ill," "hospital," "disease," "treat," "died," or "better," based on corresponding remarks about the specific treatment that was applied in each case. The incidence rate was calculated for each herd, and edits were applied as follows: 1) herds with an incidence rate of less than 1% for 3 or more years were deleted; 2) herds with an incidence rate of less than 1% in 1 or 2 yr, plus a total incidence rate of less than 1% were also deleted; 3) multiple records of mastitis within a period of fewer than 6 consecutive days for an individual cow were assumed to describe the same case (Gasqui and Barnouin, 2003); and 4) each sire and herd was required to have a minimum of 5 observations. After editing, data consisted of 44,908 first-lactation cows, which were daughters of 1,861 sires, housed in 94 herds.…”

Section: Datamentioning

confidence: 99%

Poisson versus threshold models for genetic analysis of clinical mastitis in US Holsteins

Vázquez

Weigel

Gianola

et al. 2009

Journal of Dairy Science

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Typically, clinical mastitis is coded as the presence or absence of disease in a given lactation, and records are analyzed with either linear models or binary threshold models. Because the presence of mastitis may include cows with multiple episodes, there is a loss of information when counts are treated as binary responses. Poisson models are appropriated for random variables measured as the number of events, and although these models are used extensively in studying the epidemiology of mastitis, they have rarely been used for studying the genetic aspects of mastitis. Ordinal threshold models are pertinent for ordered categorical responses; although one can hypothesize that the number of clinical mastitis episodes per animal reflects a continuous underlying increase in mastitis susceptibility, these models have rarely been used in genetic analysis of mastitis. The objective of this study was to compare probit, Poisson, and ordinal threshold models for the genetic evaluation of US Holstein sires for clinical mastitis. Mastitis was measured as a binary trait or as the number of mastitis cases. Data from 44,908 first-parity cows recorded in on-farm herd management software were gathered, edited, and processed for the present study. The cows were daughters of 1,861 sires, distributed over 94 herds. Predictive ability was assessed via a 5-fold cross-validation using 2 loss functions: mean squared error of prediction (MSEP) as the end point and a cost difference function. The heritability estimates were 0.061 for mastitis measured as a binary trait in the probit model and 0.085 and 0.132 for the number of mastitis cases in the ordinal threshold and Poisson models, respectively; because of scale differences, only the probit and ordinal threshold models are directly comparable. Among healthy animals, MSEP was smallest for the probit model, and the cost function was smallest for the ordinal threshold model. Among diseased animals, MSEP and the cost function were smallest for the Poisson model, followed by the ordinal threshold model. In general, the models for count variables more accurately identified diseased animals and more accurately predicted mastitis costs. Healthy animals were more accurately identified by the probit model.

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Section: Datamentioning

confidence: 99%

Poisson versus threshold models for genetic analysis of clinical mastitis in US Holsteins

Vázquez

Weigel

Gianola

et al. 2009

Journal of Dairy Science

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“…This seems straightforward but studies differ greatly in their definition of CM and in the models used to compute h². As pointed out by Gasqui and Barnouin (2003), the type of model used depends on the unit of observation or the study level: herd, lactation, animal, udder and quarter. Clinical signs may be observed at the animal (de Haas et al, 2002) or quarter level (Nash et al, 2000).…”

Section: The Polygenic Hypothesismentioning

confidence: 99%

Genetic factors affecting susceptibility to udder pathogens

Detilleux

2009

Veterinary Microbiology

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11 12Many studies have identified genetic factors underlying resistance or susceptibility to 13 mastitis in dairy cows and heifers. Some authors focused on polygenic variation while others 14 searched for genes and/or quantitative trait loci with major effects on mastitis. Classical traits related 15 to mastitis include somatic cell counts, electrical conductivity and clinical cases of the disease. With 16 the development of automatic milking devices and '-omics' technologies, new traits are considered, 17 such as acute phase proteins, immunological assays, and milk flow patterns, and new biological 18 pathways are discovered, for example the role of mammary epithelium and the nervous system. The 19 usefulness of these traits for identification of resistant cows is discussed in relation to the biological 20 mechanisms underlying the development of the disease. In addition, the utility of these traits for 21 genetic improvement is reviewed. Finally, the problem of durability in resistance is addressed, 22 including co-evolution and the cost of resistance. 23 24

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“…Some studies suggested that low SCC was associated with high IRCM (Elbers et al, 1998;Beaudeau et al, 2002;Green et al, 2004), whereas other works did not reveal any interrelationship (Barkema et al, 1998;Beaudeau et al, 1998). These controversial results could depend on: 1) the situation of the herds in terms of prevailing pathogens (Barkema et al, 1998;; 2) the accuracy of CM evaluation including the epidemiological unit (Gasqui and Barnouin, 2003;, SCC measurement level (from most to least accurate: quarter, cow, bulk), and length of the period taken to observe CM cases after SCC measurement (Beaudeau et al, 2002); 3) the stability of management practices in the herds; 4) the fact that the farmers who are better at diagnosing CM might divert more high SCC milk (Elbers et al, 1998); 5) the observed (negative) association between milk yield level and SCC (Barkema et al, 1998), and (positive association) between milk yield level and CM risk (Schukken et al, 1990;Chassagne et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Dairy Management Practices Associated with Incidence Rate of Clinical Mastitis in Low Somatic Cell Score Herds in France

Barnouin

Bord

Bazin

et al. 2005

Journal of Dairy Science

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An epidemiological prospective study was carried out in French dairy herds with Holstein, Montbéliarde, or Normande cows and with low herd somatic cell scores. The objective was to identify dairy management practices associated with herd incidence rate of clinical mastitis. The studied herds were selected on a national basis, clinical cases were recorded through a standardized system, and a stable dairy management system existed. In the surveyed herds, mean milk yield was 7420 kg/cow per yr and mean milk somatic cell score was 2.04 (132,000 cells/mL). Overdispersion Poisson models were performed to investigate risk factors for mastitis incidence rate. From the final model, the herds with the following characteristics had lower incidence rates of clinical mastitis: 1) culling of cows with more than 3 cases of clinical mastitis within a lactation; 2) more than 2 person-years assigned to dairy herd management; 3) balanced concentrate in the cow basal diet. Moreover, herds with the following characteristics had higher incidence rates of clinical mastitis: 1) milking cows loose-housed in a straw yard; 2) no mastitis therapy performed when a single clot was observed in the milk; 3) clusters rinsed using water or soapy water after milking a cow with high somatic cell count; 4) 305-d milk yield >7435 kg; 5) herd located in the South region; 6) herd located in the North region; 7) cows with at least 1 nonfunctional quarter; and 8) premilking holding area with a slippery surface. The underlying mechanisms of some highlighted risk factors, such as milk production level and dietary management practices, should be investigated more thoroughly through international collaboration.

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

Cited by 9 publications

References 81 publications

Poisson versus threshold models for genetic analysis of clinical mastitis in US Holsteins

Poisson versus threshold models for genetic analysis of clinical mastitis in US Holsteins

Genetic factors affecting susceptibility to udder pathogens

Dairy Management Practices Associated with Incidence Rate of Clinical Mastitis in Low Somatic Cell Score Herds in France

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