title: Detection of subclinical mastitis from on-line milking parlor data.
abstract: A model, based on automatically collected data, was developed for detection of subclinical mastitis. The logistic regression model was based on the following variables: milk electrical conductivity, milk production, parity, and DIM. Subclinical mastitis was defined as a minimal period of 1 wk in which the SCC was > 500 x 10(3) cells/ml. In contrast, periods were defined as healthy if the SCC was < 200 x 10(3) cells/ml. The resulting model had a sensitivity of 55% and specificity of 90% for individual milkings. For periods of 14 milkings, sensitivity was 54% and specificity 92% when the threshold for that period was > 6 electrical conductivity signals for high SCC. Based on these test characteristics, the model could be used as an initial screening tool in a herd with a high incidence of subclinical mastitis. Cows with a signal would have a higher probability of being diseased than the total population. In such herds, separation of milk from the signaled cows might be a possible management strategy to reduce the SCC in the bulk milk tank.
authors.affiliation: Utrecht University, Department of Herd Health and Reproduction, The Netherlands.
authors.forename: M
authors.initials: M
authors.lastname: Nielen
authors.forename: Y H
authors.initials: YH
authors.lastname: Schukken
authors.forename: A
authors.initials: A
authors.lastname: Brand
authors.forename: H A
authors.initials: HA
authors.lastname: Deluyker
authors.forename: K
authors.initials: K
authors.lastname: Maatje
mesh_terms: D000818:Animals; D002417:Cattle; D002452:Cell Count; D004553:Electric Conductivity; D005260:Female; D008414:Mastitis, Bovine / Q000175:diagnosis*; D008892:Milk / Q000166:cytology; D008954:Models, Biological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009862:Online Systems; D012044:Regression Analysis; D012680:Sensitivity and Specificity