Somatic Cell Count Distributions During Lactation Predict Clinical Mastitis

• Published in: Journal of dairy science Vol. 87; no. 5; pp. 1256 - 1264
• Main Authors: Green, M.J., Green, L.E., Schukken, Y.H., Bradley, A.J., Peeler, E.J., Barkema, H.W., de Haas, Y., Collis, V.J., Medley, G.F.
• Format: Journal Article
• Language: English
• Published: Savoy, IL Elsevier Inc 01.05.2004; Am Dairy Sci Assoc; American Dairy Science Association
• Subjects: Animal productions; Animals; Biological and medical sciences; bovine mastitis; Cattle; Cell Count; coliform mastitis; cows; dairy cows; dairy herds; Escherichia coli; Escherichia coli Infections; Female; Food industries; Fundamental and applied biological sciences. Psychology; infection; Lactation; linear mixed models; mastitis; Mastitis, Bovine - diagnosis; Mastitis, Bovine - microbiology; milk; Milk - cytology; Milk and cheese industries. Ice creams; Models, Biological; parameters; prediction; quarter; resistance; selection; somatic cell count; Staphylococcal Infections; Staphylococcus aureus; statistical analysis; statistical models; statistical variation; Streptococcal Infections; Streptococcus uberis; Terrestrial animal productions; udder quarters; Vertebrates; CM; mastitis; QSCC; somatic cell count; CSCC; Somatic cell; Mastitis; Lactation; Distribution; Dairy industry
• ISSN: 0022-0302; 1525-3198
• DOI: 10.3168/jds.s0022-0302(04)73276-2

This research investigated somatic cell count (SCC) records during lactation, with the purpose of identifying distribution characteristics (mean and measures of variation) that were most closely associated with clinical mastitis. Three separate data sets were used, one containing quarter SCC (n = 1444) and two containing cow SCC (n = 933 and 11,825). Clinical mastitis was defined as a binary outcome, present or absent, for each lactation, and SCC were log (base 10) transformed. A generalized linear mixed model within a Bayesian framework was used for analysis. Parameters were estimated using Markov Chain Monte Carlo with Gibbs sampling. Results from the 3 data sets were similar. Increased maximum and standard deviation log SCC during lactation, rather than increased geometric mean, were the best overall indicators of clinical mastitis. Distributions of SCC were also investigated separately for different mastitis pathogens. Increased maximum log SCC was associated with clinical mastitis caused by all pathogen types. Increased standard deviation log SCC was associated with Staphylococcus aureus, and Streptococcus uberis clinical mastitis and increased coefficient of variation log SCC (standard deviation divided by mean) was associated with Escherichia coli clinical mastitis. Increased geometric mean lactation SCC was associated with an increased risk of Staph. aureus clinical mastitis but a reduced risk of E. coli clinical mastitis. Our results suggest that using measures of variation and maximum cow SCC would enhance the accuracy of predicting clinical mastitis, compared with geometric mean SCC, and therefore improve genetic programs that aim to select for clinical mastitis resistance. The results are also consistent with low SCC increasing susceptibility to some mastitis pathogens.