Abstract

Coagulase-negative staphylococci (CNS) are frequently isolated from quarters with subclinical mastitis, teat apices, and the cows’ environment. Virulence, ecology, epidemiological behavior, and effect on udder health vary between different CNS species. Staphylococcus chromogenes, Staph. simulans, and Staph. xylosus are frequently present in milk and have a more substantial effect on quarter milk somatic cell count than other species. Therefore, these species are considered the “more relevant” CNS. As species-specific factors associated with CNS intramammary infection (IMI) have not yet been identified and susceptibility for IMI differs between cows and quarters, this study aimed to identify predictors for CNS IMI at the cow and quarter level (some of them changing over time) with a specific focus on the aforementioned more relevant CNS. Precise data were available from a longitudinal study (3,052 observations from 344 quarters from 86 dairy cows belonging to 3 commercial dairy herds). All CNS were molecularly identified to the species level, and multivariable, multilevel logistic regression models taking into account the longitudinal nature of the data, were fit to study the likelihood of infection. Staphylococcus chromogenes, Staph. xylosus, and Staph. cohnii were the most frequently isolated species from CNS IMI in older cows, whereas Staph. chromogenes, Staph. xylosus, and Staph. simulans were the main species found in IMI in heifers. Quarters from heifers (as opposed to multiparous cows), from heifers and multiparous cows in third or fourth month in lactation (as opposed to early lactation, <60 d in milk), and with an increasing quarter milk SCC were more likely to be infected with the more relevant CNS species. Quarter milk SCC was identified as the sole statistically significant predictor for IMI with other CNS species, although the size of the effect was lower [odds ratio of 1.6 (1.4–1.9) vs. 2.1 (1.8–2.5)] than the effect for IMI with the more relevant CNS. As a strong herd effect was present, studying herd-level predictors is warranted.

Full Text
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