Cohort-level disease prediction by extrapolation of individual-level predictions in transition dairy cattle

Publication date: Available online 20 May 2019Source: Preventive Veterinary MedicineAuthor(s): L. Wisnieski, B. Norby, S.J. Pierce, T. Becker, J.C. Gandy, L.M. SordilloAbstractDairy cattle experience metabolic stress during the transition from late gestation to early lactation resulting in higher risk for several economically important diseases (e.g. mastitis, metritis, and ketosis). Metabolic stress is described as a physiological state composed of 3 processes: nutrient metabolism, oxidative stress, and inflammation. Current strategies for monitoring transition cow nutrient metabolism include assessment of plasma non-esterified fatty acids and beta-hydroxybutyrate concentrations around the time of calving. Although this method is effective at identifying cows with higher disease risk, there is often not enough time to implement intervention strategies to prevent health disorders from occurring around the time of calving. Previously, we published predictive models for early lactation diseases at the individual cow level at dry-off. We also previously investigated different methods of aggregating individual level biomarker data at dry off to predict cohort-level disease risk around the time of calving. However, it is unknown if predictive probabilities from individual-level models can be aggregated to the cohort level to predict cohort-level incidence. Therefore, our objective was to test different data aggregation methods using previously published models that represented the...
Source: Preventive Veterinary Medicine - Category: Veterinary Research Source Type: research