A Bayesian decision-theoretic framework to evaluate and optimize decision making for mastitis control in the UK Mastitis Control Scheme.

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Bovine mastitis is the foremost endemic infectious disease of dairy cattle and remains a major challenge to the UK and worldwide dairy industries. Although there have been numerous studies reporting cow and herd risk factors for bovine mastitis, no research has been conducted to evaluate optimal decision making in different farm circumstances; this is a critical unknown element in mastitis control. The purpose of this research is to use a Bayesian decision analytic framework to investigate the hypothesis that mastitis control can be improved by adopting a 'best strategy' in given farm circumstances. To conduct this research, detailed data are available from a recently launched national mastitis control scheme, the DairyCo Mastitis Control Plan (www.mastitiscontrolplan.co.uk) that has been developed by our industrial partner DairyCo. We will use the rich data from at least 600 farms in the scheme which includes detailed information on herd size, farm facilities and manpower, management interventions and detailed records of clinical and subclinical mastitis. For the initial statistical analysis, sample size estimates indicate that differences in clinical and subclinical mastitis of 5% will be detectable for individual or groups of management interventions, and this is deemed to be a clinically important effect size. A Bayesian decision analytic framework will be constructed to allow synthesis of the initial multivariable data analysis of the National Scheme data with economic and production information, to explicitly represent the decision process associated with mastitis control. The Bayesian framework provides a structure that will allow synthesis of multiple sources of information and also for uncertainty (risk) to be evaluated in the cost benefit of different interventions. Such an approach, often termed probabilistic sensitivity analysis, is now required by the National Institute of Clinical Excellence (NICE) for the evaluation of human medical interventions, but is rarely used in animal health. In Year 1, collation and initial analysis of the data from 600 farms in the National Mastitis Control Scheme will occur. In Year 2, the Bayesian framework will be constructed to quantify the relative importance of, and uncertainty in, different preventive strategies for bovine mastitis. We will specifically predict the consequences of different interventions (and groups of interventions), in different farm circumstances on the 'Incremental Net Benefit' (net financial return) of each strategy. Therefore, given farm patterns of mastitis (farms in the national scheme are grouped into four categories according to the predominant pattern of mastitis on the unit) and a set of possible interventions, we will predict the optimum prevention strategy and the associated uncertainty of a financial return. We will employ a single integrated Bayesian procedure, using Markov chain Monte Carlo, which has the mathematical advantage of allowing all joint parameter uncertainty to be propagated through the model and is important because evaluation of the uncertainty associated with different decisions is a key area of investigation. In Year 3, the predictive value of models will be evaluated using both 'within' and 'out of model' posterior predictions (using new farms in the national scheme) and in the second 6 months of Year 3, the student will work with the industrial partner. In Year 4, results from the decision models will be incorporated into the DairyCo National Mastitis Control Scheme software (in collaboration with DairyCo partner QMMS Ltd) to inform on farm decision making. This software will allow scheme participants to identify the management interventions that, for specific farms, are most likely to provide the greatest health and financial benefits, and will ensure that the research has an immediate impact to improve mastitis in UK dairy cows.