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

奶牛乳房炎是奶牛最主要的地方性传染病，对英国和世界范围内的奶牛业仍然是一个重大挑战。尽管有许多研究报告了奶牛和牛群的乳房炎风险因素，但尚未进行研究以评估不同农场环境下的最佳决策;这是乳房炎控制中的关键未知因素。本研究的目的是使用贝叶斯决策分析框架，调查的假设，乳腺炎的控制，可以通过采取“最佳策略”，在给定的农场情况下得到改善。为了进行这项研究，我们的工业合作伙伴DairyCo最近推出了一项国家乳腺炎控制计划，即DairyCo乳腺炎控制计划（www.mastitiscontrolplan.co.uk）。我们将在该计划中使用来自至少600个农场的丰富数据，其中包括牛群规模、农场设施和人力、管理干预措施的详细信息以及临床和亚临床乳腺炎的详细记录。对于初始统计分析，样本量估计值表明，对于单个或各组管理干预措施，可检测到临床和亚临床乳腺炎的差异为5%，这被认为是具有临床意义的效应量。将构建贝叶斯决策分析框架，以允许将国家计划数据的初始多变量数据分析与经济和生产信息进行合成，以明确表示与乳腺炎控制相关的决策过程。贝叶斯框架提供了一种结构，可以综合多种信息来源，也可以在不同干预措施的成本效益中评估不确定性（风险）。这种方法通常被称为概率敏感性分析，现在被国家临床卓越研究所（NICE）要求用于评估人类医疗干预措施，但很少用于动物健康。在第一年，将对国家乳腺炎控制计划中的600个农场的数据进行整理和初步分析。在第二年，将构建贝叶斯框架，以量化牛乳房炎不同预防策略的相对重要性和不确定性。我们将具体预测不同干预措施（和干预措施组）的后果，在不同的农场情况下，对每种策略的“增量净效益”（净财务回报）。因此，给定乳房炎的农场模式（国家计划中的农场根据单位上乳房炎的主要模式分为四类）和一组可能的干预措施，我们将预测最佳预防策略和相关的财务回报的不确定性。我们将采用一个单一的集成贝叶斯过程，使用马尔可夫链蒙特卡罗，它具有数学上的优势，允许所有的联合参数的不确定性，通过模型传播，是很重要的，因为与不同的决策相关的不确定性的评估是一个关键的调查领域。在第三年，模型的预测值将使用“内”和“模型外”后验预测（使用国家计划中的新农场）进行评估，在第三年的第二个6个月，学生将与工业合作伙伴合作。在第4年，决策模型的结果将被纳入DairyCo国家乳腺炎控制计划软件（与DairyCo合作伙伴QMMS Ltd合作），为农场决策提供信息。该软件将允许计划参与者确定管理干预措施，对于特定的农场，最有可能提供最大的健康和经济效益，并将确保研究对改善英国奶牛乳房炎产生直接影响。

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.