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

贝叶斯决策理论框架，用于评估和优化英国乳腺炎控制计划中乳腺炎控制的决策。

• 批准号: BB/I015493/1
• 金额: $ 13.3万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Training Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FI015493%2F1
• 关键词: Bayesian; decision; theoretic; framework; evaluate

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.

牛乳腺炎是奶牛最重要的地方性传染病，仍然是英国和世界范围内乳制品行业的主要挑战。虽然已经有大量的研究报告了奶牛和牛群患牛乳腺炎的危险因素，但没有研究对不同农场环境下的最佳决策进行评估；这是乳腺炎控制中一个关键的未知因素。本研究的目的是使用贝叶斯决策分析框架来调查假设，即在给定的农场环境中，通过采用“最佳策略”可以改善乳腺炎控制。为了进行这项研究，我们从最近启动的一项国家乳腺炎控制计划中获得了详细的数据，该计划是由我们的工业合作伙伴DairyCo制定的乳腺炎控制计划（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，包括畜群规模、农场设施和人力、管理干预措施以及临床和亚临床乳腺炎的详细记录的详细信息。对于最初的统计分析，样本量估计表明，个体或组管理干预可以检测到5%的临床和亚临床乳腺炎差异，这被认为是临床上重要的效应量。将构建贝叶斯决策分析框架，以允许将国家计划数据与经济和生产信息的初始多变量数据分析综合起来，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，也可以评估不同干预措施的成本效益的不确定性（风险）。这种通常被称为概率敏感性分析的方法，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600个农场的数据进行整理和初步分析。在第二年，贝叶斯框架将构建量化的相对重要性和不确定性，在不同的预防策略，牛乳腺炎。我们将具体预测不同农场环境下不同干预（和干预组）对每种策略的“增量净收益”（净财务回报）的影响。因此，考虑到乳腺炎的农场模式（国家计划中的农场根据单位乳腺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经济回报的不确定性。我们将采用一个单一的综合贝叶斯过程，使用马尔可夫链蒙特卡罗，它具有允许所有联合参数不确定性通过模型传播的数学优势，并且很重要，因为评估与不同决策相关的不确定性是研究的关键领域。在三年级，将使用“模型内”和“模型外”后验预测（使用国家计划中的新农场）来评估模型的预测价值。在三年级的后六个月，学生将与工业合作伙伴一起工作。在第四年，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的乳腺炎有直接的影响。