Tremblay Marlène, Bennett Tom, Döpfer Dörte
Departments of Medical Sciences, Section of Food Animal Production Medicine, School of Veterinary Medicine, University of Wisconsin-Madison, 2015 Linden Drive, Madison 53706, United States.
Departments of Medical Sciences, Section of Food Animal Production Medicine, School of Veterinary Medicine, University of Wisconsin-Madison, 2015 Linden Drive, Madison 53706, United States.
Prev Vet Med. 2016 Sep 15;132:1-13. doi: 10.1016/j.prevetmed.2016.07.016. Epub 2016 Aug 1.
Digital dermatitis (DD) is the most important infectious claw disease in the cattle industry causing outbreaks of lameness. The clinical course of disease can be classified using 5 clinical stages. M-stages represent not only different disease severities but also unique clinical characteristics and outcomes. Monitoring the proportions of cows per M-stage is needed to better understand and address DD and factors influencing risks of DD in a herd. Changes in the proportion of cows per M-stage over time or between groups may be attributed to differences in management, environment, or treatment and can have impact on the future claw health of the herd. Yet trends in claw health regarding DD are not intuitively noticed without statistical analysis of detailed records. Our specific aim was to develop a mobile application (app) for persons with less statistical training, experience or supporting programs that would standardize M-stage records, automate data analysis including trends of M-stages over time, the calculation of predictions and assignments of Cow Types (i.e., Cow Types I-III are assigned to cows without active lesions, single and repeated cases of active DD lesions, respectively). The predictions were the stationary distributions of transitions between DD states (i.e., M-stages or signs of chronicity) in a class-structured multi-state Markov chain population model commonly used to model endemic diseases. We hypothesized that the app can be used at different levels of record detail to discover significant trends in the prevalence of M-stages that help to make informed decisions to prevent and control DD on-farm. Four data sets were used to test the flexibility and value of the DD Check App. The app allows easy recording of M-stages in different environments and is flexible in terms of the users' goals and the level of detail used. Results show that this tool discovers trends in M-stage proportions, predicts potential outbreaks of DD, and makes comparisons among Cow Types, signs of chronicity, scorers or pens. The DD Check App also provides a list of cows that should be treated augmented by individual Cow Types to help guide treatment and determine prognoses. Producers can be proactive instead of reactive in controlling DD in a herd by using this app. The DD Check App serves as an example of how technology makes knowledge and advice of veterinary epidemiology widely available to monitor, control and prevent this complex disease.
数字性皮炎（DD）是养牛业中最重要的感染性蹄病，可引发跛行疫情。该病的临床病程可分为5个临床阶段。M阶段不仅代表不同的疾病严重程度，还具有独特的临床特征和转归。需要监测每个M阶段奶牛的比例，以便更好地了解和应对DD以及影响牛群中DD风险的因素。每个M阶段奶牛比例随时间或组间的变化可能归因于管理、环境或治疗的差异，并可能对牛群未来的蹄健康产生影响。然而，如果没有对详细记录进行统计分析，就无法直观地发现关于DD的蹄健康趋势。我们的具体目标是为统计培训、经验较少或缺乏支持程序的人员开发一款移动应用程序（应用），该应用将规范M阶段记录，自动进行数据分析，包括M阶段随时间的趋势、预测计算以及奶牛类型（即，奶牛类型I - III分别分配给无活动性病变的奶牛、活动性DD病变的单发病例和复发病例）的分配。这些预测是在常用于模拟地方病的类结构多状态马尔可夫链种群模型中DD状态（即M阶段或慢性体征）之间转换的平稳分布。我们假设该应用可用于不同详细程度的记录，以发现M阶段患病率的显著趋势，从而有助于做出明智决策，在农场预防和控制DD。使用了四个数据集来测试DD检查应用的灵活性和价值。该应用允许在不同环境中轻松记录M阶段，并且在用户目标和使用的详细程度方面具有灵活性。结果表明，该工具可发现M阶段比例的趋势，预测DD的潜在疫情，并对奶牛类型、慢性体征、评分员或牛栏进行比较。DD检查应用还提供一份应接受治疗的奶牛清单，并按个体奶牛类型进行补充，以帮助指导治疗和确定预后。通过使用该应用，生产者在控制牛群中的DD方面可以变被动为主动。DD检查应用是一个技术如何使兽医流行病学的知识和建议广泛可用以监测、控制和预防这种复杂疾病的示例。
