Sala Carole, Vinard Jean-Luc, Pandolfi Fanny, Lambert Yves, Calavas Didier, Dupuy Céline, Garin Emmanuel, Touratier Anne
Epidemiology and Support to Surveillance Unit, University of Lyon-ANSES Lyon, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), Lyon, France.
National Technical Grouping of Vets Association (SNGTV), Paris, France.
Front Vet Sci. 2020 Jan 9;6:453. doi: 10.3389/fvets.2019.00453. eCollection 2019.
Between May 2018 and 2019, a syndromic bovine mortality surveillance system (OMAR) was tested in 10 volunteer French (French intermediate-level administrative unit) to assess its performance in real conditions, as well as the human and financial resources needed to ensure normal functioning. The system is based on the automated weekly analysis of the number of cattle deaths reported by renderers in the Fallen Stock Data Interchange Database established in January 2011. In our system, every Thursday, the number of deaths is grouped by ISO week and small surveillance areas and then analyzed using traditional time-series analysis steps (cleaning, prediction, signal detection). For each of the five detection algorithms implemented (i.e., the exponentially weighted moving average chart, cumulative sum chart, Shewhart chart, Holt-Winters, and historical limits algorithms), seven detection limits are applied, giving a signal score from 1 (low excess mortality) to 7 (high excess mortality). The severity of excess mortality (alarm) is then classified into four categories, from very low to very high, by combining the signal scores, the relative excess mortality, and the persistence of the signal(s) over the previous 4 weeks. Detailed and interactive weekly reports and a short online questionnaire help pilot and the OMAR central coordination cell assess the performance of the system. During the 1-year test, the system showed highly variable sensitivity among . This variability was partly due not only to the demographic distribution of cattle (very few signals in low-density areas) but also to the renderer's delay in reporting to the Fallen Stock Data Interchange Database (on average, only 40% of the number of real deaths had been transmitted within week, with huge variations among ). As a result, in the pilot , very few alarms required on-farm investigation and excess mortality often involved a small number of farms already known to have health or welfare problems. Despite its perfectibility, the system nevertheless proved useful in the daily work of animal health professionals for collective and individual surveillance. The test is still ongoing for a second year in nine to evaluate the effectiveness of the improvements agreed upon at the final meeting.
2018年5月至2019年期间，在法国10个志愿行政区（法国中级行政单位）对一种综合征牛死亡率监测系统（OMAR）进行了测试，以评估其在实际情况下的性能，以及确保其正常运行所需的人力和财力资源。该系统基于对2011年1月建立的病死畜数据交换数据库中处理商报告的牛死亡数量进行每周自动分析。在我们的系统中，每周四，死亡数量按ISO周和小监测区域进行分组，然后使用传统的时间序列分析步骤（清理、预测、信号检测）进行分析。对于实施的五种检测算法（即指数加权移动平均图、累积和图、休哈特图、霍尔特-温特斯算法和历史限值算法）中的每一种，应用七个检测限，给出从1（低超额死亡率）到7（高超额死亡率）的信号评分。然后，通过结合信号评分、相对超额死亡率以及信号在过去4周内的持续性，将超额死亡率（警报）的严重程度分为从极低到极高的四类。详细且交互式的每周报告以及一份简短的在线调查问卷有助于试点人员和OMAR中央协调小组评估系统性能。在为期1年的测试期间，该系统在各行政区之间表现出高度可变的敏感性。这种变异性部分不仅归因于牛的人口分布（低密度地区信号极少），还归因于处理商向病死畜数据交换数据库报告的延迟（平均而言，在一周内仅传输了实际死亡数量的40%，各行政区之间差异巨大）。结果，在试点行政区，很少有警报需要进行农场调查，而且超额死亡率往往涉及少数已知存在健康或福利问题的农场。尽管该系统仍有可完善之处，但它在动物卫生专业人员的日常工作中被证明对集体和个体监测很有用。在九个行政区，该测试仍在进行第二年，以评估在最后一次会议上商定的改进措施的有效性。
