Designing a Syndromic Bovine Mortality Surveillance System: Lessons Learned From the 1-Year Test of the French OMAR Alert Tool

Abstract

Between May 2018 and 2019, a syndromic bovine mortality surveillance system (OMAR) was tested in 10 volunteer French départements (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 départements and the OMAR central coordination cell assess the performance of the system. During the 1-year test, the system showed highly variable sensitivity among départements. 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 départements). As a result, in the pilot départements, 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 départements to evaluate the effectiveness of the improvements agreed upon at the final meeting.

Bovine mortality: the utility of two data sources for the provision of population-level surveillance intelligence

“…To further develop animal health surveillance, periodic retrospective analysis could be automated and implemented providing data could be accessed in a timely manner and technological issues were not an obstruction. This has been achieved elsewhere ( 11 ). It would require further investment of resources as well as policy decisions about the time-scale of the surveillance and the purpose of the analysis.…”

“…One data source that could provide a complementary indication of cattle mortality at a population level is fallen stock collections data. In two other countries, Spain and France, researchers have examined the utility of centralized fallen stock data for surveillance of health status of animal populations in different regions [( 10 , 11 ), respectively]. In Britain, the National Fallen Stock Company (NFSCo) collects such data as part of its business operations.…”

“…While this data source may provide regular, comprehensive, temporal and spatially representative datasets, access to it in a timely fashion manner can be a challenge, especially for external parties. Consequently, the potential for use of such dataset to provide early warning systems for detecting aberrations in health events, while technically possible (10) may not be fully realized (3). Demonstration of this potential, through an examination of the utility of these data in retrospective analyses, may help to encourage the investment of both time and resources in the development necessary to move from applied research toward operational systems.…”

Exploring Options for Syndromic Surveillance in Aquaculture: Outbreak Detection of Salmon Pancreas Disease Using Production Data from Norwegian Farms

“…Previous research adopted autoregressive integrated moving average models [8,46] or the Holt-Winters forecasting method to deal with temporal correlation. In cases where temporal correlation could be ignored or removed, control charts such as Shewhart charts, exponentially weighted moving average charts, and cumulative sum charts were common options of aberration detection for SyS systems [5,12,[47][48][49][50][51]. However, these methods were less suitable for our system since we could not assume the constancy of the analyzed time series.…”

Cattle herd typology for epidemiology, surveillance, and animal welfare: Method and applications in France

“…OMAR is the French syndromic surveillance system of cattle mortality that is still under pilot phase. In addition to the real time monitoring of mortality, the system provides mortality standards (rates and standardized mortality ratio) to compare cattle mortality at farm scale, taking into account the main production of farms (Sala et al, 2017).…”