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Application of Syndromic Surveillance on Routinely Collected Cattle Preventive Veterinary Medicine 124 (2016) 15–24 Contents lists available at ScienceDirect Preventive Veterinary Medicine j ournal homepage: www.elsevier.com/locate/prevetmed Application of syndromic surveillance on routinely collected cattle reproduction and milk production data for the early detection of outbreaks of Bluetongue and Schmallenberg viruses a,∗ a b,c Anouk Veldhuis , Henriëtte Brouwer-Middelesch , Alexis Marceau , b,c d,e b,c d Aurélien Madouasse , Yves Van der Stede , Christine Fourichon , Sarah Welby , a a,f Paul Wever , Gerdien van Schaik a GD Animal Health, PO Box 9, 7400 AA Deventer, The Netherlands b INRA, UMR1300, Biologie, Epidémiologie et Analyse de Risque en santé animale, F-44307 Nantes, France c LUNAM Université, Oniris, F-44307 Nantes, France d CODA-CERVA, Groeselenberg 99, 1180 Brussels, Belgium e Ghent University, Laboratory of Veterinary Immunology, Merelbeke, Belgium f Utrecht University, Department of Farm Animal Health, Utrecht, The Netherlands a r t i c l e i n f o a b s t r a c t Article history: This study aimed to evaluate the use of routinely collected reproductive and milk production data for the Received 20 April 2015 early detection of emerging vector-borne diseases in cattle in the Netherlands and the Flanders region of Received in revised form 2 December 2015 Belgium (i.e., the northern part of Belgium). Prospective space-time cluster analyses on residuals from a Accepted 11 December 2015 model on milk production were carried out to detect clusters of reduced milk yield. A CUSUM algorithm was used to detect temporal aberrations in model residuals of reproductive performance models on two Keywords: indicators of gestation length. The Bluetongue serotype-8 (BTV-8) epidemics of 2006 and 2007 and the Early-warning Schmallenberg virus (SBV) epidemic of 2011 were used as case studies to evaluate the sensitivity and Syndromic surveillance Vector-borne timeliness of these methods. The methods investigated in this study did not result in a more timely Cattle detection of BTV-8 and SBV in the Netherlands and BTV-8 in Belgium given the surveillance systems in place when these viruses emerged. This could be due to (i) the large geographical units used in the analyses (country, region and province level), and (ii) the high level of sensitivity of the surveillance systems in place when these viruses emerged. Nevertheless, it might be worthwhile to use a syndromic surveillance system based on non-specific animal health data in real-time alongside regular surveillance, to increase the sense of urgency and to provide valuable quantitative information for decision makers in the initial phase of an emerging disease outbreak. © 2015 Elsevier B.V. All rights reserved. 1. Introduction emerging diseases is of major importance to minimize their impact on animal welfare, animal trade and the costs associated with an Vector-borne emerging or re-emerging diseases can spread over outbreak. Two vector-borne diseases have emerged and spread large areas having major consequences for the livestock industry. In throughout the north-western part of Europe in the last decade: cattle, vector-borne diseases can be difficult to detect at the early Bluetongue virus serotype-8 (BTV-8) and the Schmallenberg virus onset of the disease when they result in non-specific symptoms, (SBV). BTV-8 emerged in August 2006 and re-emerged in July 2007 such as fever, loss of appetite and drop in milk production. They may and affected animal welfare and caused important economic losses therefore go unnoticed as such symptoms can be misinterpreted as (Wilson and Mellor, 2009). At the end of the vector-active season the result of common endemic diseases, environmental conditions in 2006, the southern provinces and some central provinces of the or reduced feed quality. However, early detection of vector-borne Netherlands were affected by BTV-8 (Van Schaik et al., 2008). In Belgium, BTV-8 had affected all provinces by January 2007, with a gradient of decreasing seropositivity toward the south and the west of the country (Méroc et al., 2008). SBV emerged in Europe in the ∗ Corresponding author. Fax: +31 0570660354. late summer of 2011, causing diarrhea and drop in milk production E-mail address: [email protected] (A. Veldhuis). http://dx.doi.org/10.1016/j.prevetmed.2015.12.006 0167-5877/© 2015 Elsevier B.V. All rights reserved. 16 A. Veldhuis et al. / Preventive Veterinary Medicine 124 (2016) 15–24 in adult cattle (Muskens et al., 2012) and congenital malforma- 2. Materials and methods tions in newborn ruminants (Hoffmann et al., 2012). SBV spread throughout the Netherlands and Belgium in one vector-active sea- 2.1. Data collection and validation son leading to high herd prevalences (Méroc et al., 2013; Veldhuis et al., 2013). Given their vector-borne nature, the spread of viruses 2.1.1. Milk production data like BTV and SBV is not limited by territorial borders and therefore Milk recording is the process of measuring milk yield and difficult to prevent. In addition, regarding SBV, control measures composition of (all) individual lactating cows on a regular basis, such as animal movement restrictions are expected to have little usually monthly, as a decision-support tool for farmers and the effect on the spread of the disease (Gubbins et al., 2014). cattle breeding programme. Milk recording data collected in the Surveillance systems for early warning of emerging animal dis- Netherlands and the Flanders region of Belgium were obtained from eases are often based on passive surveillance that consists of clinical the cattle improvement company CRV. CRV carries out milk record- diagnosis in the field and on the reporting of suspect cases. Elbers ing on a daily basis (week days only), covering 80–85% of Dutch and et al. (2008) concluded that BTV-associated clinical signs in cat- approximately 48% of Belgian dairy herds each month. Geograph- tle (and sheep) are important in the recognition of BTV, but as a ical location data were obtained from GD Animal Health and from diagnostic tool they result in a maximal sensitivity of 67% and speci- the SANITEL (National animal identification and registration system ficity of 72% in cattle (based on a combination of specific clinical in Belgium) database for Dutch and Flemish dairy herds, respec- signs). The performance of passive (clinical) surveillance systems tively. Unfortunately, in Flemish data 76% of the milk production for emerging diseases with non-specific clinical signs is likely to be records were not associated with herd location data. Therefore, no lower, in particular if the disease is unknown or if animal health space-time analyses could be carried out on such data. Milk pro- observers are unfamiliar with the disease. The efficiency of such duction data from Flanders are therefore not further described in a system depends on the awareness of the farmer and/or the vet- this manuscript. erinarian and the willingness to report a clinical suspicion to the Milk recording data from cows from 1 to 305 days in milk from authorities. Therefore, the implementation of a surveillance system the Netherlands were provided from January 1, 2003 to March based on non-specific herd production data may be more objective 31, 2012 and included an anonymous unique farm identification and may, in addition, support clinical observations from the field. number, location, date of milk recording (i.e., test-day), cow iden- This type of surveillance is known as syndromic surveillance. Syn- tification numbers and milk production per cow per test-day (kg). dromic surveillance systems are generally based on health-related Milk production data were aggregated at herd and test-day level to information (often clinical signs) or information obtained by pas- obtain a mean milk production per cow per herd per test-day (in sive surveillance that might precede (or may substitute for) formal kg). Records with 0 kg milk were removed from the dataset (0.3%). diagnosis (Hoinville et al., 2013). As a result, timeliness is an asset of Records with a mean milk production below 10 kg per cow or above syndromic surveillance. Also, syndromic surveillance is not usually 50 kg per cow were removed from the dataset (0.2%), because of focused on a specific hazard providing an ability to detect several possible recording errors. Herd location was unknown for 0.02% of diseases of interest with similar symptoms. These characteristics the data. These data were excluded from further analyses. make syndromic surveillance most applicable for early-warning surveillance (Hoinville et al., 2013). In veterinary health, the appli- 2.1.2. Reproduction data cation of syndromic surveillance is frequently based on clinical data Routinely collected data on reproductive events of cows in the from practitioners and laboratory data, although a number of other Netherlands and Flanders, such as artificial inseminations (AI) and data sources are being explored (Dórea et al., 2011; Perrin et al., dates of calving were supplied by CRV. Each dataset contained 2010). Two recent examples in the field of cattle health surveil- (anonymous) unique herd numbers, location at postal district and lance illustrate the potential of non-specific herd productivity data province level (data from the Netherlands only), cow identification for veterinary syndromic surveillance. Madouasse et al. (2014) eval- numbers, parity numbers, insemination dates and calving dates. uated milk yield from milk recording in dairy cattle as an indicator A maximum of 10 inseminations per parity was set to exclude to be included in an emerging disease surveillance system. Marceau repeated inseminations carried out for embryo transfer procedures. et al. (2013, 2014) described the use of routinely recorded repro- Data from nulliparous heifers were excluded. Also, data from cat- ductive events as indicators of disease emergence in dairy cattle. tle with parity >8 were excluded, in agreement with Marceau et al.
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