Search

Given that bovine herpesvirus 1 (BoHV-1) -the causative agent of Infectious Bovine Rhinotracheitis (IBR)- is still endemic in most European countries, BoHV-1 free herds are subject to a considerable risk of (re)introduction of the virus. The aim of this literature review was to describe published, quantified risk factors that are relevant for the introduction of BoHV-1. The risk factors described in this study can be used as input for modeling eradication scenarios and for communication on biosecurity measures to stakeholders. A literature search was conducted in November 2020 in two major online search databases, PubMed and Web of Science. The search criteria “risk factor” combined with different synonyms for BoHV-1 were explored, which resulted in 564 hits. Only studies performed in Europe, written in Dutch, English, French, German or Spanish with an English summary and that quantified risk factors for introduction of BoHV-1 into cattle herds were included. Studies had to quantify the risk factors with crude odds ratios (OR), an estimate of the chance of a particular event occurring in an exposed group to a non-exposed group. After checking for duplicates and excluding articles that did not meet the inclusion criteria, 12 publications remained for this review. Risk factors were classified into seven groups, i.e., herd characteristics, management, animal characteristics, purchase, direct animal contact, neighborhood and indirect transmission routes. Most relevant factors for introduction of BoHV-1 into cattle herds include herd size, purchase of cattle, cattle density, age of cattle, distance to neighboring cattle herds and professional visitors. Together with other direct and indirect animal contacts, these factors are important when elimination of BoHV-1 is considered. A closed farming system and protective clothing for professional visitors can eliminate the major routes of introduction of BoHV-1 in cattle herds. To the best of our knowledge, this is the first systematic review solely focussing on measures that can be taken to control introduction of BoHV-1 into cattle herds. Besides testing, focus on managing these (biosecurity) factors will decrease the risk of introducing the virus.

Bovine viral diarrhea (BVD) is controlled in many countries by detection and culling of persistently infected (PI) animals. The most important risk factor for BVDV introduction is purchase. An introduced cow can be PI and transmit the virus to other cattle in the herd. If she is not PI but is pregnant, there is still a risk because the subsequently born calf may be PI, when she encountered the virus in early pregnancy. To control this risk, all cows > 1 year from non-BVDV-free herds that are introduced in herds that participate in the Dutch BVDV control program are tested for virus and antibodies. Depending on the results, subsequent measures such as suspension of the BVDV-free status, removing the animals from the herd, or testing the off-spring of the cow for virus, are undertaken. The aim of this study was to evaluate the results of this risk mitigating measure. Data on cattle movements, calving's, herd-level BVDV status, and animal-level test data were available from all dairy herds that participated in the national BVDV control program (>14,000 dairy herds) for the year 2019. The data were combined and parameters of interest were calculated, i.e., (i) the number of purchased BVD virus positive cattle and (ii) the number of BVD virus positive calves born from purchased cows within 9 months after introduction. In 2019, 217,301 cattle were introduced in Dutch dairy herds that participated in the BVDV control program. Of these, 49,820 were tested for presence of BVD virus and 27 (0.05%) cows introduced in 21 different herds tested BVD virus positive. Out of 46,727 cattle that were tested for antibodies, 20.5% tested positive. The seropositive cows produced 4,341 viable calves, of which 3,062 were tested for virus and subsequently, 40 (1.3%) were found BVD virus positive. These 40 BVD virus positive calves were born in 23 herds. The risk mitigating measure led to detection of 67 BVD virus positive animals in 44 unique herds in 2019. This study makes plausible that the probability and impact of re-introduction of BVDV can be minimized by testing introduced cattle and their subsequently born calves.

Antimicrobial resistance (AMR) monitoring in animals is performed in commensal Escherichia coli, and other microorganisms relevant for human or veterinary health. Due to advances in the field and major reductions in cost, it is expected that whole-genome sequencing (WGS)-based antimicrobial susceptibility testing (AST) will (partly) replace culture-based AST. So far, no studies have been performed without using culture-based AST as the gold standard. Our aim was to use Bayesian latent class analysis to evaluate the accuracy of susceptibility testing of commensal E. coli by WGS-based AST versus culture-based AST as this test does not assume a gold standard. OpenBUGS was used to model two independent tests in three animal populations (N = 150, 50 bacterial isolates per population): veal calves, pigs, and broilers. This resulted in the first estimation of sensitivity and specificity of WGS-based AST versus culture-based AST to detect AMR without a gold standard. Both methods had high sensitivity (>0.92, lowest limit probability interval: 0.76) and specificity was generally high for both methods for all antimicrobial classes except for aminoglycosides and macrolides. We compared WGS results for different length and identity settings (%) of gene alignment and found few differences between the 60/90, 90/90 and 95/95 settings. We recommend to further investigate sensitivity and specificity of WGS-based AST by means of latent class analysis, especially for low-prevalent resistance.

Dairy cows are negatively affected by the introduction of bovine viral diarrhea virus (BVDV), and consequently, produce less milk. Existing literature on potential milk production losses is based on relatively outdated data and hardly evaluates milk production loss in relation to a new BVDV infection in a surveillance system. This study determined the annual and quarterly loss in milk production of BVDV introduction in 3,126 dairy herds participating in the Dutch BVDV-free program between 2007 and 2017. Among these herds, 640 were "breakdown-herds" that obtained and subsequently lost their BVDV-free status during the study period, and 2,486 herds obtained and retained their BVDV-free status during the study period. Milk yields before and after BVDV introduction were compared through annual and quarterly linear mixed models. The fixed variables for both models included herd type (breakdown-herd or free-herd), bovine viral diarrhea status (on an annual and quarterly basis), year, season, and a random herd effect. The dependent variable was the average daily milk yield on the test day. To define the possible BVDV-introduction dates, 4 scenarios were developed. In the default scenario, the date of breakdown (i.e., loss of the BVDV-free status) was assumed as the BVDV-introduction date. For the other 3 scenarios, the BVDV-introduction dates were set at 4, 6, and 9 mo before the date of breakdown, based on the estimated birth date of a persistently infected calf. In the default scenario, the loss in milk yield due to BVDV introduction occurred mainly in the first year after breakdown, with a reduction in yield of 0.08 kg/cow per day compared with the last year before breakdown. For the other 3 scenarios, the greatest yield reduction occurred in the second year after BVDV introduction, with a loss of 0.09, 0.09, and 0.1 kg/cow per day, respectively. For the first 4 quarters after BVDV introduction in the default scenario, milk yield loss was 0.14, 0.09, 0.02, and 0.08 kg/cow per day, respectively. These quarterly results indicated that milk yield loss was greatest in the first quarter after BVDV introduction. Overall, BVDV introduction had a negative, but on average a relatively small, effect on milk yield for herds participating in the BVDV-free program. This study will enable dairy farmers and policymakers to have a clearer understanding of the quantitative milk production effect of BVDV on dairy farms in a control program.

Two vector-borne infections have emerged and spread throughout the north-western part of Europe in the last decade: Bluetongue virus serotype-8 (BTV-8) and the Schmallenberg virus (SBV). The objective of the current study was to compare three statistical methods when applied in a syndromic surveillance context for the early detection of emerging diseases in cattle in the Netherlands. Since BTV-8 and SBV both have a negative effect on milk production in dairy cattle, routinely collected bulk milk recordings were used to compare the three statistical methods in their potential to detect drops in milk production during a period of seven years in which BTV-8 and SBV emerged. A Cusum algorithm, Bayesian disease mapping model, and spatiotemporal cluster analysis using the space-time scan statistic were performed and their performance in terms of sensitivity and specificity was compared. Spatiotemporal cluster analysis performed best for early detection of SBV in cattle in the Netherlands with a relative sensitivity of 71% compared to clinical surveillance and 100% specificity in a year without major disease outbreaks. Sensitivity to detect BTV-8 was low for all methods. However, many alerts of reduced milk production were generated several weeks before the week in which first clinical suspicions were reported. It cannot be excluded that these alerts represent the actual first signs of BTV-8 infections in cattle in the Netherlands thus leading to an underestimation of the sensitivity of the syndromic surveillance methods relative to the clinical surveillance in place.

There is a plethora of meat-borne hazards – including parasites - for which there may be a need for surveillance. However, veterinary services worldwide need to decide how to use their scarce resources and prioritise among the perceived hazards. Moreover, to remain competitive, food business operators – irrespective of whether they are farmers or abattoir operators - are preoccupied with maintaining a profit and minimizing costs. Still, customers and trade partners expect that meat products placed on the market are safe to consume and should not bear any risks of causing disease.\ud \ud Risk-based surveillance systems may offer a solution to this challenge by applying risk analysis principles; first to set priorities, and secondly to allocate resources effectively and efficiently. The latter is done through a focus on the cost-effectiveness ratio in sampling and prioritisation. Risk-based surveillance was originally introduced into veterinary public health in 2006. Since then, experience has been gathered, and the methodology has been further developed. Guidelines and tools have been developed, which can be used to set up appropriate surveillance programmes. In this paper, the basic principles are described, and by use of a surveillance design tool called SURVTOOLS (https://survtools.org/), examples are given covering three meat-borne parasites for which risk-based surveillance is 1) either in place in the European Union (EU) (Trichinella spp.), 2) to be officially implemented in December 2019 (Taenia saginata) or 3) only carried out by one abattoir company in the EU as there is no official EU requirement (Toxoplasma gondii). Moreover, advantages, requirements and limitations of risk-based surveillance for meat-borne parasites are discussed.

To combat antimicrobial resistance (AMR), policymakers need an overview of evolution and trends of AMR in relevant animal reservoirs, and livestock is monitored by susceptibility testing of sentinel organisms such as commensal E. coli. Such monitoring data are often vast and complex and generates a need for outcome indicators that summarize AMR for multiple antimicrobial classes. Model‐based clustering is a data‐driven approach that can help to objectively summarize AMR in animal reservoirs. In this study, a model‐based cluster analysis was carried out on a dataset of minimum inhibitory concentrations (MIC), recoded to binary variables, for 10 antimicrobials of commensal E. coli isolates (N = 12,986) derived from four animal species (broilers, pigs, veal calves and dairy cows) in Dutch AMR monitoring, 2007–2018. This analysis revealed four clusters in commensal E. coli in livestock containing 201 unique resistance combinations. The prevalence of these combinations and clusters differs between animal species. Our results indicate that to monitor different animal populations, more than one indicator for multidrug resistance seems necessary. We show how these clusters summarize multidrug resistance and have potential as monitoring outcome indicators to benchmark and prioritize AMR problems in livestock.

Extended-spectrum β-lactamase and AmpC-producing Escherichia coli (ESBL/AmpC) are an emerging problem and are hypothesized to be associated with antimicrobial use (AMU), and more specifically with the use of third- and fourth-generation cephalosporins. Whether ESBL/AmpC also occur in organic dairy herds, which have restricted AMU, is not known. Additionally, it is unknown whether, in addition to restricted AMU, other factors in organic herd management are associated with ESBL/AmpC herd status. The aim of this study was to estimate the prevalence of ESBL/AmpC in organic dairy herds in the Netherlands. Subsequently, the relationships between the ESBL/AmpC herd status and AMU and between ESBL/AmpC herd status and farmers' management were assessed in organic dairy herds. For this study, 90 randomly selected, officially registered organic dairy herds were included. The ESBL/AmpC herd status was determined based on the bacteriological culture result of a slurry sample. The sensitivity of testing slurry samples for ESBL/AmpC herd status is less than 100% for detecting herds with a low ESBL/AmpC prevalence. For that reason, herds that tested positive for ESBL/AmpC in slurry were defined as positive and herds with negative slurry samples were defined as unsuspected. A comprehensive questionnaire on management practices was conducted and records on specified antimicrobials that were provided to these herds by the veterinary service providers were obtained. From the data on antimicrobial supplies by the veterinarian, the animal daily defined dose of antimicrobials per farm per year (DDDAF) was calculated. Descriptive statistics were used to describe the relation between the ESBL/AmpC herd status and DDDAF. Multivariable logistic regression models were used to evaluate management factors associated with the ESBL/AmpC herd status. We found ESBL/AmpC in 12 of the 90 (13%; 95% confidence interval = 7-22%) slurry samples from organic dairy herds. The median DDDAF in organic dairy herds was 0.5, which was not significantly different between ESBL/AmpC-positive and -unsuspected dairy herds. No association could be found between the use of different types of antimicrobials, such as third- and fourth-generation cephalosporins, and ESBL/AmpC herd status. Factors that were associated with higher odds of being ESBL/AmpC-positive were pig farms located within a 2-km radius of the barn, applying parental treatment for clinical mastitis, and providing milk replacer to the female calves after colostrum intake. The prevalence of ESBL/AmpC in organic dairy herds appeared lower than the prevalence in previous studies conducted in conventional dairy herds. Apparently, ESBL/AmpC are also present in herds with low AMU; this indicates that other factors than AMU are also associated with ESBL/AmpC herd status.

The aims of our study were to calculate the most appropriate cut-off value for milk samples in a serum-validated Mycobacterium avium subsp. paratuberculosis (MAP) ELISA and to analyze MAP ELISA responses in milk samples from vaccinated and nonvaccinated dairy goats in the Netherlands. Analyzed herds were representative for location and herd size of dairy goat herds in the Netherlands. A significantly higher proportion of the analyzed 49 herds were organic as compared with the total Dutch dairy goat population. First, the MAP ELISA was optimized using 992 paired serum and milk samples. At a cut-off of 25 S/P%, the relative sensitivity (Se) was 58.4% (n = 992, 95% CI: 48.8%&minus, 67.6%) and relative specificity (Sp) was 98.5% (n = 992, 95% CI: 97.5%&minus, 99.2%), as compared to serum ELISA results. The percentage of positively tested herds was 78.2% (n = 49, 95% CI: 63.4%&minus, 88.1%). The percentage of positive milk samples per herd (n = 22) was on average 4.6% (median, min, and max of 4.7%, 0.0%, and 10.7%, respectively). Average age of ELISA-positive (3.2 years) and -negative goats (3.2 years) was not different. Significantly more vaccinated goats tested positive (6.7%) as compared with nonvaccinated goats (1.1%). This study shows that a high number of vaccinated and nonvaccinated commercial dairy goat herds in the Netherlands have MAP-ELISA-positive goats.

Background Monitoring of antimicrobial resistance (AMR) in animals is essential for public health surveillance. To enhance interpretation of monitoring data, evaluation and optimisation of AMR trend analysis is needed. Aims To quantify and evaluate trends in AMR in commensal Escherichia coli, using data from the Dutch national AMR monitoring programme in livestock (1998–2016). Methods Faecal samples were collected at slaughter from broilers, pigs and veal calves. Minimum inhibitory concentration values were obtained by broth microdilution for E. coli for 15 antimicrobials of eight antimicrobial classes. A Poisson regression model was applied to resistant isolate counts, with explanatory variables representing time before and after 2009 (reference year); for veal calves, sampling changed from 2012 represented by an extra explanatory variable. Results Resistant counts increased significantly from 1998-2009 in broilers and pigs, except for tetracyclines and sulfamethoxazole in broilers and chloramphenicol and aminoglycosides in pigs. Since 2009, resistant counts decreased for all antimicrobials in broilers and for all but the phenicols in pigs. In veal calves, for most antimicrobials no significant decrease in resistant counts could be determined for 2009–16, except for sulfamethoxazole and nalidixic acid. Within animal species, antimicrobial-specific trends were similar. Conclusions Using Dutch monitoring data from 1998-2016, this study quantified AMR trends in broilers and slaughter pigs and showed significant trend changes in the reference year 2009. We showed that monitoring in commensal E. coli is useful to quantify trends and detect trend changes in AMR. This model is applicable to similar data from other European countries.

This study was conducted to assess: (1) a change in between-herd prevalence of extended-spectrum and AmpC β-lactamase-producing Escherichia coli (ESBL/AmpC-EC) between 2011 and 2013, the period during which the antimicrobial policy in animal husbandry in the Netherlands changed significantly, and (2) the prevalence of ESBL/AmpC-EC in individual calves, young stock, and dairy cows in the Netherlands. In 196 randomly selected conventional dairy herds, faecal samples were collected from calves (maximum n = 15), and randomly selected young stock (n = 5) and dairy cows (n = 15). Additionally, fresh faecal samples were collected from five different places on the floors where the dairy cows were housed. Samples were screened for E. coli with non-wild type susceptibility for cefotaxime and isolates were phenotypically confirmed as ESBL/AmpC-producing by disc diffusion, using cefotaxime and ceftazidime with and without clavulanic acid, and cefoxitin. Samples containing ESBL/AmpC-EC were examined semi-quantitatively. In 59.6% of the dairy herds one or more samples tested positive for ESBL/AmpC-EC. The between-herd prevalence based on floor samples in 2013 (18.0%) was significantly lower than the prevalence in 2011 based on comparable samples (32.7%). The individual animal prevalence of ESBL/AmpC-EC, with a minimum shedding level of 103 cfu/g of faeces, was 19.3% in calves, 0.9% in young stock, and 0.8% in dairy cows. Although ESBL/AmpC-EC was found in the majority of dairy herds, the herd prevalence declined significantly between 2011 and 2013. Calves were found to have both, a much higher individual animal prevalence and a higher level of shedding than young stock and cows.

International audience; Certification-and-monitoring programs for paratuberculosis are based on repetitive herd testing to establish a herd's health status. The available tests have poor sensitivity. Infected but undetected herds may remain among certified "paratuberculosis-free" herds. The objective was to determine if truly free herds acquire a certified status and keep it over time when infected but undetected herds remain. The Dutch program was used as a basis to construct a mechanistic deterministic model of the evolution over 25 years of the number of herds per health status. Three health states for herds were defined: not detected as infected in the certification process to obtain a free status; not detected as infected by any of the repetitive tests for monitoring the certified free status; detected as infected. Among undetected herds, two types were defined: truly free versus undetected but infected. Transitions between states were due to the purchase of an infected animal, infection via the environment, clearance via culling or sales, detection of an infected animal, and certification. A sensitivity analysis was carried out. We showed that - for a 100% specific test only - most of the truly free herds at the beginning of the program got a certified free status and kept it over time. Most infected herds were either detected as infected or cleared. The number of certified truly free herds increased with a decrease in the animal-level prevalence or in the risk of purchasing an infected cattle, for example by restricting purchases to cattle from herds at the highest level of certification.