Risk factors for introduction of BVDV in the context of a mandatory control program in Dutch dairy herds.

Bovine Viral Diarrhea Virus (BVDV) is a common viral disease in cattle, causing economic losses in naive herds where the virus is introduced. In the Netherlands, a BVDV control program has been in place since 1998, evolving from voluntary to mandatory participation for dairy herds since April 2018. Participation in the BVDV control program is not mandatory for non-dairy farms. The aim of this study was to determine risk factors for new introduction of BVDV into certified BVDV-free herds in the context of a national BVDV control program in dairy herds. In this retrospective case-control study, survey data were collected from 149 dairy farms that lost their BVDV-free status (case herds) and 148 matched dairy farms that maintained their BVDV-free status (control herds) between 2018 and 2021. The survey captured information about potential risk factors and herd characteristics in the 2 years leading up to the loss of BVDV-free status (case herds, virus detection in at least one animal or when seroconversion was detected) or remaining BVDV-free (control herds). Descriptive statistics and logistic regression with a backward selection and elimination procedure were used to identify potential risk factors associated with losing BVDV-free status. Risk factors were quantified as an Odds Ratio (OR) with the associated 95% confidence interval (CI). The risk factor with the highest OR for losing BVDV-free status was purchasing cattle from herds without BVDV-free status (OR 1.25, CI 1.10-1.41), followed by the farmer having another profession that resulted in contact with other cattle (OR 1.25, CI 1.06-1.47), housing young calves and adult cows in the same barn (OR 1.22, CI 1.08-1.38), having a permanent employee on the farm (OR 1.17, CI 1.04-1.31), having a group calving pen (OR 1.16, CI 1.03-1.32), escaped cattle from other farms that mingled with own cattle (OR 1.16, CI 1.01-1.33), and nearest distance to a non-dairy farm (OR 1.15, CI 1.03-1.28). Although the BVDV status of most dairy herds can be checked in an open register, approximately half of the farmers indicated that they purchased cattle from BVDV-free herds while they were actually purchasing from non-BVDV-free farms. Farmers should be stimulated to actively check the true BVDV status of the herd from which cattle are purchased to further reduce the risk of introduction. In addition, indirect contact with cattle from other farms through either the farmer or other on-farm staff should be avoided. It is strongly advised to work in these situations with proper biosecurity measures such as changing boots and coveralls. The results can be used to improve BVDV control programs to further decrease the prevalence.

A Living Lab approach to understanding dairy farmers' technology and data needs to improve herd health: Focus groups from 6 European countries.

For successful development and adoption of technology on dairy farms, farmers need to be included in the innovation process. However, the design of agricultural technologies usually takes a top-down approach with little involvement of end-users at the early stages. Living Labs offer a methodology that involve end-users throughout the development process and emphasize the importance of understanding users' needs. Currently, exploration of dairy farmers' technology needs has been limited to specific types of technology (e.g., smartphone apps) and adult cattle. The aim of this study was to use a Living Lab approach to identify dairy farmers' data and technology needs to improve herd health and inform innovation development. We conducted 18 focus groups with a total of 80 dairy farmers from Belgium, Ireland, the Netherlands, Norway, Sweden, and the United Kingdom. Data were analyzed using Template Analysis, and 6 themes were generated representing the fundamental needs of autonomy, comfort, competence, community and relatedness, purpose, and security. Farmers favored technologies that provided them with convenience, facilitated their knowledge and understanding of problems on farm, and allowed them to be self-reliant. Issues with data sharing and accessibility and usability of software were barriers to technology use. Furthermore, farmers were facing problems around recruitment and management of labor and needed ways to reduce stress. Controlling aspects of the barn environment, such as air quality, hygiene, and stocking density, were particular concerns in relation to youngstock management. Overall, the findings suggest that developers of farm technologies may want to include farmers in the design process to ensure a positive user experience and improve accessibility. The needs identified in this study can be used as a framework when designing farm technologies to strengthen need satisfaction and reduce any potential harm toward needs.

Prevalence of extended-spectrum and AmpC ß-lactamase-producing Escherichia coli in young calves on Dutch dairy farms.

In young calves on dairy farms the animal prevalence of extended-spectrum and AmpC ß-lactamase-producing Escherichia coli (ESBL/AmpC-EC) is significantly higher compared with the animal prevalence in young stock and dairy cows. Hitherto it was unknown at what age antimicrobial resistant bacteria appear for the first time in the gut of calves on dairy farms, and how long these infections persist. The aim of this study was to examine the prevalence of ESBL/AmpC-EC, the number of excreted ESBL/AmpC-EC (in cfu/g of feces), as well as the ESBL/AmpC genotypes in young dairy calves (0-21 d of age) and the variation of these parameters between calves of different ages. Next to this, the course of shedding ESBL/AmpC-EC during the first year in dairy calves was studied. In a cross-sectional study, fecal samples from 748 calves, from 0 to 88 d of age, on 188 Dutch dairy farms were collected. The prevalence of calves testing positive for ESBL/AmpC-EC in a phenotypic assay was determined for different age categories (per 2 d of age). Positive samples were subjected to a semiquantitative test to determine the numbers of ESBL/AmpC-EC per gram of feces and for a selection of ESBL/AmpC-EC isolates the ESBL/AmpC genotype was determined. Ten of the 188 farms were selected for a longitudinal study based on the presence of at least 1 female calf with ESBL/Amp-EC in the cross-sectional study. These farms were additionally visited 3 times with a 4-mo interval. All calves that were sampled in the cross-sectional study were, if still present, resampled during the follow-up visits. Results show that from the day of birth ESBL/AmpC-EC can be present in the gut of calves. The phenotypic prevalence of ESBL/AmpC-EC was 33.3% in 0- to 21-d-old calves and 28.4% in 22- to 88-d-old calves. The prevalence of ESBL/AmpC-EC positive calves varied per age category among calves up to 21 d of age: significant increases and decreases at an early age were shown. Results of the longitudinal study show that after 4, 8, and 12 mo the prevalence of ESBL/AmpC-EC positive calves dropped to 3.8% (2/53), 5.8% (3/52), and 2.0% (1/49), respectively. This indicates that early gut colonization in young calves with ESBL/AmpC-EC is transient and does not lead to long-term shedding of these bacteria.

A framework for assessing confidence in freedom from infection in animal disease control programmes.

In the Surveillance Tool for Outcome-based Comparison of FREEdom from infection (STOC free) project (https://www.stocfree.eu), a data collection tool was constructed to facilitate standardised collection of input data, and a model was developed to allow a standardised and harmonised comparison of the outputs of different control programmes (CPs) for cattle diseases. The STOC free model can be used to evaluate the probability of freedom from infection for herds in CPs and to determine whether these CPs comply with the European Union's pre-defined output-based standards. Bovine viral diarrhoea virus (BVDV) was chosen as the case disease for this project because of the diversity in CPs in the six participating countries. Detailed BVDV CP and risk factor information was collected using the data collection tool. For inclusion of the data in the STOC free model, key aspects and default values were quantified. A Bayesian hidden Markov model was deemed appropriate, and a model was developed for BVDV CPs. The model was tested and validated using real BVDV CP data from partner countries, and corresponding computer code was made publicly available. The STOC free model focuses on herd-level data, although that animal-level data can be included after aggregation to herd level. The STOC free model is applicable to diseases that are endemic, given that it needs the presence of some infection to estimate parameters and enable convergence. In countries where infection-free status has been achieved, a scenario tree model could be a better suited tool. Further work is recommended to generalise the STOC free model to other diseases.

Dans le cadre du projet européen STOC free (Surveillance Tool for Outcome-based Comparison of FREEdom from infection, outil de surveillance permettant de comparer les probabilités d'absence d'infection sur la base des résultats, https://www.stocfree.eu), un outil de recueil des données a été construit pour faciliter une collecte normalisée des données d'entrée ; un modèle a également été élaboré pour permettre une comparaison normalisée et harmonisée des données sur les résultats des différents programmes de contrôle des maladies des bovins. Le modèle STOC free peut être utilisé pour évaluer la probabilité d'absence d'infection au sein des troupeaux dans le cadre des programmes de contrôle et déterminer si ces programmes sont conformes aux normes définies par l'Union européenne en termes de résultats attendus. L'infection par le virus de la diarrhée virale bovine a été choisie comme maladie d'étude pour ce projet en raison de la diversité des programmes de contrôle dans les six pays participants. Les informations relatives aux programmes de contrôle et aux facteurs de risque d'infection ont été recueillies à l'aide de l'outil de collecte des données. Les aspects clés et valeurs par défaut ont été quantifiés en vue d'être inclus dans le modèle STOC free. Un modèle de Markov caché dont les paramètres sont estimés par inférence bayésienne a été considéré comme le plus adapté et développé pour une application aux données issues des programmes de contrôle de la diarrhée virale bovine. Ce modèle a été testé et validé en utilisant des données réelles des programmes de contrôle du virus de la diarrhée virale bovine des pays participants ; le code informatique correspondant a été rendu public. Le modèle STOC free utilise des données au niveau des troupeaux, même si des données au niveau des animaux individuels peuvent être incluses une fois agrégées au niveau du troupeau. Le modèle STOC free s'applique aux maladies endémiques, puisqu'un certain niveau de présence de l'infection est nécessaire pour estimer les paramètres et permettre la convergence. Dans les pays ayant obtenu le statut indemne d'infection, un modèle du type arbre de scénario pourrait être un outil plus adapté. Des travaux supplémentaires sont recommandés pour généraliser le modèle STOC free à d'autres maladies.

Como parte del proyecto europeo STOC free (Surveillance Tool for Outcome-based Comparison of FREEdom from infection, herramienta de vigilancia para comparaciones por resultados respecto a la ausencia de infecciones, https://www.stocfree.eu), se confeccionó una herramienta de obtención de datos para facilitar la recogida normalizada de datos entrantes y se elaboró un modelo que posibilitara una comparación normalizada y armonizada de los resultados (datos salientes) de distintos programas de control de enfermedades bovinas. El modelo STOC free puede servir para calcular la probabilidad de ausencia de infección en los rebaños como parte de los programas de control y para determinar si estos programas se ajustan a las normas predefinidas de resultados de la Unión Europea. Como ejemplo de estudio para el proyecto se eligió el virus de la diarrea viral bovina (virus DVB) por la diversidad que presentaban los correspondientes programas de control de los seis países participantes. Empleando la herramienta de obtención de datos, se reunió información pormenorizada de los programas de control del virus DVB y los factores de riesgo. Para incluir los datos en el modelo STOC free, se cifraron unos aspectos clave y valores predeterminados Juzgando conveniente el empleo de un modelo oculto de Markov cuyos parámetros se estiman por inferencia bayesiana, se elaboró un modelo de esta índole aplicable a los programas de control del virus DVB. Para ensayar y validar el modelo se utilizaron datos reales de los programas de control del virus DVB de los países participantes, tras lo cual se hizo público el correspondiente código informático. El modelo STOC free trabaja con los datos por rebaño, aunque tras la agregación por rebaños pueden incluirse también datos por individuo. Para que este modelo sea aplicable a una enfermedad es preciso que esta sea endémica, pues el modelo requiere la presencia de cierto nivel de infección para calcular los parámetros y determinar convergencias. En aquellos países donde ya esté reconocida la ausencia de infección, sería más apropiado utilizar como herramienta un modelo de árbol de hipótesis. Los autores recomiendan ahondar en esta línea de trabajo para poder extender a otras enfermedades el uso del modelo STOC free.

Possible causes of aberrations in adverse grouping behavior of dairy cows: A field study.

In the Dutch national surveillance system, an increasing number of reports were received in the summer of 2017 from farmers about unusual behavior of their cows. The cows were grouping during the day in summer in one part of the barn and did not move for several hours, which, according to the farmers, led to reduced food and water intake and lying time and resulted in decreased milk production and increased risk of lameness. Many farmers perceived magnetic fields from, for instance, high-voltage lines, automated milking systems, or solar panels as possible causes for the behavior of their cows. Our aim for the study was to study potential factors such as magnetic fields and other factors such as barn climate and insect burden for adverse grouping behavior of dairy cows in the barn. For each case herd, 2 control herds were selected in the same postal area code. A case was a herd in which cattle grouped at least on 7 occasions in a month for several hours. In a control herd, the cows were in the barn during the same time period as in the matching case herd but did not show adverse grouping behavior. A questionnaire was administered by telephone in 31 case herds and 62 control herds. The questionnaire gathered information on behavior of the cows and potential risk factors. In addition, data on the distance of the herd to high-voltage lines was obtained. From a total of 74 variables, all variables with a P-value ≤0.10 were included in full multivariable logistic regression model. Backward selection was carried out at P ≤ 0.10. The grouping behavior of the cows started in most herds in June, was seen only during the day, and lasted mostly 6 to 8 h, with cows often grouped in the northern part of the barn. Identified risk factors appeared to be recently constructed barns, measured stray voltage in barns, and presence of fans in barns. Given the cross-sectional design of the case-control study, causality for these risk factors leading to adverse behavior of the cows could not be proven. Dissemination of the results to farmers hopefully results in measures that can prevent the unusual grouping behavior of cows.

Monitoring udder health on routinely collected census data: Evaluating the short- to mid-term consequences of implementing selective dry cow treatment.

In 2013, the preventive use of antimicrobials in Dutch livestock was prohibited, including a ban on the blanket application of antimicrobial dry cow treatment (BDCT). Since then, selective dry cow treatment (SDCT) has become the standard approach. In this study, we aimed to determine the effect of the ban on BDCT and the extent of the subsequent adoption of SDCT on antimicrobial usage (AMU) and udder health on Dutch dairy farms. In the Dutch cattle health surveillance system, AMU for dry cow treatment (AMUDCT), AMU for intramammary treatment at any point in time (AMUIMM), and udder health indicators are routinely and continuously monitored. This provided the opportunity to study associations among SDCT, udder health, and AMU on census data of approximately 17,000 dairy herds, with about 1.67 million cows in total (>2 yr old) at one moment in time in the period from 2013 until 2017. Six udder health parameters were evaluated using multivariable population-averaged generalized estimating equation models. The year in which the ban on BDCT was introduced (2013) was compared with the period thereafter (2014-2017). Additionally, AMUIMM and AMUDCT were included as independent variables to evaluate whether the extent to which SDCT was implemented on the herd level was associated with udder health. Demographic parameters were included as potential confounders. Since the ban on BDCT, overall declines of 63% in AMUDCT and 15% in AMUIMM were observed. The raw data show an improvement in 5 out of 6 evaluated udder health parameters between 2013 and 2017. Nevertheless, the multivariable model results showed that the period since the ban on BDCT was associated with a small but significant increase in the percentage of cows with high somatic cell count (HSCC) and new HSCC (+0.41% and +0.06%, respectively). Additionally, the probability of belonging to the group of herds with more than 25% of primiparous cows having HSCC during the start of lactation increased slightly, associated with the period after which BDCT was banned (odds ratio = 1.08). The probability of belonging to the group of herds with more than 25% cows having a persistent HSCC during the dry period was not affected and bulk milk somatic cell count showed a slight but significant reduction. The only udder health parameter that notably worsened during the study period was the probability of belonging to the group of herds with more than 25% of multiparous cows with a new HSCC after the dry period, during the start of lactation (odds ratio = 1.23). In herds where the farmer decided not to apply any dry cow therapy (≈20% of all herds), all udder health parameters were poorer compared with herds in which dry cow therapy was applied to some extent. The ban on BDCT and implementation of SDCT in the Netherlands was associated with a considerable reduction in AMU without a major impairment in udder health at the national level. Although negative effects of changed dry cow management were observed in some herds, we conclude that SDCT can be introduced without substantial negative effects on udder health.

Quantification of risk factors for bovine viral diarrhea virus in cattle herds: A systematic search and meta-analysis of observational studies.

Bovine viral diarrhea virus (BVDV) is endemic in many parts of the world, and multiple countries have implemented surveillance activities for disease control or eradication. In such control programs, the disease-free status can be compromised by factors that pose risks for introduction or persistence of the virus. The aim of the present study was to gain a comprehensive overview of possible risk factors for BVDV infection in cattle herds in Europe and to assess their importance. Papers that considered risk factors for BVDV infection in cattle were identified through a systematic search. Further selection of papers eligible for quantitative analysis was performed using a predefined checklist, including (1) appropriate region (i.e., studies performed in Europe), (2) representativeness of the study population, (3) quality of statistical analysis, and (4) availability of sufficient quantitative data. In total, 18 observational studies were selected. Data were analyzed by a random-effects meta-analysis to obtain pooled estimates of the odds of BVDV infection. Meta-analyses were performed on 6 risk factors: herd type, herd size, participation in shows or markets, introduction of cattle, grazing, and contact with other cattle herds on pasture. Significant higher odds were found for dairy herds (odds ratio, OR = 1.63, 95% confidence interval, CI: 1.06-2.50) compared with beef herds, for larger herds (OR = 1.04 for every 10 extra animals in the herd, 95% CI: 1.02-1.06), for herds that participate in shows or markets (OR = 1.45, 95% CI: 1.10-1.91), for herds that introduced cattle into the herd (OR = 1.41, 95% CI: 1.18-1.69), and for herds that share pasture or have direct contact with cattle of other herds at pasture (OR = 1.32, 95% CI: 1.07-1.63). These pooled values must be interpreted with care, as there was a high level of heterogeneity between studies. However, they do give an indication of the importance of the most frequently studied risk factors and can therefore assist in the development, evaluation, and optimization of BVDV control programs.

A description and qualitative comparison of the elements of heterogeneous bovine viral diarrhea control programs that influence confidence of freedom.

For endemic infections in cattle that are not regulated at the European Union level, such as bovine viral diarrhea virus (BVDV), European Member States have implemented control or eradication programs (CEP) tailored to their specific situations. Different methods are used to assign infection-free status in CEP; therefore, the confidence of freedom associated with the "free" status generated by different CEP are difficult to compare, creating problems for the safe trade of cattle between territories. Safe trade would be facilitated with an output-based framework that enables a transparent and standardized comparison of confidence of freedom for CEP across herds, regions, or countries. The current paper represents the first step toward development of such a framework by seeking to describe and qualitatively compare elements of CEP that contribute to confidence of freedom. For this work, BVDV was used as a case study. We qualitatively compared heterogeneous BVDV CEP in 6 European countries: Germany, France, Ireland, the Netherlands, Sweden, and Scotland. Information about BVDV CEP that were in place in 2017 and factors influencing the risk of introduction and transmission of BVDV (the context) were collected using an existing tool, with modifications to collect information about aspects of control and context. For the 6 participating countries, we ranked all individual elements of the CEP and their contexts that could influence the probability that cattle from a herd categorized as BVDV-free are truly free from infection. Many differences in the context and design of BVDV CEP were found. As examples, CEP were either mandatory or voluntary, resulting in variation in risks from neighboring herds, and risk factors such as cattle density and the number of imported cattle varied greatly between territories. Differences were also found in both testing protocols and definitions of freedom from disease. The observed heterogeneity in both the context and CEP design will create difficulties when comparing different CEP in terms of confidence of freedom from infection. These results highlight the need for a standardized practical methodology to objectively and quantitatively determine confidence of freedom resulting from different CEP around the world.

Quantifying calf mortality on dairy farms: Challenges and solutions.

In the Netherlands, the mortality rate of ear-tagged calves <1 yr is one of the indicators that is continuously monitored in census data and is defined as the number of deceased calves relative to the number of calf-days-at-risk. In 2017, yearly calf mortality rates were published in the lay press and resulted in discussions about the calculation of this parameter among stakeholders because the same parameter appeared to be calculated in many different ways by different organizations. These diverse definitions of calf mortality answered different aims such as early detection of deviations, monitoring trends, or providing insight into herd-specific results, but were difficult to understand by stakeholders. The aim of this study was to evaluate several definitions of calf mortality for scientific validity, usefulness for policymakers, and comprehensibility by farmers. Based on expert consultations, 10 definitions for calf mortality were evaluated that assessed different age categories, time periods, and denominators. Differences in definitions appeared to have a large effect on the magnitude of mortality. For example, with the original mortality parameter, the mortality rate was 16.5% per year. When the first year of life was subdivided into 3 age categories, the mortality rate was 3.3, 4.5, and 3.1% for postnatal calves (≤14 d), preweaned calves (15-55 d), and weaned calves (56 d-1 yr), respectively. Although it was logical that these mortality rates were lower than the original, the sum of the 3 separate mortality rates was also lower than the original mortality rate. The reason was that the number of calves present in a herd and the risk of mortality are not randomly distributed over a calf's first year of life and the conditional nature of mortality rates when calculated for different age categories. Ultimately, 4 parameters to monitor calf mortality in Dutch dairy herds were chosen based on scientific value, usefulness for monitoring of trends, and comprehensibility by farmers: perinatal calf mortality risk (i.e., mortality before, during, or shortly after the moment of birth up to the moment of ear-tagging), postnatal calf mortality risk (≤14 d), preweaned calf mortality rate (15-55 d), and weaned calf mortality rate (56 d-1 yr). Slight differences in definitions of parameters can have a major effect on results, and many factors have to be taken into account when defining an important health indicator such as mortality. Our evaluation resulted in a more thorough understanding of the definitions of the selected parameters and agreement by the stakeholders to use these key indicators to monitor calf mortality.

Development of an objective and uniform scoring method to evaluate the quality of rearing in Dutch dairy herds.

Young stock rearing is an essential part of dairy management, and it is important that the quality of rearing can be monitored and altered if necessary. In this study, a young stock rearing quality system (KalfOK) was developed with the aim to provide an objective and standardized means to evaluate and monitor the quality of young stock rearing in Dutch dairy herds. In the project, 201 dairy farmers participated. Twelve key indicators were defined that were related to calving and successful rearing, antimicrobial use, and herd health. For each of the key indicators, the value was calculated per herd and quarter of the year between January 2014 and April 2017. Benchmark values were determined to compare herd-specific results and for selection of threshold values. Each of the key indicators was graded when the value scored above the threshold. Combining the grades resulted in the herd-specific KalfOK score, which could vary between 0 and 100. Subsequently, 100 of the participating dairy herds were visited and the quality of young stock rearing was scored by a trained veterinarian. Using principal component analysis, the results of the herd health checks were combined into a factor score that represented the observed quality of young stock rearing during the visit. The amount of variance in observed quality of rearing during the herd health check that was explained by the key indicators in KalfOK was evaluated. Additionally, the validity of KalfOK to distinguish herds with an excellent or insufficient quality of young stock rearing was assessed by comparing the top and bottom 10% herds in the herd health check with the proportion of herds with a KalfOK score above or below a prespecified cutoff value. The results of the linear regression model showed that the key indicators included in KalfOK accounted for 56% of the variation in the score of the herd visits by a veterinarian. The moving average of the annual KalfOK score, which was the sum of the grades of all key indicators, was 77 points (25th percentile = 71, 75th percentile = 85 points). The combination of the sensitivity (88%, 95% confidence interval = 47-100%) and specificity (67%, 95% confidence interval = 54-78%) of KalfOK to correctly classify herds with an excellent quality of young stock rearing was highest when a cutoff value of 80 points was applied. Detection of dairy herds with an insufficient quality of young stock rearing was best at a cutoff value of 70 points (sensitivity 86%, 95% confidence interval = 42-100%; specificity 77%, 95% confidence interval = 66-86%). The KalfOK score that was based on routinely collected herd data provided an indication of the quality of young stock rearing in individual Dutch dairy farms. The KalfOK score illustrates how such data can be transferred into herd-specific information in support of animal health and welfare. Given the increasing availability of automatically assembled data, the development of similar monitoring tools seems a feasible option to enhance herd-specific management.

Factors associated with high antimicrobial use in young calves on Dutch dairy farms: A case-control study.

Since 2012, the Dutch Veterinary Medicine Authority reports antimicrobial usage (AMU) in young calves (<56 d) on dairy farms on an annual basis. The AMU distribution in this age group is skewed, with a low AMU in young calves on the majority of dairy farms and a high AMU in a relatively small number of farms. This results in a notable difference between the mean and median AMU. To further reduce the mean AMU, the AMU on the high-AMU farms must be decreased. The objective of this study was to evaluate the association between both young stock management and an indication of the farmers' mindset and AMU in young calves on Dutch dairy farms with a high and low AMU in young calves. This knowledge may be helpful in decreasing AMU in young calves on high-AMU farms. We performed a case-control study in which 200 dairy farms (100 with high AMU and 100 with low AMU in young calves) participated. Case farms were defined as farms with an animal daily-defined dose at the farm level in young calves >28 in 2012 and 2013, based on the 90th percentile of the use of antimicrobials in young calves in 2012. Control farms had an animal daily-defined dose at the farm level in young calves of <0.5 in 2012 and 2013, which was determined to be low use. A questionnaire was conducted about general farm and young stock management, hygiene, housing, vaccination, and calf health. An indication of the farmers' mindset with regard to AMU and treatment of sick calves was determined by including statements (agree/disagree) in the questionnaire. In addition, routinely collected data on herd size, growth in herd size, replacement, and calf mortality were available for analysis. Dairy farmers that immediately started antimicrobial treatment in sick calves had higher odds of being in the high-AMU group than farmers who started treatment of sick calves with supportive nonantimicrobial therapy. Other variables associated with a high AMU in young calves included housing calves on partially slatted floors, a high prevalence of respiratory disease, an unfavorable Salmonella status, and not agreeing with the statement "Young stock need specific management." Both dairy farm management and opinions of dairy producers regarding AMU, indicative of mindset, are important when distinguishing farms with high and low AMU in young calves. Although the rationale behind mindset warrants more research, likely a change in both aspects seems to be required to reduce the AMU in young calves on dairy farms.

Active animal health surveillance in European Union Member States: gaps and opportunities.

Animal health surveillance enables the detection and control of animal diseases including zoonoses. Under the EU-FP7 project RISKSUR, a survey was conducted in 11 EU Member States and Switzerland to describe active surveillance components in 2011 managed by the public or private sector and identify gaps and opportunities. Information was collected about hazard, target population, geographical focus, legal obligation, management, surveillance design, risk-based sampling, and multi-hazard surveillance. Two countries were excluded due to incompleteness of data. Most of the 664 components targeted cattle (26·7%), pigs (17·5%) or poultry (16·0%). The most common surveillance objectives were demonstrating freedom from disease (43·8%) and case detection (26·8%). Over half of components applied risk-based sampling (57·1%), but mainly focused on a single population stratum (targeted risk-based) rather than differentiating between risk levels of different strata (stratified risk-based). About a third of components were multi-hazard (37·3%). Both risk-based sampling and multi-hazard surveillance were used more frequently in privately funded components. The study identified several gaps (e.g. lack of systematic documentation, inconsistent application of terminology) and opportunities (e.g. stratified risk-based sampling). The greater flexibility provided by the new EU Animal Health Law means that systematic evaluation of surveillance alternatives will be required to optimize cost-effectiveness.

Prevalence and risk factors for extended-spectrum ß-lactamase or AmpC-producing Escherichia coli in organic dairy herds in the Netherlands.

Extended-spectrum ß-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.

Prevalence and risk factors for extended-spectrum ß-lactamase- and AmpC-producing Escherichia coli in dairy farms.

A cross-sectional study was conducted to evaluate the prevalence of extended-spectrum ß-lactamase (ESBL)- and plasmid-mediated AmpC-producing Escherichia coli and associated risk factors in dairy herds. One hundred dairy herds were randomly selected and sampled to study the presence of ESBL- and AmpC-producing E. coli in slurry samples. The sensitivity of testing slurry samples for ESBL/AmpC herd status is less than 100%, especially for detecting herds with a low ESBL/AmpC prevalence. Therefore, whereas herds that tested positive for ESBL/AmpC-producing E. coli in slurry were defined as positive herds, herds with negative slurry samples were defined as unsuspected. Isolates of ESBL- and AmpC-producing E. coli were further characterized by detection and typing of their ESBL/AmpC gene. At the initial sampling, a comprehensive questionnaire was conducted at the participating farms. The farmers were asked questions about management practices potentially associated with the ESBL/AMPC herd status. Also, data on antimicrobial purchases during 2011 were acquired to evaluate whether the animal-defined daily dose of antimicrobials per year at farm level was associated with the ESBL/AmpC herd status. Multivariable logistic regression models were used to determine the association between management practices and the ESBL/AmpC herd status. Six months after the initial slurry sampling, 10 positive herds and 10 herds that had an unsuspected ESBL/AmpC herd status during the first visit were resampled. At each farm, slurry samples and feces from 24 individual cows were collected to evaluate within herd dynamics. During the first sampling, ESBL/AmpC-producing E. coli were isolated from the slurry samples collected at 41% of the herds. In total, 37 isolates were further characterized, revealing 7 different ESBL genes (blaCTX-M-1, -2, -14, -15, -32, -55 and blaTEM-52), 1 plasmid-encoded AmpC gene (blaCMY-2), and 1 chromosomally encoded ampC gene (ampC type 3). The total animal-defined daily dose of antimicrobials per year at farm level was not significantly different between ESBL/AmpC-positive and unsuspected dairy herds. The use of third- and fourth-generation cephalosporins, however, was found to be associated with ESBL/AmpC status, with higher use of these antimicrobials resulting in a significant higher odds to be ESBL/AmpC-positive. Management factors that were associated with a higher odds of being ESBL/AmpC-positive were treatment of all cases of clinical mastitis with antimicrobials, a higher proportion of calves treated with antimicrobials, not applying teat sealants in all cows at dry off, and the use of a floor scraper. This last association, however, was considered a methodological effect rather than a true risk factor. On 5 of the 10 initially positive farms, no ESBL/AmpC-producing E. coli were cultured from the slurry or any of the individual cow samples collected during the second sampling. In 4 of the initially unsuspected farms, slurry or individual cow samples tested positive during the second sampling. In conclusion, ESBL/AmpC could frequently be cultured from slurry samples collected from Dutch dairy farms and the ESBL/AmpC genes carried by the isolates were consistent with those reported earlier. The use of third- and fourth-generation cephalosporins appeared to be associated the ESBL/AmpC herd status.

Improving the time efficiency of identifying dairy herds with poorer welfare in a population.

Animal-based welfare assessment is time consuming and expensive. A promising strategy for improving the efficiency of identifying dairy herds with poorer welfare is to first estimate levels of welfare in herds based on data that are more easily obtained. Our aims were to evaluate the potential of herd housing and management data for estimating the level of welfare in dairy herds, and to estimate the associated reduction in the number of farm visits required for identification of herds with poorer welfare in a population. Seven trained observers collected data on 6 animal-based welfare indicators in a selected sample of 181 loose-housed Dutch dairy herds (herd size: 22 to 211 cows). Severely lame cows, cows with lesions or swellings, cows with a dirty hindquarter, and very lean cows were counted, and avoidance distance was assessed for a sample of cows. Occurrence of displacements (social behavior) was recorded in the whole barn during 120 min of observation. For the same herds, data regarding cattle housing and management were collected on farms, and data relating to demography, management, milk production and composition, and fertility were extracted from national databases. A herd was classified as having poorer welfare when it belonged to the 25% worst-scoring herds. We used variables of herd housing and management data as potential predictors for individual animal-based welfare indicators in logistic regressions at the herd level. Prediction was less accurate for the avoidance distance index [area under the curve (AUC)=0.69], and moderately accurate for prevalence of severely lame cows (AUC=0.83), prevalence of cows with lesions or swellings (AUC=0.81), prevalence of cows with a dirty hindquarter (AUC=0.74), prevalence of very lean cows (AUC=0.83), and frequency of displacements (AUC=0.72). We compared the number of farm visits required for identifying herds with poorer welfare in a population for a risk-based screening with predictions based on herd housing and management data and a full screening of herds. Compared with a full screening, the number of farm visits required for identifying almost all herds with poorer welfare reduced by 5% (avoidance distance index) to 37% (prevalence of severely lame cows) when using risk-based screening. For identifying 70% of herds with poorer welfare, the number of farm visits reduced by 43% to 67%. The number of farm visits required for identifying dairy herds with poorer welfare can be reduced when herds are first screened using herd housing and management data.

Evaluation of udder health parameters and risk factors for clinical mastitis in Dutch dairy herds in the context of a restricted antimicrobial usage policy.

Recently, many changes have been implemented in Dutch dairy herds. Herd sizes have increased and antimicrobial use has been reduced. Certain types of antimicrobials can only be used in specific circumstances, and the preventive use of antimicrobials in dry cows is prohibited. The aim of this study was to quantify clinical mastitis (CM), subclinical mastitis (SCM), and risk factors associated with CM in Dutch dairy herds in 2013, in the context of these changes. For this study, 240 dairy herds were randomly selected from farms that participated in test-day milk recording, used a conventional milking system, and agreed to participate in the study. Eventually, 233 Dutch dairy farmers had complete records of CM in their herds in 2013 and 224 of these farmers completed a questionnaire on management factors potentially associated with CM. All participating farmers gave consent to use their routinely collected herd data such as test-day records and cow identification and registration data. Clinical and subclinical mastitis incidence rate (CMI and SCMI, respectively) per 100 cows per year, subclinical mastitis prevalence, and average bulk tank milk somatic cell count were obtained for 2013. The risk factor analysis was conducted using a generalized linear model with a log link function and a negative binomial distribution on herd level in Stata 13.1. A median CMI of 28.6 per 100 cows at risk per year, SCMI of 70.1 per 100 cows at risk per year, SCM prevalence of 15.8%, and bulk tank milk somatic cell count of 171 × 10(3) cells/mL were observed in 2013. Factors that were significantly associated with a higher CMI were cleaning slatted floors only once per day compared with more than 4 times a day (i.e., mechanical), a higher percentage of Holstein Friesian cows present in the herd, treating less than 50% of the cows with CM with antimicrobials, postmilking teat disinfection, and treatment of cows with elevated somatic cell count with antimicrobials. The results of this study indicated that udder health had not deteriorated compared with udder health in previous Dutch studies where herd sizes were somewhat smaller and before the restrictions in antimicrobial use. Several of the risk factors that were found can be influenced by the farmer and can prevent the occurrence of CMI. Still, when cases of CM occur, treatment with antimicrobials might be necessary to cure the CM case and is beneficial for the overall udder health in the herd.

Effect of different scenarios for selective dry-cow therapy on udder health, antimicrobial usage, and economics.

The goal of dry-cow therapy (DCT) is to reduce the prevalence of intramammary infections (IMI) by eliminating existing IMI at drying off and preventing new IMI from occurring during the dry period. Due to public health concerns, however, preventive use of antimicrobials has become questionable. In this study, we evaluated the effects of 8 scenarios for selecting animals for DCT, taking into account variation in parity and cow-level somatic cell count (SCC) at drying off. The aim of this study was to evaluate udder health, antimicrobial usage, and economics at the herd level when using different scenarios for selecting cows for DCT. To enable calculation and comparison of the effects of different scenarios to select cows for DCT in an "average" herd, we created an example herd, with a virtual herd size of 100 dairy cows to be calving during a year. Udder health, antimicrobial usage, and economics were evaluated during the dry period and the first 100 d in lactation, the period during which the greatest effect of DCT is expected. This leads to an estimated 13,551 cow-days at risk during a year in a 100-cow dairy herd. In addition to a blanket DCT (BDCT) scenario, we developed 7 scenarios to select cows for DCT based on SCC. The scenarios covered a range of possible approaches to select low-SCC cows for DCT, all based on cow-level SCC thresholds on the last milk recording before drying off. The incidence rate of clinical mastitis in the example herd varied from 11.6 to 14.5 cases of clinical mastitis per 10,000 cow-days at risk in the different scenarios, and the prevalence of subclinical mastitis varied from 38.8% in scenario 1 (BDCT) to 48.3% in scenario 8. Total antimicrobial usage for DCT and clinical mastitis treatment varied over the scenarios from 1.27 (scenario 8) to 3.15 animal daily dosages (BDCT), leading to a maximum reduction in antimicrobial usage of 60% for scenario 8 compared with BDCT. The total costs for each of the scenarios showed little variation, varying from €4,893 for scenario 5 to €5,383 for scenario 8. The effect of selective DCT compared with BDCT on udder health, antimicrobial usage, and economics is influenced by the SCC criteria used to select cows for DCT. Scenario 2 resulted in the lowest increases in clinical and subclinical mastitis compared with BDCT. The greatest reduction in antimicrobial usage was achieved under scenario 8. From an economic perspective, lowest costs were achieved with scenario 5. Drying off dairy cows with antimicrobials has an effect on udder health, antimicrobial usage, and economics.

Implementing wildlife disease surveillance in the Netherlands, a One Health approach.

The surveillance of (emerging) wildlife diseases can provide important, objective evidence of the circulation of pathogens of interest for veterinary and/or public health. The involvement of multiple research institutions in wildlife disease surveillance can ensure the best use of existing knowledge and expertise, but can also complicate or add challenges to the integration of wildlife disease surveillance components into a national programme. Documenting the existing efforts in a country's surveillance of wildlife diseases, including the institutes in which it takes place, provides a basis for policy-makers and authorities to identify gaps and priorities in their current surveillance programmes. This paper describes the wildlife disease surveillance activities taking place in the Netherlands. The authors recommend that, in addition to funding these current activities, surveillance resources should be allocated with the flexibility to allow for additional targeted surveillance, to detect and adequately respond to newly introduced or emerging pathogens. Similar structured overviews of wildlife disease surveillance in other countries would be very useful to facilitate international collaboration.

La surveillance exercée sur les maladies (émergentes) de la faune sauvage permet de réunir des données déterminantes, objectives et probantes sur la présence d'agents pathogènes importants pour la santé animale et/ou publique. La participation de plusieurs instituts de recherche dans les activités de surveillance des maladies de la faune sauvage permet de tirer le meilleur parti des connaissances et de l'expertise disponibles mais, dans certains cas, elle peut aussi se traduire par une complexité ou des difficultés supplémentaires qui compromettent l'intégration des composantes axées sur les maladies de la faune sauvage dans les programmes nationaux de surveillance. La collecte d'informations sur les efforts déployés au niveau national pour surveiller les maladies des animaux sauvages ainsi que sur les institutions chargées de cette surveillance constitue une première étape essentielle pour que les responsables des politiques sanitaires et les autorités puissent identifier les lacunes et les priorités des programmes de surveillance en vigueur. Les auteurs décrivent les activités de surveillance des maladies de la faune sauvage conduites actuellement aux Pays-Bas. Ils recommandent que parallèlement au financement des activités en cours, les ressources destinées à la surveillance soient allouées de manière plus souple afin de couvrir de nouvelles activités ciblées, de détecter les agents pathogènes émergents ou d'introduction récente et de préparer une réponse adéquate. Ils préconisent de réaliser dans d'autres pays des études structurées similaires sur la surveillance des maladies de la faune sauvage afin de faciliter la collaboration internationale.

La vigilancia de enfermedades (emergentes) de la fauna silvestre puede proporcionar importantes elementos de prueba objetivos sobre la circulación de patógenos de interés para la salud pública y/o veterinaria. La participación de numerosos establecimientos de investigación en estas actividades de vigilancia puede garantizar que se haga un uso idóneo de los conocimientos teóricos y técnicos existentes, pero a veces también complica o dificulta la integración en un programa nacional de las tareas de vigilancia de las enfermedades de la fauna silvestre. El hecho de repertoriar las actividades en la materia que se llevan a cabo en un país, incluidos los establecimientos donde tienen lugar, sienta las bases para que las autoridades e instancias de planificación de políticas puedan determinar las carencias y prioridades de los programas de vigilancia que ya tengan en marcha. Tras describir las actividades de vigilancia sanitaria de la fauna silvestre que se llevan a cabo en los Países Bajos, los autores recomiendan que los recursos para fines de vigilancia se asignen de manera flexible para que, además de costear las actividades ya en curso, sirvan para financiar otras labores de vigilancia selectiva que permitan detectar patógenos emergentes o recién introducidos en el país y responder debidamente a ellos. Para facilitar la colaboración internacional sería muy útil contar con estudios estructurados similares, que ofrezcan una visión de conjunto de la vigilancia sanitaria de la fauna silvestre en otros países.

Evaluation of the epidemiological and economic consequences of control scenarios for bovine viral diarrhea virus in dairy herds.

Bovine viral diarrhea virus (BVDV) is an important endemic infection. However, no information was available on whether it would be economically beneficial to implement a national control program in the Netherlands. Therefore, a stochastic simulation model was developed in which control scenarios were added to compare the epidemiological and economic consequences of BVDV control in Dutch dairy herds in the next 10 yr. In the epidemiological part of the model, herds could be classified as susceptible, infectious, recovered, or vaccinated. The outputs of the epidemiological module served as input for the economic module. Net costs that could be attributed to bovine viral diarrhea consisted of production losses, costs for testing, and culling persistently infected cattle in the present voluntary Dutch BVDV control program and costs for vaccination. Four different control scenarios were simulated, involving testing and culling of persistently infected (based on serum or ear-notch testing), and monitoring BVDV statuses and vaccination and were derived from BVDV control programs that are currently executed in Europe. The costs and benefits of BVDV control in the current situation and in each of the simulated control scenarios were evaluated assuming an annual discount rate of 2%. The model estimated a mean BVDV herd prevalence of 18.0% in 2014 and showed a slightly decreasing prevalence over time. The outputs seemed realistic for the present situation in the Netherlands when compared with actual survey data. The average annual net costs associated with bovine viral diarrhea were estimated at €27.8 million for the dairy industry. Two control scenarios were beneficial in controlling BVDV during the study period (between 2015 and 2025). In the scenario where tracing and removing of PI animals and monitoring of the subsequent status was obligatory, the benefit to cost (B/C) ratio was 1.5 (€1.5 benefit for each invested euro). In the scenario in which the BVDV status of all herds was determined, followed by voluntary measures before control measures became obligatory, the B/C ratio was 1.1. The B/C ratio of the scenarios included could be even higher when it was assumed that nondairy herds participated in the control program as well. The model provided the opportunity to compare the effect of voluntary and mandatory control scenarios on the BVDV prevalence and costs and benefits relative to the current situation in the Netherlands. The model was used to support policy makers in their decisions about a BVDV control program.

An estimation of the clinical mastitis incidence per 100 cows per year based on routinely collected herd data.

The aim of this study was to evaluate whether it was possible to (1) estimate the clinical mastitis incidence rate (CMI) for all Dutch dairy herds and (2) to detect farms with a high CMI based on routinely collected herd data. For this study, 240 dairy farms with a conventional milking system that participated in the milk recording program every 4 to 6 wk were randomly selected and agreed to participate. From the initial 240 herds, data of clinical mastitis (CM) registrations and routinely collected herd data of 227 herds were complete and could be used for analysis. Routinely collected herd data consisted of identification and registration records, antimicrobial usage, test-day records from the milk recording program, bulk tank milk (BTM) somatic cell count data and results of diagnostic tests on BTM samples. For each of the 227 herds, the CMI per 100 cows per year was calculated per quarter of the year and was combined with the available herd data. Two models were developed to predict the CMI for all dairy herds and to detect individual herds that belonged to the 25% herds with the highest CMI. Records of 156 (67%) herds were used for development of the models and the remaining 71 (33%) were used for validation. The model that estimated the CMI in all herds consisted of 11 explanatory variables. The observed and predicted averages of the validation herds were not significantly different. The model estimated a CMI per 100 cows per year of 32.5 cases (95% confidence interval=30.2-34.8), whereas the farmers registered 33.4 cases (95% confidence interval=29.5-37.4). The model that aimed at detecting individual herds with a high CMI contained 6 explanatory variables and could correctly classify 77% of all validation herds at the quarter-year level. The most important variables in the model were antibiotic usage for treating CM and BTM somatic cell count. In conclusion, models based on routinely collected herd data gave an accurate prediction of CMI for all Dutch dairy herds and could detect individual dairy herds with a high CMI. With these models it is possible to periodically monitor CMI both at the herd and at the national level, which is valuable for monitoring purposes and can motivate farmers to continuously improve udder health in their herds.