A quantitative risk assessment for human Taenia solium exposure from home slaughtered pigs in European countries

Background: Taenia solium, a zoonotic tapeworm, is responsible for about a third of all preventable epilepsy human cases in endemic regions. In Europe, adequate biosecurity of pig housing and meat inspection practices have decreased the incidence of T. solium taeniosis and cysticercosis. Pigs slaughtered at home may have been raised in suboptimal biosecurity conditions and slaughtered without meat inspection. As a result, consumption of undercooked pork from home slaughtered pigs could pose a risk for exposure to T. solium. The aim of this study was to quantify the risk of human T. solium exposure from meat of home slaughtered pigs, in comparison to controlled slaughtered pigs, in European countries. A quantitative microbial risk assessment model (QMRA) was developed and porcine cysticercosis prevalence data, the percentage of home slaughtered pigs, meat inspection sensitivity, the cyst distribution in pork and pork consumption in five European countries, Bulgaria, Germany, Poland, Romania and Spain, were included as variables in the model. This was combined with literature about cooking habits to estimate the number of infected pork portions eaten per year in a country. Results: The results of the model showed a 13.83 times higher prevalence of contaminated pork portions from home slaughtered pigs than controlled slaughtered pigs. This difference is brought about by the higher prevalence of cysticercosis in pigs that are home raised and slaughtered. Meat inspection did not affect the higher exposure from pork that is home slaughtered. Cooking meat effectively lowered the risk of exposure to T. solium-infected pork. Conclusions: This QMRA showed that there is still a risk of obtaining an infection with T. solium due to consumption of pork, especially when pigs are reared and slaughtered at home, using data of five European countries that reported porcine cysticercosis cases. We propose systematic reporting of cysticercosis cases in slaughterhouses, and in addition molecularly confirming suspected cases to gain more insight into the presence of T. solium in pigs and the risk for humans in Europe. When more data become available, this QMRA model could be used to evaluate human exposure to T. solium in Europe and beyond

Standardizing output-based surveillance to control non-regulated cattle diseases:Aspiring for a single general regulatory framework in the European Union

Several European countries have implemented country specific programmes to control cattle diseases with little or no regulation in the European Union (EU). These control programmes vary between member states, impairing a confident comparison of freedom from disease when cattle originate from different countries. In order to facilitate safe trade, there is a need to support the development of transparent methods that enable comparison of outputs of surveillance, control or eradication programmes. The aim of the COST Action (CA 17110), Standardizing OUtput-based surveillance to control Non-regulated Diseases in the EU (SOUND control), is the development of a generic and joint understanding of the requirements and characteristics needed for a flexible output-based framework. This framework should be able to substantiate the confidence of disease freedom and cost-effectiveness of heterogeneous surveillance, control or eradication programmes for cattle diseases in the EU. This project supports other initiatives in the development of an output-based framework which will subsequently facilitate safe trade and support the improvement of disease control measures, which is of great importance as the cattle sector contributes to one third of the total gross production value of EU agriculture

A modelling framework for the prediction of the herd-level probability of infection from longitudinal data

International audienceThe collective control programmes (CPs) that exist for many infectious diseases of farm animals rely on the application of diagnostic testing at regular time intervals for the identification of infected animals or herds. The diversity of these CPs complicates the trade of animals between regions or countries because the definition of freedom from infection differs from one CP to another. In this paper, we describe a statistical model for the prediction of herd-level probabilities of infection from longitudinal data collected as part of CPs against infectious diseases of cattle. The model was applied to data collected as part of a CP against bovine viral diarrhoea virus (BVDV) infection in Loire-Atlantique, France. The model represents infection as a herd latent status with a monthly dynamics. This latent status determines test results through test sensitivity and test specificity. The probability of becoming status positive between consecutive months is modelled as a function of risk factors (when available) using logistic regression. Modelling is performed in a Bayesian framework, using either Stan or JAGS. Prior distributions need to be provided for the sensitivities and specificities of the different tests used, for the probability of remaining status positive between months as well as for the probability of becoming positive between months. When risk factors are available, prior distributions need to be provided for the coefficients of the logistic regression, replacing the prior for the probability of becoming positive. From these prior distributions and from the longitudinal data, the model returns posterior probability distributions for being status positive for all herds on the current month. Data from the previous months are used for parameter estimation. The impact of using different prior distributions and model implementations on parameter estimation was evaluated. The main advantage of this model is its ability to predict a probability of being status positive in a month from inputs that can vary in terms of nature of test, frequency of testing and risk factor availability/presence. The main challenge in applying the model to the BVDV CP data was in identifying prior distributions, especially for test characteristics, that corresponded to the latent status of interest, i.e. herds with at least one persistently infected (PI) animal. The model is available on Github as an R package (https://github.com/AurMad/STOCfree) and can be used to carry out output-based evaluation of disease CPs

Existence and Quality of Data on Control Programs for EU Non-regulated Cattle Diseases: Consequences for Estimation and Comparison of the Probability of Freedom From Infection

Some European countries have successfully implemented country-specific control programs (CPs) for infectious cattle diseases that are not regulated or are regulated only to a limited extent at the European Union (EU) level. Examples of such diseases include bovine viral diarrhea (BVD), infectious bovine rhinotracheitis (IBR), and Johne's disease (JD). The CPs vary between countries in the design and quality of collected data as well as methods used to detect infection and estimate prevalence or probability of freedom from infection. Differences in disease status between countries and non-standardized approaches to assess freedom from infection pose a risk for countries with CPs for non-regulated diseases as infected animals may influence the progress of the disease control or eradication program. The implementation of output-based standards allows estimation and comparison of the probability of freedom for non-regulated cattle diseases in European countries. The aim of the current study was to assess the existence and quality of data that could be used for estimating freedom from infection in European countries. The online data collection tool was sent to 32 countries participating in the SOUND control COST Action and was completed by 24 countries. Data on cattle demographics and data from CPs of IBR and BVD exist in more than 50% of the response countries. However, data describing risk factors and CP of JD was reported as existing in < 25% of the countries. The overall quality of data in the sections on demographics and CPs of IBR and BVD were evaluated as "good ", but risk factors and JD data were mostly evaluated as "fair. " Data quality was considered less good mainly due to two quality criteria: accessibility and accuracy. The results of this study show that the quantity and quality of data about cattle populations and CPs are relatively similar in many surveyed countries. The outcome of this work provides an overview of the current situation in the European countries regarding data on EU non-regulated cattle diseases and will further assist in the development and implementation of output-based standards

Estimating freedom from infection: Output-based evaluation of BVDV control programmes

There has been a slight shift in animal disease surveillance from input-based standards towards output-based standards, meaning that it is not prescribed what needs to be done, but rather what must be achieved. An objective and standardized assessment of the outputs of differently designed control programmes (CPs) is needed and therefore the aim of this thesis was to develop and test a generic output-based framework to determine the probability of freedom from Bovine Viral Diarrhea virus (BVDV) infection in cattle herds. The in this project developed STOC free framework (Surveillance analysis Tool for Outcome-based Comparison of the confidence of FREEdom), consists of a data collection tool (STOC free DATA) and a model to estimate herd-level freedom from infection (STOC free MODEL). Elements of BVDV CPs in six European countries that contribute to confidence of freedom from BVDV were described and qualitatively compared. Many differences in the context and design of BVDV CPs were found. CPs were either mandatory or voluntary, resulting in variation in risks from (in)direct animal contacts. Risk factors such as cattle density and the number of imported cattle varied greatly between countries. Differences were also found in testing protocols and definitions of freedom from infection. A systematic search and meta-analysis of risk factors for the presence of BVDV in cattle herds in Europe was performed to obtain generic estimates that could be used as default input data for STOC free MODEL. Data from 18 observational studies were analyzed by a random effects meta-analysis and significant higher odds were found for dairy herds compared to beef herds, for larger herds, for herds that participated in shows or markets, for herds that introduced cattle and for herds that had contact with cattle of other herds at pasture. STOC free DATA was developed to evaluate data availability and quality and to collect actual input data required for STOC free MODEL. Initially, the tool was developed for assessment of freedom from BVDV in six Western European countries and was then further generalized to enable inclusion of data for other cattle diseases and for use throughout Europe. STOC free DATA includes a wide range of variables that could reasonably influence confidence of freedom from infection, including those relating to cattle demographics, risk factors for introduction and characteristics of CPs. STOC free MODEL, a Bayesian Hidden Markov model, was applied to BVDV field data from CPs based on ear notch samples from newborn calves in four study regions to estimate the probability of herd level freedom from BVDV. The probability of freedom from BVDV for dairy herds that are free according to each study region’s CP was predicted to be very high ranging from 0.98 to 1.00, regardless of the use of default or country-specific priors. The STOC free model can be used to evaluate and improve BVDV CPs and to determine whether they comply with output-based regulations of the EU. The tool is currently evaluated by other research groups for application to other infectious cattle diseases such as Johne’s disease and salmonella

Estimating freedom from infection: Output-based evaluation of BVDV control programmes

There has been a slight shift in animal disease surveillance from input-based standards towards output-based standards, meaning that it is not prescribed what needs to be done, but rather what must be achieved. An objective and standardized assessment of the outputs of differently designed control programmes (CPs) is needed and therefore the aim of this thesis was to develop and test a generic output-based framework to determine the probability of freedom from Bovine Viral Diarrhea virus (BVDV) infection in cattle herds. The in this project developed STOC free framework (Surveillance analysis Tool for Outcome-based Comparison of the confidence of FREEdom), consists of a data collection tool (STOC free DATA) and a model to estimate herd-level freedom from infection (STOC free MODEL). Elements of BVDV CPs in six European countries that contribute to confidence of freedom from BVDV were described and qualitatively compared. Many differences in the context and design of BVDV CPs were found. CPs were either mandatory or voluntary, resulting in variation in risks from (in)direct animal contacts. Risk factors such as cattle density and the number of imported cattle varied greatly between countries. Differences were also found in testing protocols and definitions of freedom from infection. A systematic search and meta-analysis of risk factors for the presence of BVDV in cattle herds in Europe was performed to obtain generic estimates that could be used as default input data for STOC free MODEL. Data from 18 observational studies were analyzed by a random effects meta-analysis and significant higher odds were found for dairy herds compared to beef herds, for larger herds, for herds that participated in shows or markets, for herds that introduced cattle and for herds that had contact with cattle of other herds at pasture. STOC free DATA was developed to evaluate data availability and quality and to collect actual input data required for STOC free MODEL. Initially, the tool was developed for assessment of freedom from BVDV in six Western European countries and was then further generalized to enable inclusion of data for other cattle diseases and for use throughout Europe. STOC free DATA includes a wide range of variables that could reasonably influence confidence of freedom from infection, including those relating to cattle demographics, risk factors for introduction and characteristics of CPs. STOC free MODEL, a Bayesian Hidden Markov model, was applied to BVDV field data from CPs based on ear notch samples from newborn calves in four study regions to estimate the probability of herd level freedom from BVDV. The probability of freedom from BVDV for dairy herds that are free according to each study region’s CP was predicted to be very high ranging from 0.98 to 1.00, regardless of the use of default or country-specific priors. The STOC free model can be used to evaluate and improve BVDV CPs and to determine whether they comply with output-based regulations of the EU. The tool is currently evaluated by other research groups for application to other infectious cattle diseases such as Johne’s disease and salmonella

Live exotic animals legally and illegally imported via the main Dutch airport and considerations for public health.

The trade in live animals and animal products is considered one of the major drivers of zoonotic disease emergence. Schiphol airport in the Netherlands is one of the largest European airports and is considered a main hub for legal and illegal import of exotic animals. However, so far there is little information about what pathogens these imported animals might carry with them. Therefore, this study aimed to assess the zoonotic risks of exotic animals imported into the Netherlands through Schiphol airport in 2013 and 2014. Based on a previous list of highly prioritised emerging zoonoses for the Netherlands (EmZoo list), WAHID and Promed databases, literature and expert opinions, a list of 143 potentially relevant zoonotic pathogens was compiled. In a step-wise selection process eighteen pathogen-host combinations that may pose a public health risk by the import of exotic animals via Schiphol airport were identified and these were assessed by expert elicitation. The five pathogens with the highest combined scores were Salmonella spp., Crimean-Congo haemorrhagic fever virus, West Nile virus, Yersinia pestis and arenaviruses, but overall, the public health risk of the introduction of these exotic pathogens into the Netherlands via the legal import of exotic animals was considered low. However, the vast majority of imported exotic animals were imported by trade companies, increasing the risk for specific groups such as retail and hobbyists/pet owners. It is expected that the risk of introduction of exotic zoonotic pathogens via illegal import is substantial due to the unknown health status. Due to changing trade patterns combined with changing epidemiological situation in the world and changing epidemiological features of pathogens, this risk assessment needs regular updating. The results could give directions for further adjusting of health requirements and risk based additional testing of imported exotic animals

Un dispositif d’évaluation du niveau de confiance dans l’absence d’infection dans le cadre de programmes de contrôle des maladies animales

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.European Food Safety Authority (EFSA

Comparison of the confidence in freedom from infection based on different control programmes between EU member states: STOC free

The STOC free project constructed a generic framework that allows a standardised and harmonised description of different control programmes (CP) for cattle diseases. The STOC free model can be used to determine the confidence of freedom from infection that has been achieved in disease CPs, in support of an ongoing assessment of progress towards output-based standards as outlined in the EU Animal Health Law. With this information, and as required, further CP actions can be taken to mitigate the risks of persistence and (re-)introduction on the probability of freedom from infection. Bovine viral diarrhoea virus (BVDV) was chosen as the case disease because of the diversity in CPs in the six participating countries. A Bayesian hidden Markov model was considered the best modelling method. Detailed BVDV CP information was collected in the participating countries and the key aspects for inclusion in the STOC free model were identified. A first version of STOC free model was developed and tested on simulated data. The risk factors for BVDV infection that needed to be included in the model were defined and default values for these risk factors were quantified. A data collection tool was finalised with which the data for the STOC free model was collected. Subsequently, the developed model was tested and validated using real BVDV CP data from partner countries. Based on the feedback, the model was finalised and the report and corresponding computer code were made publicly available. There were roughly three different BVDV situations that occurred in the partner countries: 1. Endemic situation with a CP operating at herd level, 2. Endemic situation with a CP operating at animal level and 3. BVD free situation. The STOC free model is able to include herd level data only and animal level data has to be aggregated to herd level before the model can be applied. The STOC free model is not applicable for a country that is completely BVDV free given that it needs some infections to estimate its parameters and converge. In the latter situation, a scenario tree model could be a better suited tool, and this was evaluated in the Swedish case study. Further work is needed for generalisation of the method to other diseases and expansion of the method to include socioeconomic aspects of CPs.European Food Safety Authority (EFSA