Revue Scientifique et Technique-Office International Des Epizooties最新文献

There is increased pressure by governments and industry to develop national surveillance programmes to evaluate antimicrobial usage (AMU) in animals. This article presents a methodological approach to cost-effectiveness analysis of such programmes. Seven objectives are proposed for AMU surveillance in animals: quantifying use, finding trends, detecting hotspots, identifying risk factors, encouraging research, evaluating the impact of policies and diseases, and demonstrating compliance with regulations. Achieving these objectives would assist in making decisions about potential interventions, help to generate trust, incentivise the reduction of AMU and decrease the risk of antimicrobial resistance. The cost-effectiveness of each objective can be found by dividing the cost of the programme by the performance indicators of the surveillance required to meet the objective concerned. The precision and accuracy of surveillance outputs are suggested here as useful performance indicators. Precision depends on the level of surveillance coverage (SC) and surveillance representativeness (SR). Accuracy is influenced by the quality of farm records and SR. The authors argue that there is an increase in marginal cost for each unit increase of SC, SR and data quality. This is caused by the increasing difficulty of recruiting farmers due to potential barriers such as staff capacity, capital availability, computing literacy and availability, and geographical differences, among other factors. A simulation model was conducted to test the approach, using the quantification of AMU as the primary objective, and to provide evidence of the application of the law of diminishing returns. Cost-effectiveness analysis can be used to support decisions on the level of coverage, representativeness and data quality required in such AMU programmes.

This article reviews the Observatory of the World Organisation for Animal Health (WOAH, founded as OIE), including its objectives, direction and progress achieved so far. It explains the benefits offered by this data-driven programme in improving access to data and information analysis while ensuring confidentiality. In addition, the authors examine the challenges that the Observatory faces and its inextricable link to the Organisation's data management. The development of the Observatory is of the utmost importance, not only for its relevance to the development of WOAH International Standards and their implementation worldwide, but also because of its role as one of the drivers of WOAH's digital transformation plan. This transformation is essential, given the major role of information technologies in supporting regulation for animal health, animal welfare and veterinary public health.

The need to control transboundary animal disease outbreaks is widely recognised, as is the need for evidence-based decisions regarding which control measures to implement. Key data and information are required to inform this evidence base. To ensure effective communication of the evidence, a rapid process of collation, interpretation and translation is required. This paper describes how epidemiology can provide the framework through which relevant specialists can be engaged to this end, and highlights the central role of epidemiologists, with their unique combination of skills, in this process. It provides an example of an evidence team led by epidemiologists, namely the United Kingdom National Emergency Epidemiology Group, which was established to address this need. It then goes on to consider the different strands of epidemiology, the need for a wide multidisciplinary approach, and the importance of training and preparedness activities to facilitate rapid response.

With modelling becoming increasingly important in helping to inform decisions about animal diseases, it is essential that the process be optimised to gain the maximum benefit for the decision-maker. Here, the authors set out ten steps that can improve this process for all concerned. Four steps describe initialisation to ensure that the question, answer and timescale are defined; two steps describe the modelling process and quality assurance; and four steps describe the reporting stage. The authors believe that this greater emphasis at the beginning and end of a modelling project will increase the relevance of the work and understanding of the results, and thus contribute towards better decision-making.

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.

The sharing of animal disease data should be encouraged. The analysis of such data will broaden our knowledge of animal diseases and potentially provide insights into their management. However, the need to conform to data protection rules in the sharing of such data for analysis purposes often poses practical difficulties. This paper sets out the challenges and the methods used for the sharing of animal health data in England, Scotland and Wales - Great Britain - using bovine tuberculosis (bTB) data as a case study. The data sharing described is undertaken by the Animal and Plant Health Agency on behalf of the Department for Environment, Food and Rural Affairs and the Welsh and Scottish Governments. It should be noted that animal health data are held at the level of Great Britain (rather than the United Kingdom - which includes Northern Ireland), as Northern Ireland's Department of Agriculture, Environment and Rural Affairs has its own separate data systems. Bovine tuberculosis is the most significant and costly animal health problem facing cattle farmers in England and Wales. It can be devastating for farmers and farming communities and the control costs for taxpayers in Great Britain are over £150 million a year. The authors describe two methods of data sharing - first, where data are requested by, and delivered to, an academic institution for epidemiological or scientific analysis, and second, where data are proactively published in an accessible and meaningful way. They provide details of an example of the second method, namely, the free-to-access website ‘information bovine TB' (https://ibtb.co.uk), which publishes bTB data for the benefit of the farming community and veterinary health professionals.

Business-centric solutions to data-related problems often yield the greatest positive impacts and improvements for private enterprises but are challenging to design and implement at scale within government agencies. The core mission of the Veterinary Services of the United States Department of Agriculture (USDA) Animal Plant Health Inspection Service is to safeguard animal agriculture in the United States of America, and effective data management underpins these efforts. As this agency works to assist data-driven decision-making in animal health management, it continues to use a blend of best practices from Federal Data Strategy initiatives and the International Data Management Association framework. This paper describes three case studies that focus on strategies to improve animal health data collection, integration, reporting and governance for animal health authorities. These strategies have enhanced the way USDA's Veterinary Services execute their mission and core operational activities for prevention, detection and early response to support disease containment and control.

Environmental exposures can have large impacts on health outcomes. While many resources have been dedicated to understanding how humans are influenced by the environment, few efforts have been made to study the role of built and natural environmental features on animal health. The Dog Aging Project (DAP) is a longitudinal community science study of aging in companion dogs. Using a combination of owner-reported surveys and secondary sources linked through geocoded coordinates, DAP has captured home, yard and neighbourhood variables for over 40,000 dogs. The DAP environmental data set spans four domains: the physical and built environment; chemical environment and exposures; diet and exercise; and social environment and interactions. By combining biometric data, measures of cognitive function and behaviour, and medical records, DAP is attempting to use a big-data approach to transform the understanding of how the surrounding world affects the health of companion dogs. In this paper, the authors describe the data infrastructure developed to integrate and analyse multi-level environmental data that can be used to improve the understanding of canine co-morbidity and aging.