Implementation of the One Health approach to fight arbovirus infections in the Mediterranean and Black Sea Region: Assessing integrated surveillance in Serbia, Tunisia and Georgia

Background In the Mediterranean and Black Sea Region, arbovirus infections are emerging infectious diseases. Their surveillance can benefit from one health inter-sectoral collaboration; however, no standardized methodology exists to study One Health surveillance. Methods We designed a situation analysis study to document how integration of laboratory/clinical human, animal and entomological surveillance of arboviruses was being implemented in the Region. We applied a framework designed to assess three levels of integration: policy/institutional, data collection/data analysis and dissemination. We tested the use of Business Process Modelling Notation (BPMN) to graphically present evidence of inter-sectoral integration. Results Serbia, Tunisia and Georgia participated in the study. West Nile Virus surveillance was analysed in Serbia and Tunisia, Crimea-Congo Haemorrhagic Fever surveillance in Georgia. Our framework enabled a standardized analysis of One Health surveillance integration, and BPMN was easily understandable and conducive to detailed discussions among different actors/institutions. In all countries, we observed integration across sectors and levels except in data collection and data analysis. Data collection was interoperable only in Georgia without integrated analysis. In all countries, surveillance was mainly oriented towards outbreak response, triggered by an index human case. Discussion The three surveillance systems we observed prove that integrated surveillance can be operationalized with a diverse spectrum of options. However, in all countries, the integrated use of data for early warning and inter-sectoral priority setting is pioneeristic. We also noted that early warning before human case occurrence is recurrently not operationally prioritized

Veterinary syndromic surveillance : current initiatives and potential for development

This paper reviews recent progress in the development of syndromic surveillance systems for veterinary medicine. Peer-reviewed and grey literature were searched in order to identify surveillance systems that explicitly address outbreak detection based on systematic monitoring of animal population data, in any phase of implementation. The review found that developments in veterinary syndromic surveillance are focused not only on animal health, but also on the use of animals as sentinels for public health, representing a further step towards One Medicine. The main sources of information are clinical data from practitioners and laboratory data, but a number of other sources are being explored. Due to limitations inherent in the way data on animal health is collected, the development of veterinary syndromic surveillance initially focused on animal health data collection strategies, analyzing historical data for their potential to support systematic monitoring, or solving problems of data classification and integration. Systems based on passive notification or data transfers are now dealing with sustainability issues. Given the ongoing barriers in availability of data, diagnostic laboratories appear to provide the most readily available data sources for syndromic surveillance in animal health. As the bottlenecks around data source availability are overcome, the next challenge is consolidating data standards for data classification, promoting the integration of different animal health surveillance systems, and also the integration to public health surveillance. Moreover, the outputs of systems for systematic monitoring of animal health data must be directly connected to real-time decision support systems which are increasingly being used for disease management and control

A One Health approach to antimicrobial resistance surveillance: is there a business case for it?

Antimicrobial resistance is a global problem of complex epidemiology, suited to a broad, integrated One Health approach. Resistant organisms exist in humans, animals, food and the environment, and the main driver of this resistance is antimicrobial usage. A One Health conceptual framework for surveillance is presented to include all of these aspects. Global and European (regional and national) surveillance systems are described, highlighting shortcomings compared with the framework. Policy decisions rely on economic and scientific evidence, so the business case for a fully integrated system is presented. The costs of integrated surveillance are offset by the costs of unchecked resistance and the benefits arising from interventions and outcomes. Current estimates focus on costs and benefits of human health outcomes. A One Health assessment includes wider societal costs of lost labour, changes in health-seeking behaviour, impacts on animal health and welfare, higher costs of animal-origin food production, and reduced consumer confidence in safety and international trade of such food. Benefits of surveillance may take years to realise and are dependent on effective and accepted interventions. Benefits, including the less tangible, such as improved synergies and efficiencies in service delivery and more timely and accurate risk identification, should also be recognised. By including these less tangible benefits to society, animal welfare, ecosystem health and resilience, together with the savings and efficiencies through shared resources and social capital-building, a stronger business case for a One Health approach to surveillance can be made

Evidence in Practice – A Pilot Study Leveraging Companion Animal and Equine Health Data from Primary Care Veterinary Clinics in New Zealand

Veterinary practitioners have extensive knowledge of animal health from their day-to-day observations of clinical patients. There have been several recent initiatives to capture these data from electronic medical records for use in national surveillance systems and clinical research. In response, an approach to surveillance has been evolving that leverages existing computerized veterinary practice management systems to capture animal health data recorded by veterinarians. Work in the United Kingdom within the VetCompass program utilizes routinely recorded clinical data with the addition of further standardized fields. The current study describes a prototype system that was developed based on this approach. In a 4-week pilot study in New Zealand, clinical data on presentation reasons and diagnoses from a total of 344 patient consults were extracted from two veterinary clinics into a dedicated database and analyzed at the population level. New Zealand companion animal and equine veterinary practitioners were engaged to test the feasibility of this national practice-based health information and data system. Strategies to ensure continued engagement and submission of quality data by participating veterinarians were identified, as were important considerations for transitioning the pilot program to a sustainable large-scale and multi-species surveillance system that has the capacity to securely manage big data. The results further emphasized the need for a high degree of usability and smart interface design to make such a system work effectively in practice. The geospatial integration of data from multiple clinical practices into a common operating picture can be used to establish the baseline incidence of disease in New Zealand companion animal and equine populations, detect unusual trends that may indicate an emerging disease threat or welfare issue, improve the management of endemic and exotic infectious diseases, and support research activities. This pilot project is an important step toward developing a national surveillance system for companion animals and equines that moves beyond emerging infectious disease detection to provide important animal health information that can be used by a wide range of stakeholder groups, including participating veterinary practices

‘Next-Generation’ surveillance: an epidemiologists’ perspective on the use of molecular information in food safety and animal health decision-making

Advances in the availability and affordability of molecular and genomic data are transforming human health care. Surveillance aimed at supporting and improving food safety and animal health is likely to undergo a similar transformation. We propose a definition of ‘molecular surveillance’ in this context and argue that molecular data are an adjunct to rather than a substitute for sound epidemiological study and surveillance design. Specific considerations with regard to sample collection are raised, as is the importance of the relation between the molecular clock speed of genetic markers and the spatiotemporal scale of the surveillance activity, which can be control- or strategy-focused. Development of standards for study design and assessment of molecular surveillance system attributes is needed, together with development of an interdisciplinary skills base covering both molecular and epidemiological principles

The Infectious Disease Ontology in the Age of COVID-19

The Infectious Disease Ontology (IDO) is a suite of interoperable ontology modules that aims to provide coverage of all aspects of the infectious disease domain, including biomedical research, clinical care, and public health. IDO Core is designed to be a disease and pathogen neutral ontology, covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is then extended by a collection of ontology modules focusing on specific diseases and pathogens. In this paper we present applications of IDO Core within various areas of infectious disease research, together with an overview of all IDO extension ontologies and the methodology on the basis of which they are built. We also survey recent developments involving IDO, including the creation of IDO Virus; the Coronaviruses Infectious Disease Ontology (CIDO); and an extension of CIDO focused on COVID-19 (IDO-CovID-19).We also discuss how these ontologies might assist in information-driven efforts to deal with the ongoing COVID-19 pandemic, to accelerate data discovery in the early stages of future pandemics, and to promote reproducibility of infectious disease research

Report on the evaluation of surveillance systems relevant to zoonotic diseases in Kenya, 2015: A basis for design of an integrated human–livestock surveillance system

The Zoonoses in Livestock in Kenya (ZooLinK) is a project that seeks to enable Kenya develop an effective surveillance programme for zoonotic diseases (infectious diseases transmissible between animals and human beings). The surveillance programme will be integrated across both human and animal health sectors. To achieve this goal the project will work in close collaboration with Kenyan government departments in responsible for animal and human health. As a prelude to the start of the project, an evaluation of the existing surveillance systems for human and animal health was carried out. The evaluation focused on the national surveillance system and the systems at the western part of Kenya (Busia county, Kakamega county and Bungoma county) where the initial programme will be developed. In conducting the evaluation the investigators used key informant interviews, focused group discussion participant questionnaires, audio recordings and observation for data collection. Data analysis for the qualitative data focused on generating themes or theory around the responses obtained in the key informants interviews and focused group discussions. Univariate analysis was performed by use of simple proportions in calculation for surveillance system attributes like sensitivity, completeness, PVP and Timeliness for the human health surveillance systems. The findings of the evaluation revealed that there was poor linkage between animal health surveillance and the human health surveillance systems. None of the systems had surveillance structures dedicated to zoonotic diseases. Most practitioners used clinical signs for diagnosis of diseases with little reference to acceptable case definitions. Laboratory diagnosis in animal health services focused more on suspected notifiable diseases as opposed to being a standard operating procedure for diagnosis. In Human health services the health care facilities that had laboratory within the facility conducted laboratory diagnosis for cases referred by the clinicians. However, some clinicians preferred using clinical signs for diagnosis to avoid the wait or turn-around time in the laboratory. For effective surveillance of zoonoses to be realized it would be advisable to establish surveillance structures specific to zoonoses and the necessary resources allocated to the surveillance activities. In addition, an integrated approach that incorporated both human and animal disease surveillance should be employed in the surveillance of zoonoses

Approaches to canine health surveillance

Effective canine health surveillance systems can be used to monitor disease in the general population, prioritise disorders for strategic control and focus clinical research, and to evaluate the success of these measures. The key attributes for optimal data collection systems that support canine disease surveillance are representativeness of the general population, validity of disorder data and sustainability. Limitations in these areas present as selection bias, misclassification bias and discontinuation of the system respectively. Canine health data sources are reviewed to identify their strengths and weaknesses for supporting effective canine health surveillance. Insurance data benefit from large and well-defined denominator populations but are limited by selection bias relating to the clinical events claimed and animals covered. Veterinary referral clinical data offer good reliability for diagnoses but are limited by referral bias for the disorders and animals included. Primary-care practice data have the advantage of excellent representation of the general dog population and recording at the point of care by veterinary professionals but may encounter misclassification problems and technical difficulties related to management and analysis of large datasets. Questionnaire surveys offer speed and low cost but may suffer from low response rates, poor data validation, recall bias and ill-defined denominator population information. Canine health scheme data benefit from well-characterised disorder and animal data but reflect selection bias during the voluntary submissions process. Formal UK passive surveillance systems are limited by chronic under-reporting and selection bias. It is concluded that active collection systems using secondary health data provide the optimal resource for canine health surveillance

Research Note: Surveillance in contemporary health and social care: friend or foe?

Peer reviewedPublisher PD

Building the Science Foundation of a Modern Food Safety System: Lessons From Denmark, the Netherlands, and the United Kingdom on Creating a More Coordinated and Integrated Approach to Food Safety Information

Examines how food safety reforms in three countries and the European Union affected data collection and analysis, coordination and integrated approaches, and use of data for prevention. Makes recommendations for U.S. programs, policies, and activities