Marion Bordier
email: marion.bordier@cirad.fr

Theethawat Uea-Anuwong

Aurélie Binot
email: aurelie.binot@cirad.fr

Pascal Hendrikx
email: pascal.hendrikx@anses.fr

Flavie L Goutard
email: flavie.goutard@cirad.fr

Characteristics of One Health surveillance systems: A systematic literature review

Keywords: surveillance, One Health, framework, collaboration

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

The concept of One Health (OH) promotes the decompartmentalisation of human, animal, and ecosystem health for the more efficient and sustainable governance of complex health issues. This means that traditional boundaries between disciplines and sectors must be transgressed and that all relevant stakeholders must be involved in the definition and management of health problems.

International efforts have been made to strengthen collaboration across sectors and disciplines and OH surveillance is strongly encouraged at global, national and local-level to efficiently manage hazards involving humans, animals and ecosystems. This concept is intuitively appealing and would suggest the enhanced performance and costeffectiveness of surveillance systems, as compared to more conventional approaches. Nevertheless, confusion and uncertainty regarding the practical application, outcomes and impacts prevail. We believe that this is due to the lack of a conceptual and methodological framework which would (i) define the characteristics of OH surveillance, and (ii) identify the appropriate mechanisms for intersectoral and multi-disciplinary collaboration, to ensure that the surveillance system performs well, with regard to the objective, the context and the health hazard under surveillance.

The objective of the study is to define the organisational and functional characteristics of OH surveillance systems, the context in which they are implemented, as well as the influential factors which may obstruct or support their implementation and performance. To achieve this, a systematic literature review of existing OH surveillance systems was conducted using the Prisma guidelines.

The selected systems were assessed according to 38 predetermined variables. These allowed the characterisation of their objectives, organisation, functioning, performance and benefits. Data extraction was conducted using a spreadsheet and a database was built using an electronic multiplechoice questionnaire.

The literature search identified a total of 1,635 records. After the screening phase, 31 references were kept and 22 additional references retrieved from bibliographies were added. From these 53

Introduction

Current international consensus highlights the need to develop integrated policies to efficiently manage health issues at the human-animal-environment interface (Jeggo and Mackenzie, 2012).

The management of complex health issues should therefore shift from isolated, sectoral and linear, to systemic and transdisciplinary approaches to health. This requires the engagement of a wide range of stakeholders from different professional sectors and decision-making scales (including community) and of disciplines belonging to biosciences, social sciences and engineering [START_REF] Queenan | A One Health approach to antimicrobial resistance surveillance: is there a business case for it?[END_REF]. Such an approach is in line with the One Health (OH) concept, which promotes collaborative efforts across sectors and disciplines as well as an ecosystemic approach to health, to attain optimal health for humans, animals and their environment (AVMA, 2008;[START_REF] Zinsstag | From "one medicine" to "one 800 health" and systemic approaches to health and well-being[END_REF]. Close collaboration between health systems is therefore strongly encouraged, in particular in the surveillance of health hazards involving humans, animals and their environment (FAO, 2010).

This means that we must also consider ecosystems within which conditions may trigger health risks for humans and animals [START_REF] Queenan | A One Health approach to antimicrobial resistance surveillance: is there a business case for it?[END_REF].

Health surveillance is the ongoing, systematic collection, analysis and interpretation of healthrelated data with the a priori purpose of preventing or controlling health hazards and identifying unusual events of health importance, followed by the dissemination and use of such information for health action [START_REF] Lee | Public health surveillance and knowing about health in the context of growing sources of health data[END_REF]. There is no current consensual definition for a OH surveillance system. [START_REF] Stärk | One Health surveillance -More than a buzz word?[END_REF] and [START_REF] Berezowski | Do we need One Health surveillance?[END_REF] characterise OH surveillance as a system that collects data in multiple domains. For [START_REF] Hattendorf | One Health and its practical implications for surveillance of endemic zoonotic diseases in resource limited settings[END_REF], using a OH approach to surveillance does not automatically imply that data must be collected both from animals and humans, as long as there is inter-sectoral collaboration that leads to improved health management. The definition provided by [START_REF] Karimuribo | Towards One Health disease surveillance: the Southern African Centre for Infectious Disease Surveillance approach: proceeding[END_REF] also emphasises cross-sectoral collaborative efforts between the human and animal (wildlife and domestic) sectors.

Despite a lack of evidence to support this, the application of the OH concept to surveillance is expected to increase efficiency, cost-effectiveness and cost-benefits [START_REF] Stärk | One Health surveillance -More than a buzz word?[END_REF][START_REF] Martins | Economics of zoonoses surveillance in a "One Health" context: an assessment of Campylobacter surveillance in Switzerland[END_REF]. Nevertheless, surveillance systems continue to be developed and operated in a highly sectoral approach [START_REF] Baum | Evaluating one health: Are we demonstrating effectiveness?[END_REF]. We argue that the OH approach is difficult to implement in the field of surveillance, partly due to the absence of a conceptual and methodological framework that characterises OH surveillance and supports the implementation of appropriate cross-sectoral and multi-disciplinary collaboration. Depending on the surveillance context and objective, the required collaborative efforts across sectors and disciplines might differ. They might be implemented for various activities throughout the surveillance process and engage different combinations of sectors, disciplines and decision-making scales (Dente et al., 2016;[START_REF] Martins | Economics of zoonoses surveillance in a "One Health" context: an assessment of Campylobacter surveillance in Switzerland[END_REF][START_REF] Hattendorf | One Health and its practical implications for surveillance of endemic zoonotic diseases in resource limited settings[END_REF].

We conducted a systematic literature review of the organisational and functional characteristics of existing OH surveillance systems, as well as the influential factors which may obstruct or support their implementation. Based on our results, we propose a framework that characterises the organisation of collaboration for the governance and operation of effective OH surveillance systems, and the factors that influence their performance and maintenance over time.

Materials and methods

In the absence of a consensual definition for a OH surveillance system and based on elements found in published literature [START_REF] Karimuribo | Towards One Health disease surveillance: the Southern African Centre for Infectious Disease Surveillance approach: proceeding[END_REF][START_REF] Berezowski | Do we need One Health surveillance?[END_REF]Stark et al, 2015;[START_REF] Hattendorf | One Health and its practical implications for surveillance of endemic zoonotic diseases in resource limited settings[END_REF], the following definition is proposed for this study. A OH surveillance system is a system in which collaborative efforts exist between at least two sectors (among human health, animal health, plant health, food safety, wildlife and environmental health) at any stage of the surveillance process, to produce and disseminate information with the purpose of improving an aspect of human, animal or environmental health.

Literature sources and search strategy

A systematic literature search was conducted according to the PRISMA requirements (Preferred Reporting Items for Systematic Reviews and Meta-analysis) [START_REF] Moher | Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement[END_REF]. Searches were conducted using Google Scholar, PubMed and ScienceDirect. The literature search focused on scientific and grey literature, in French and English, published between 01/01/1985 and 31/12/2016. Keywords for four different domains were used, and applied only to the title, abstract and key words within literature (See Table 1).

Study selection

All documents retrieved from the bibliographic databases were screened by two reviewers following two distinct steps. For the first step, three inclusion criteria were applied to titles and abstracts: (i) the document describes a surveillance system (as defined previously), (ii) the surveillance system focuses on a health hazard, (iii) the surveillance system shows evidence of collaborative efforts between professionals working in at least two different sectors, among animal health, human health, food safety and the environment. In the second step, only references with the full text available were screened. An additional criterion was used: the document provides a detailed description of the surveillance organisation and operation. Articles and reports meeting all inclusion criteria were registered. Bibliographies of selected publications were reviewed to identify other relevant references.

Data extraction

To meet the objective of the review, the selected surveillance systems were assessed according to 38 predetermined variables, allowing the description of the organisation, the functioning, the surveillance context, the health hazards and domain under surveillance, the type of collaboration and underlying mechanisms, the barriers and favouring factors regarding on-going collaboration, and the performance and benefits of the systems (See Table 2). Variables related to collaboration were slightly refined during the information collection to capture the different dimensions and areas of collaboration arising from the literature review process. Data extraction was conducted using a spreadsheet; a database was then developed by entering this data into an electronic multiple-choice questionnaire, with pre-defined modalities. If data for certain organisational and functional variables was missing, additional searches were conducted on the webpages of the coordinating institutions to retrieve the missing information.

Results

The literature search identified a total of 1,635 records. After the screening phase, 31 references were kept and 22 additional references retrieved from bibliographies were added (Figure 1). From these 53 documents, we retrieved 41 different surveillance systems in line with the definition. Table 3 describes these systems regarding six main variables: hazard(s) under surveillance, domain(s) under surveillance, objective and purpose, coordination modalities (number of institutions involved and type of sector they belong to), sectors involved in the operation of the surveillance and type of inter-sectoral collaboration.

Dimensions and degrees of collaboration in One Health surveillance systems

The analysis of the existing systems led to the identification of four main dimensions where collaboration across sectors and disciplines may occur (variables 25 to 33 in Table 2): (i) institutional collaboration across sectors for the governance and operation of the surveillance system; (ii) collaboration at the different scales of the decision-making process; (iii) collaboration across disciplines; (iv) collaboration through public-private partnerships. These four dimensions are described in more detail below.

The first dimension refers to collaboration between sectoral institutions with different jurisdictions and mandates, mainly public health, animal health, plant health, environmental health and food safety. Collaboration can take place at the governance-level for the coordination and supervision of the surveillance system and/or at the operational-level for the implementation of surveillance activities, at the different steps of the surveillance process. Multi-institutional coordination is in place for 43.9% of the surveillance systems. If the coordination is led by a single sectoral institution (mainly the public health sector), in 87% of the cases, collaboration is established at the operational-level, with institutions in charge of other domains covered by the surveillance system. Where the mechanisms supporting institutional collaboration are described (36.6% of the surveillance systems), these most commonly (80% of the cases) include establishment of an inter-agency committee and/or the existence of official documents framing collaboration. Official documents are usually legal instruments, such as the inter-ministerial circular that describes the role and responsibilities of each party in the surveillance system for West Nile virus in France (Ministry of Health, 2012), or the legally binding agreement for data sharing between the animal health and the human health sectors within the RAIZO, in Canada [START_REF] Roth | Surveillance for emerging infectious diseases: a Canadian perspective[END_REF]. At the operational-level, various degrees of collaboration were identified at all steps of the surveillance process: planning, data collection (including sampling and laboratory testing), data management, data sharing, data analysis/interpretation and results dissemination. These degrees of collaboration are presented in Figure 2.

The second collaborative dimension concerns the engagement of different disciplines, among biosciences, social sciences and engineering. The data retrieved was insufficient to allow a detailed description of the proportion of disciplines engaged in the surveillance process, but disciplines referring to biosciences (medicine, microbiology, epidemiology, entomology, ornithology, parasitology) showed a significantly higher representation. These disciplines can be used for different occupational purposes (risk assessment, risk management, research, etc.).

A third collaborative dimension can be described regarding collaboration between different decision-making scales. These scales include the different administrative jurisdictional scales within a same country (central, provincial and local authorities) but also the supra-national scales such as the international-scale (e.g. international organisations) or the regional-scale (e.g. regional economic communities). Within this dimension, the engagement of civil society must also be considered and is, for instance, clearly emphasised for two surveillance systems targeting rabies [START_REF] Abbas | Rabies control initiative in Tamil Nadu, India: a test case for the "One Health" approach[END_REF][START_REF] Lapiz | Implementation of an Intersectoral Program to Eliminate Human and Canine Rabies: The Bohol Rabies Prevention and Elimination Project[END_REF].

Finally, a last collaborative dimension can be defined through the development of public-private partnerships within, but also across, sectors. For instance, in Canada, veterinary pharmaceutical companies as well as private veterinarians collaborate within the surveillance system for antimicrobial resistance, which is coordinated by the Ministry of Health (CIPARS, 2015;[START_REF] Grant | Surveillance of antimicrobial resistance and antimicrobial utilization in Canada[END_REF]. Reported private partners are mainly veterinarians, physicians, private laboratories, farmers, feed/food operators and pharmaceutical companies, on an individual basis or through professional organisations.

Factors influencing collaboration

Favouring factors

Factors that have positively influenced the implementation and the functioning of a collaborative surveillance system are mentioned for 21 systems (51.2%). Factors related to the existence of an appropriate framework to ease collaboration across sectors are the most numerous (71.4%). Some surveillance systems are embedded in an overarching OH programme and benefit from the existing inter-sectoral framework to develop collaborative surveillance activities. This is the case for the surveillance of rabies in Bohol, where collaborative surveillance activities are part of a programme for the elimination of rabies supervised by an inter-sectoral council [START_REF] Lapiz | Implementation of an Intersectoral Program to Eliminate Human and Canine Rabies: The Bohol Rabies Prevention and Elimination Project[END_REF]. Other references emphasise the existence of an appropriate legal or institutional framework as a lever for collaboration [START_REF] Abbas | Rabies control initiative in Tamil Nadu, India: a test case for the "One Health" approach[END_REF][START_REF] Adamson | One Health in NSW: coordination of human and animal health sector management of zoonoses of public health significance[END_REF][START_REF] Lapiz | Implementation of an Intersectoral Program to Eliminate Human and Canine Rabies: The Bohol Rabies Prevention and Elimination Project[END_REF]Ministry of Health, 2012). For two systems, however, collaboration is not supported by an official framework but by preferential relationships existing between individuals working in different sectors and disciplines (Epp et al., 2008;[START_REF] Adamson | One Health in NSW: coordination of human and animal health sector management of zoonoses of public health significance[END_REF]. For the surveillance of rabies in Tamil Nadu [START_REF] Abbas | Rabies control initiative in Tamil Nadu, India: a test case for the "One Health" approach[END_REF], the clear definition of roles and duties of the different agencies involved is considered to have strongly supported the operationalisation of collaboration. The existence of inter-sectoral collaboration mechanisms already established at a supra-level will also usually provide a framework for infra-level collaboration. For instance, in Mongolia, the implementation of a multi-agency committee for zoonotic diseases at country-level was facilitated by the existence of inter-sectoral strategy at the regional-level -the Asia Pacific Strategy for Emerging Diseases [START_REF] Batsukh | One Health in Mongolia[END_REF]. Finally, the last factor related to the existence of an appropriate framework to support collaboration refers to the supervision, by the same authority, of sectors in charge of surveillance components. In Italy, the veterinary services are under the authority of the Ministry of Health, and this administrative organisation is considered to strengthen the channels of communication across animal health and public health professionals within the integrated surveillance system for West Nile Virus [START_REF] Rizzo | Epidemiological surveillance of West Nile neuroinvasive diseases in Italy, 2008 to 2011[END_REF][START_REF] Napoli | Vector Borne Infections in Italy: Results of the Integrated Surveillance System for West Nile Disease in 2013[END_REF]. Other favouring factors are related to mechanisms ensuring the commitment of stakeholders, at the political and operational-levels (51.7% of the systems). For three systems [START_REF] Talaska | A salmonella data bank for routine surveillance and research[END_REF]Sleigh et al., 1998a;Sleigh et al., 1998b;[START_REF] Wielinga | Evidence-based policy for controlling antimicrobial resistance in the food chain in Denmark[END_REF], efficient and appropriate communication and consultation channels helped in achieving stakeholder commitment within the collaborative system. In two cases, the ability of the system to meet the objectives of the different stakeholders was specifically identified to be the key to success and sustainability of the surveillance system [START_REF] Adamson | One Health in NSW: coordination of human and animal health sector management of zoonoses of public health significance[END_REF]Donado-Godoy et al., 2015). For 51.7% of the systems, epidemiological factors are also mentioned as a motivation to establish collaboration for surveillance activities, such as the scientific evidence of the efficiency of using animal sentinels or vector surveillance components to protect human health [START_REF] Mcnamara | The Human-Animal Interface and Zoonotic Threats: The Russian Federation Approach[END_REF][START_REF] Morgan | Control of arbovirus infections by a coordinated response: West Nile Virus in England and Wales[END_REF][START_REF] Petrić | West Nile virus "circulation" in Vojvodina, Serbia: Mosquito, bird, horse and human surveillance[END_REF], or the necessary recognition of the interconnectivity between domains in the conception of an efficient surveillance system [START_REF] Talaska | A salmonella data bank for routine surveillance and research[END_REF][START_REF] Morgan | Assessing the risk from emerging infections[END_REF][START_REF] Witt | Department of Defense West Nile virus surveillance in 2002[END_REF][START_REF] Vrbova | Utility of algorithms for the analysis of integrated Salmonella surveillance data[END_REF]. For instance, some technical factors are also considered to favour collaboration and integration of data from different domains for 23.8% of the systems. The most common one refers to the availability of a joint database or the ease of data exchange, thanks to compatible sectoral information systems. In the surveillance of Salmonella in Brandenburg, a common data-bank is recognised to have stimulated the commitment of stakeholders to the collaborative system [START_REF] Talaska | A salmonella data bank for routine surveillance and research[END_REF]. The AFHSC-Division of GEIS operations predictive surveillance programme has developed, with partners from different sectors, a model merging data collected from several sources that supports pre-event advisories and alerts on the emergence of disease outbreaks [START_REF] Witt | The AFHSC-Division of GEIS Operations Predictive Surveillance Program: a multidisciplinary approach for the early detection and response to disease outbreaks[END_REF]. Another technical factor is related to the crucial role played by a fully functional national reference laboratory for harmonisation across data-sets and their further combination, as emphasised by [START_REF] Ammon | Integrated data collection on zoonoses in the European Union, from animals to humans, and the analyses of the data[END_REF] in their description of the surveillance of zoonotic diseases in the European Union.

Barriers

Barriers that hamper the operation of collaborative surveillance systems have been specified for 20 systems (48.8%). These are mostly technical barriers (78.6%): a lack of standardisation and harmonisation for data collection, incomplete data, insufficient data-sharing across sectors including unreliable cross-sectoral alert systems, incomplete multi-domain data analysis and interpretation. In four cases, the collaboration might not have reached a sufficient level because of the absence of engagement among the private sector [START_REF] Sorensen | Interplay between policy and science regarding low-dose antimicrobial use in livestock[END_REF] or an insufficient integration with certain sectoral components still conducted separately [START_REF] Adamson | One Health in NSW: coordination of human and animal health sector management of zoonoses of public health significance[END_REF][START_REF] Roth | Surveillance for emerging infectious diseases: a Canadian perspective[END_REF][START_REF] Sorensen | Interplay between policy and science regarding low-dose antimicrobial use in livestock[END_REF]. As a result, the systems cannot meet their objective, such as the detection of health events in animals to prevent human cases or the attribution of sources for human cases of food-borne diseases. In addition, legal constraints are also mentioned for 42.9% of systems: the property and confidentiality of data, ethical issues, and an inadequate legal and operational framework to precisely define the roles and mandates of the different actors involved and to support collaboration at ground-level. Inappropriate amounts and allocation of resources are also impediments to collaborative approaches. On the one hand, budgets are vertically allocated and there are no resources available for cross-sectoral actions. On the other hand, resources are scarce, especially for surveillance activities, and stakeholders may have to compete for them, reinforcing the lack of collaboration [START_REF] Batsukh | One Health in Mongolia[END_REF][START_REF] Johnson | The challenges of implementing an integrated One Health surveillance system in Australia[END_REF]. Finally, competing priorities among actors may also obstruct the involvement of the different parties in a OH surveillance system. In the surveillance system for zoonotic diseases in New South Wales, the different interests of each sector in zoonoses is responsible for the inconsistency of notification between sectors [START_REF] Adamson | One Health in NSW: coordination of human and animal health sector management of zoonoses of public health significance[END_REF].

Discussion

The systematic literature review retrieved 41 existing surveillance systems, in which collaboration across sectors and disciplines may occur at different steps of the surveillance process and to various degrees. These systems are mainly characterised by the hazard under surveillance, the surveillance purpose, the type of sector leading the coordination and the type of sectors involved in the surveillance activities, as emphasised by the results of a multi-variate analysis conducted on the database (results not yet published).

However, these results should be interpreted with caution, due to certain biases in the retrieval methodology of the documents describing surveillance systems. Many surveillance systems, and especially those established for official purposes, do not necessarily lead to publications and so might not be included in our study. During the review, some documents referring to our definition of OH surveillance system were initially retrieved but were subsequently excluded from the analysis as they did not provide enough information. On the contrary, some surveillance systems may demonstrate collaboration across sectors, but as they were not mentioned in the references retrieved, they were not captured by our study. Moreover, the organisation of some systems may have evolved further since publication describing it, and data used for the analysis might be outdated. Additionally, the level of information relative to surveillance organisation may vary from one document to another, and some characteristics may not have been captured in our study because they were not mentioned by the authors. For instance, this systematic literature review does not allow the identification of certain barriers to collaboration which are commonly described as underpinning the operationalisation of OH surveillance, such as differing priorities between riskbearers and risk managers, data sequestration, the undervaluation of certain sectors and disciplines or the fear of losing ownership and leadership [START_REF] Häsler | The Economic Value of One Health in Relation to the Mitigation of Zoonotic Disease Risks[END_REF][START_REF] Uchtmann | Barriers to, Efforts in, and Optimization of Integrated One Health Surveillance: A Review and Synthesis[END_REF]. This can be explained by the fact that the objective of the review was to retrieve documents describing the organisation and functioning of OH surveillance systems and that barriers were poorly described in the selected documents. Finally, research on OH surveillance is gaining increasing attention and the study did not include some articles published after the search period.

The definition used for a OH surveillance system (collaboration among at least two of the following sectors: animal, human and environment) can be questioned regarding the most commonly agreed OH definition that promotes the inclusion of all three sectors (AVMA, 2008;[START_REF] Zinsstag | From "one medicine" to "one 800 health" and systemic approaches to health and well-being[END_REF]. Moreover, the COHERE standards consider an OH epidemiological study only if it reports data collected in all three domains (Davis et al, 2017). However, some surveillance systems are set with a specific objective in a given socio-economic context that do not require or allow the inclusion of all the three domains. In our view, even if they do not include the three domains, surveillance systems demonstrating collaborative efforts among sectors and disciplines towards a more holistic approach should still benefit from consideration from a OH perspective.

Moreover, challenges encountered for their operationalisation are similar and require the same needs in terms of governance and operational framework to favour their implementation.

Nevertheless, to avoid confusion over terminology, these systems could be renamed, and "collaborative surveillance" is suggested.

In the last decade, the OH concept has been endorsed and largely promoted at the global and local-level [START_REF] Vandermissen | Current initiatives in One Health: consolidating the One Health Global Network[END_REF]. Despite the persistence of silo-thinking, many initiatives have emerged. In terms of surveillance, this study suggests that efforts mainly focus on the prevention of zoonotic diseases (including vector-borne and food-borne diseases), and more recently of antimicrobial resistance. The review has retrieved only two articles describing surveillance initiatives focusing on non-communicable hazards that bridged health and environmental sciences in an effort to address health risks related to environmental contaminants [START_REF] Abelsohn | Environmental Public Health Tracking/Surveillance in Canada: A Commentary[END_REF]CDC, 2004;[START_REF] Malone | Managing public health risks: role of integrated ocean observing systems (IOOS)[END_REF]. Nevertheless, environmental contaminants (such as heavy metals, dioxins, PCB, myco-and phycotoxins, etc.) are a quintessential OH issue. Animals and humans share the same environment and the same sources of food and water; therefore, they are potentially exposed to the same chemicals. Additionally, humans can be contaminated through the ingestion of contaminated animal products, which are an essential part of the human diet [START_REF] Buttke | Toxicology, Environmental Health, and the "One Health" Concept[END_REF]. Moreover, it has been demonstrated that animals are sensitive indicators of environmental chemical hazards and could serve as sentinels for human environmental health risks [START_REF] Reif | Animal sentinels for environmental and public health[END_REF][START_REF] Pearce | Research at the interface between human and veterinary health[END_REF]. Environmental contamination thus calls for a highly interdisciplinary approach to appropriately respond to the related health risk. Nevertheless, our study only retrieved a few examples of OH surveillance systems addressing chemical environmental hazards.

The definition of the OH concept is linked with the notion of attaining optimal health at once for humans, animals and the environment. In most of the documents retrieved, the primary purpose of the collaboration across sectors was to improve human health only. Nevertheless, OH offers the possibility to transcend the anthropocentric view of health and to shift the current focus to a more balanced strategy with benefits to all domains to improve health development in a sustainable way, as described by [START_REF] Queenan | Roadmap to a One Health agenda 2030[END_REF]. If relevant, collaborative surveillance systems could be developed with a wider perspective and used to inform interventions in the animal and environmental sectors, to obtain gains for the health and welfare of animals, plants, and ecosystems [START_REF] Rüegg | A Blueprint to Evaluate One Health[END_REF][START_REF] Rüegg | A Systems Approach to Evaluate One Health Initiatives[END_REF]. For instance, human diseases or behaviours could be monitored and act as risk indicators for animal and environmental health.

The study has highlighted that OH surveillance is often assimilated to integrated surveillance, insofar that data from different sources is jointly collected and/or a posteriori combined. The concept of collaboration, a fundamental principle of the OH concept [START_REF] Zinsstag | From "one medicine" to "one 800 health" and systemic approaches to health and well-being[END_REF], is therefore not inherent to integrated surveillance when defined in this way. Indeed, a surveillance system can allow the acquisition and combination of data stemming from several domains, without demonstrating any beneficial collaboration for health management across sectors and disciplines.

For instance, the surveillance of zoonotic diseases in the Russian Federation includes data collection in humans, wildlife and the environment and is coordinated by the public health sector alone, with limited collaboration with other sectors. This is considered to hamper the performance of the surveillance system [START_REF] Mcnamara | The Human-Animal Interface and Zoonotic Threats: The Russian Federation Approach[END_REF]. As a result, this system may be considered more as an integrated system than a OH surveillance system. The term OH is also often used to characterise surveillance systems in food, because they integrate data collected at the different stages of the food chain. If data is collected and analysed by a single sector and does not support interventions to improve the health situation in another sector, this approach should not be considered as OH, as no added value emerges from inter-sectoral collaboration. On the contrary, regarding our definition, surveillance systems could be labelled OH even when collecting data in a single domain, if this data is used to inform another sector to improve health management. For instance, in the Gulf of Mexico, the national agency in charge of the environment monitors coastal waters (ecological and biological data) to predict blooms of harmful algae. Results are transmitted to the authorities in charge of public health and fisheries so that they can take appropriate action to manage the risk in their respective jurisdictions [START_REF] Abelsohn | Environmental Public Health Tracking/Surveillance in Canada: A Commentary[END_REF]. The risk mitigation measures would not have been implemented if collaboration was not operational across the three sectors. Hence, "integrated" and "OH" should not be considered as synonymous. Using one term for another interchangeably is confusing and does not support the effective operationalisation of the OH concept in the field of surveillance.

The same observation can be made regarding the terms "multi-disciplinary" and "multi-sectoral" which are regularly used, one for the other, to describe ongoing collaboration happening within surveillance systems. Discipline refers to a branch of knowledge (medicine, epidemiology, economics, sociology, etc.) while sector refers to a branch of activities (animal health, public health, food and water safety, environmental health, etc.). In our view, a surveillance system showing a multi-disciplinary approach without cross-sectoral collaboration should not be qualified as OH.

Indeed, even if a sectoral institution establishes a multi-disciplinary team to integrate knowledge usually mobilized by other sectors, it will not be able to consider all the dimensions related to this sector (stakeholders, constraints, expectations, socio-economic factors, etc.). Multi-disciplinary and multi-sectoral are intrinsically linked but not interchangeable. Cross-sectoral collaboration will automatically lead to a multi-disciplinary approach, as each sector mobilizes at least one discipline (medicine, ecology, food hygiene, etc.). On the contrary, a multi-disciplinary approach can be developed within one sector without additional cross-sectoral collaboration.

Transdisciplinarity is, however, the quintessence of a OH initiative and refers to the integration across both sectors and disciplines [START_REF] Rüegg | A Systems Approach to Evaluate One Health Initiatives[END_REF]. This approach links societal and scientific problems together, by combining scientific and extra-scientific insights. It creates new connections across distinct epistemic, social-organisational and communicative entities that are part of the problem context [START_REF] Jahn | An actor-specific guideline for quality assurance in transdisciplinary research[END_REF]. Surveillance systems designed according to a transdiciplinary approach will therefore entirely fulfil the requirements of a OH approach in its broader definition. Interests, expectations and knowledge of the different scientific, societal and political stakeholders and end-users of the system are considered, and the new knowledge produced is expected to contribute to the well-balanced improvement of animal, human, and environmental health.

Our findings reinforce the hypothesis that the lack of a conceptual framework to accurately define the notion of OH surveillance is undermining the operationalisation of collaborative efforts for efficient and sustainable surveillance systems. From our perspective, a OH surveillance system is, above all, characterised by the collaboration taking place among professional sectors (both public and private) and disciplines, at different decision-making scales, to coordinate and implement appropriate surveillance activities. Based on the analysis of existing OH surveillance systems, we propose a conceptual framework (Figure 3) to describe the different organisational levels of collaboration that need to be taken into consideration, and the factors influencing their effective governance and operation, in the long-term.

In our framework, we distinguished three different levels where collaboration must be organised and planned: (i) the policy-level, (ii) the institutional-level and (iii) the operational-level. At the policy-level, the collaborative strategy is clearly defined: collaborative efforts are elucidated in broad terms emphasising the rationale behind the necessary collaborative efforts as well as the OH surveillance objective. The different dimensions (sectors, disciplines, decision-making scales and public-private partnerships) in which collaboration will take place are clarified and the role of the surveillance actors acting and interacting in those dimensions are stated. Mechanisms for the steering and coordination of the collaborative surveillance system, as well as for scientific and technical support, are clarified. The way in which resources will be mobilized across sectors and then allocated to collaborative activities is established. For instance, [START_REF] Roth | Human health benefits from livestock vaccination for brucellosis: case study[END_REF] propose that the budget allocated by each ministry for cross-sectoral activities could be proportional to the benefits that derive from the collaboration for each sector. In some countries, the government has been reorganised to reduce operational costs and silo-functioning. This is the case in Denmark, where a new Ministry in charge of food, agriculture and fisheries has been created, which is recognised to have improved inter-sectoral collaboration and thus the management of antimicrobial resistance [START_REF] Wielinga | Evidence-based policy for controlling antimicrobial resistance in the food chain in Denmark[END_REF]. All these decisions need to be formalised and endorsed by stakeholders to ensure their further commitment. Depending on the country and context, the policy can be enunciated in policy or strategy documents, national action-plans or programmes, memorandums or directly released in legal instruments. This policy framework provides guidance to organise collaboration across professional institutions for the surveillance activities. At the institutional-level, appropriate collaboration modalities are then defined to achieve the desired goals of the policy. Collaboration is described in terms of: areas of implementation (planning, sampling, laboratory testing, data management and storage, data sharing, data analysis and interpretation, results dissemination), actors involved and their respective roles and responsibilities, technical mechanisms to support collaboration (establishment of a shared database, a working group, etc.), and mechanisms for the allocation and deployment of human, material and financial resources. The institutional framework defined at the national-level should be broken down at the sub-nationallevel to ensure coordination and harmonisation across the different jurisdictional levels, between and within each sector, if deemed necessary. This is of particular importance for official surveillance in countries experimenting with a decentralised system. Regulatory instruments, agreements or charters are issued to formalise and provide a frame for the implementation of the above decisions. Finally, institutional collaboration is translated into specific surveillance actions.

This requires the establishment of procedures (or other similar mechanisms) in each institution involved to ensure the routine operation of the collaborative surveillance system, in compliance with the organisational structure decided at the policy and institutional-level. As for any surveillance system, the organisation and operation of a OH system are influenced by a set of contextual factors (epidemiological, ecological, economic, social and environmental) (RISKSUR, 2015) but also by the constraints and expectations of all the different actors and end-users, as well as international guidance. Nevertheless, in the case of OH surveillance, these factors are of particular importance as they may influence the inter-sectoral collaboration pattern, as well as the dimensions and areas of collaboration required to meet the surveillance objective. For instance, for the surveillance of antimicrobial resistance, the international community calls for countries to implement multi-domain surveillance involving the private sector and to provide guidance for the development of integrated surveillance in humans, food-producing animals and food of animal origin (WHO, 2017). Many countries have developed their surveillance strategy to comply with this guidance. The analysis of levers and barriers to collaboration in existing multi-sectoral surveillance systems resulted in the identification of a wide range of drivers that impact the performance and sustainability of OH surveillance. First, depending on the surveillance objective and context, the appropriate sectors (including both public and private institutions), decision-making scales and disciplines, must be identified and then involved in the governance and operation of the collaborative surveillance. In addition to the resources required to run the sectoral surveillance components, specific resources must be allocated for activities involving several sectors, both at the governance (provision of personnel to participate in steering committee, provision of appropriate training, evaluation of the system, etc.) and operational-level (organisation and participation in multi-sectoral working groups, development and maintenance of a joint database, etc.). Appropriate mechanisms must be defined and established to technically allow the collaboration to be implemented. For instance, efficient data-sharing on a routine basis would be hampered by the absence of a common database or incompatible sectoral information systems [START_REF] Adamson | One Health in NSW: coordination of human and animal health sector management of zoonoses of public health significance[END_REF]. A crucial element is the identification of the area and degree of collaboration that will achieve the OH surveillance objective in the given context. Indeed, the concept of a OH surveillance system is not synonymous with an all-integrated system and collaboration can take place to various extents and at different steps during the surveillance process. Collaboration is resource-consuming; it is therefore important to find the minimum level of collaboration that will achieve the optimal performance and cost-effectiveness (Babo [START_REF] Martins | Economics of zoonoses surveillance in a "One Health" context: an assessment of Campylobacter surveillance in Switzerland[END_REF]. However, only proper and rigorous evaluations of surveillance, based on sound and appropriate methods, will allow the relevance and effectiveness of collaboration to be assessed. Collaboration that does not demonstrate any benefit would only result in decreasing stakeholder commitment and in hampering the sustainability of the system.

Conclusions

This review highlights that collaboration taking place in a OH surveillance system exists in different dimensions (across sectors, disciplines, decision making-scales and through public-private partnerships) and can be implemented at various steps of the surveillance process (from planning to dissemination of the surveillance results) with various degrees of integration. Several internal or external factors influence both the effective functioning of surveillance systems, as well as their sustainability overtime.

Even if a rigorous framework must be considered at the policy and institutional-level to ensure the effective operation of a OH surveillance system, there is not a single model for OH surveillance.

Collaboration must be tailored to the surveillance objective and context, characterised by a wide range of factors (epidemiological, ecological, economic, social and environmental), and must consider the constraints and expectations of all surveillance actors and end-users. To assess if ongoing collaboration is appropriate and effective, evaluations should be conducted with a focus on the quality of inter-sectoral and inter-disciplinary collaboration. Specific evaluation attributes must be developed to allow the measurement of impacts and of the benefit resulting from collaborative surveillance as compared to a juxtaposition of isolated sectoral surveillance components. Davis, M.F., Rankin, S.C., Schurer, J.M., Cole, S., Conti, L., Rabinowitz, P., Gray, G., Kahn, L., Machalaba, C., Mazet, J., Pappaioanou, M., Sargeant, J., Thompson, A., Weese, S., Zinnstag, J., 2017. Checklist for One Health Epidemiological Reporting of Evidence (COHERE). One Health 4, One Health approach "one health", "one medicine", ecohealth, holistic, "global health", "integrated surveillance", "integrated approach", "integrated system", "integrated data", "integrating data", inter-sector*, intersector

  .G., Riccardo, F., Nacca, G., Ranghiasci, A., Manuguerra, J.C., Escadafal, C., Jimenez-Clavero, M.A., Ramirez, E.P., Robert, V., Picard, M., 2016. Strengthening integrated surveillance for arboviruses in the Mediterranean and Black Sea regions in the framework of the One Health approach. Quaderni Della Società Italiana Di Medicina Tropicale E Salute Globale. Donado-Godoy, P., Castellanos, R., León, M., Arevalo, A., Clavijo, V., Bernal, J., León, D., Tafur, M.A., Byrne, B.A., Smith, W.A., Perez-Gutierrez, E., 2015. The Establishment of the Colombian Integrated Program for Antimicrobial Resistance Surveillance (COIPARS): A Pilot Project on Poultry Farms, Slaughterhouses and Retail Market. Zoonoses and Public Health 62, 58-69. EMPRES, 2000. RVF surveillance in West Africa. Bulletin 2000 15/3-4, 9. RVF. www.fao.org/3/a-x9550e.pdf (accessed 19/12/2017). Epp, T., Waldner, C., Corrigan, R., Curry, P., 2008. Public Health Use of Surveillance for West Nile Virus in Horses: Saskatchewan, 2003-2005. Transbound. Emerg. Dis. 55, 411-416. FAO, 2010. The FAO-OIE-WHO collaboration. Sharing responsibilities and coordinating global activities to address health risks at the animal-human-ecosystems interfaces. www.fao.org/3/a-i3579e.pdf (accessed 18/12/2017).

Figure captions Figure 1 .

 captions1 Figure captions

Figure 2 .

 2 Figure 2. Possible degrees of operational collaboration at the different steps of the surveillance process.

Figure 3 .

 3 Figure 3. Organisation of collaboration in a One Health surveillance system: a conceptual framework.

Table 1 .

 1 Terms for search in bibliographic databases 810

	Domains	Key words
	Surveillance	Surveillance OR monitor*

Table 2 .

 2 *, cross-sector*, multi-sector*, multisector*, interdisciplinar*, inter-disciplinar*, multidisciplinar*, multi-disciplinar*, trans-disciplinar*, transdisciplinar* Health hazard disease* OR infection OR zoono* OR syndrom* OR outbreak* OR hazard* OR environment* OR residue* OR pesticid* OR pathogen OR bacteria OR antimicrobial* OR "antibiotic resistance"* OR virus OR parasit* OR contaminant* OR toxin* Population animal*, livestock, veterinar*, fish*, aquaculture, wildlife, food, herd*, farm*, cattle, cow*, bovine, ruminant*, pig, pigs, swine, poultry, bird*, avian , horse*, equine, dog*, cat, cats, sheep, goat*, plant* Variables used for the characterisation of the surveillance systems

	811	*truncation operator
	812
	813
		Level		Variable
		Coordination of the	1	Mono or multi-institutional coordination
		surveillance system	2	Number of institutions in charge of the coordination
			3	Type of institutions involved in the coordination (government, academia,
				independent agency, etc.)
			4	Administrative-level in charge of the coordination
			5	Number of sectors involved in the coordination
			6	Type of sectors in charge of the coordination
		Geographical area	7	Level of coverage of the surveillance (supra-national, national, subnational)
			8	Territory under surveillance
		Date	9	Year of establishment of first collaborative efforts
		General organisation 10	Status of the surveillance system (stand-alone or part of a programme)
			11	Origin of funds (state, private, external, etc.)
			12	Sustainability of funding
			13	A priori or a posteriori integration of sectoral surveillance components
		Objectives and	14	Objectives of the surveillance system
		purposes	15	Purposes of the surveillance systems
		Hazards under	16	Number of hazards (mono or multi-hazards)
		surveillance	17	Type of hazards
			18	Communicability of hazards under surveillance
		Domains under	19	Type of domains under surveillance (domestic animal, human, food, wildlife,
		surveillance		etc.)
			20	Number of domains under surveillance
			21	Data sources in each domain
			22	Type of data in each domain
			23	Epidemiological status in each domain
		Terminology	24	Terms which are used to describe inter-sectoral and inter-disciplinary
				collaboration
		Type of collaboration 25	Type of sectors collaborating within the surveillance process

Mechanisms in place to support institutional collaboration

Acknowledgements

This work was funded in part by the French Ministry of Agriculture, the GREASE platform in partnership (www.grease-net.org) and the ComAcross project (www.onehealthsea.org/comacross) implemented with the financial support of the European Union (EuropeAid, INNOVATE contract 315-047). We would also like to thank the InterRisk Program at Kasetsart University (Bangkok) for providing the scholarship of Theethawat Uea-Anuwong, who has contributed to the data collection.

Decision-making scales involved in surveillance activities (supra-national authorities/organisations, national authorities, sub-national authorities, etc.) Private actors involved in surveillance activities (veterinarians, food/feed operators, pharmaceutical companies, etc.) Type of collaborative efforts for surveillance activities (conception of the surveillance protocol, joint sampling campaigns, laboratory facilities sharing, data exchange, inter-sectoral data analysis and interpretation, etc.) Mechanisms in place to support collaboration for surveillance activities Type of collaborative efforts for dissemination of surveillance results Mechanisms in place to support collaboration for dissemination of surveillance results Type of disciplines involved in the surveillance process

Factors influencing collaboration

Favouring factors for collaboration Barriers to collaboration Performance of the surveillance system Elements supporting evidence of a good performance of the system Elements supporting evidence of a bad performance of the system Benefits Elements supporting evidence of benefits of collaboration