Integrating epidemiological and economic models to identify the cost of foodborne diseases

Journal Pre-proof Integrating epidemiological and economic models to identify the cost of foodborne diseases

Maurizio Aragrande, Massimo Canali PII:

S0014-4894(19)30414-X

DOI:

https://doi.org/10.1016/j.exppara.2020.107832

Reference:

YEXPR 107832

To appear in:

Experimental Parasitology

Received Date:

19 September 2019

Accepted Date:

06 January 2020

Please cite this article as: Maurizio Aragrande, Massimo Canali, Integrating epidemiological and economic models to identify the cost of foodborne diseases, Experimental Parasitology (2020), https://doi.org/10.1016/j.exppara.2020.107832

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

Journal Pre-proof

Integrating epidemiological and economic models to identify the cost of foodborne diseases Order of Authors: Maurizio Aragrande, Massimo Canali Corresponding author: MaurizioAragrande Affiliation: University of Bologna, Dept. Agricultural and Food Sciences, via G. Fanin 50, Italy Email id: [email protected] Co-authors and affiliations: Dr.Massimo Canali (University of Bologna) Email id: [email protected]

Journal Pre-proof Abstract Despite food technology advancements, food safety policies and alert systems, foodborne diseases are still a relevant concern for consumers and public health authorities, with great impacts on the economy and the society. Evaluating the cost of foodborne diseases may support the design and the implementation of policy interventions. This paper proposes a simple method for cost identification of foodborne diseases, accessible to researchers and practitioners who are not specialist in economics. The method is based on the assumption that epidemiological and economic models can be integrated to understand how the burden of disease determines costs in a wider socio-economic perspective. System thinking and interdisciplinary approach are the pivotal conceptual tools of the method. System thinking allows for the understanding of the complex relationships working among the elementary units of a system (e.g. wildlife, bred animals, consumers, environment, agro-food industry) in the occurrence of a health problem such foodborne diseases. Complex systemic relationships usually cross the traditional boundaries of scientific knowledge (human medicine, veterinary science, economics) and sectoral institutional responsibilities (e.g. ministry of health, ministry of agriculture). For these reasons more scientific disciplines, institutional competences and social bodies need to work together to face complex health problems, in an interdisciplinary framework. The first step of the proposed method is the identification of the potential cost of the disease. To this aim, the authors first focus on the links between epidemiological and economic models, based on the fact that foodborne diseases, likewise other diseases, hit people’s and animals’ aptitude to produce utility and goods for the society (e.g. wellbeing, revenue, safe food). This utility losses are real economic costs. Then they show how simple economic models, such as the food supply chain, can help understand the way costs spread across the economic sectors and the society. It should be underlined that the authors adopt already existing and well rooted scientific tools, focusing in particular that their integration in an interdisciplinary framework can effectively contribute to increase the understanding of complex health problems in a viable way. Graphical abstract

Journal Pre-proof

Journal Pre-proof Keywords: cost categories, foodborne diseases, food supply chain, system thinking, interdisciplinarity 1. Introduction Foodborne diseases (FBDs)1 include a wide range of diseases which affect human beings in different ways and through various transmission mechanisms (Dorny et al., 2009; EFSA, 2018). Depending on the socio-economic context, FBDs spread worldwide taking lower or greater importance (Todd 1988; Van DeVenter, 2000, OECD-WHO, 2003; Kaferstein et al., 2007; WHO 2015; Seimenis and Battelli, 2018). On the one hand, the knowledge of FBD epidemiological models and their costs are key to conceiving the risks associated to FBDs. Together with other criteria, this knowledge can support the formulation of health policy strategies, i.e. by prioritizing interventions on the diseases which show greater social and economic costs (Dewleesschauwer et al., 2017) assuming that the availability of economic resources is limited. On the other hand, due to the complexity of transmission mechanisms, the quantification of the disease burden is still difficult (Flint et al., 2005; Robertson et al., 2018), and this undermines the evaluation. A large stream of scientific production on FBDs’ effects is dedicated to the calculation of the burden of disease (BOD) which relies on the concept of health losses, usually measured through disabilityadjusted or quality-adjusted life years (respectively DALYs and QALYs). These are non-monetary indicators of individual health consequences that can be aggregated at higher levels (e.g. population layers, social categories, geographical context) with relevant advantages in epidemic studies (WHO, 2015). The above-mentioned methods reflect a sectoral approach to the evaluation as they focus on health consequences and do not consider the complexity of FBD-induced effects in the wider socio-economical context. Instead, integrated approaches to health such One Health (OH), EcoHealth, Planetary Health (Lerner and Berg, 2017; Kock et al., 2018) try to evaluate how the effects of diseases spread across that context by considering human, animal and environmental health as the result of multiple drivers within complex systems. These approaches also concern FBDs which may have far reaching effects in the economy and the society. With reference to the economic evaluation, two main problems arise: - how to identify the losses generated by the diseases in a social context; - how to monetize those losses. Monetary approaches provide means to solve those problems. Cost-of-illness (COI) is specific to health cost evaluation and assesses the economic burden imposed by illness on society as a whole in terms of the consumption of health care resources (direct costs) and production losses (indirect costs) (Hodgson and Meiners, 1982; Tarricone, 2006; Scharff, 2012, Changik, 2014). Social Cost Benefit Analysis evaluates the costs and benefits of interventions and assesses which domains of society and stakeholders are involved (Suijkerbuijk et al., 2017; Robertson et al., 2018). Willingness to pay (WTP) is a general method to assess the value of no-market goods which may appear in socially oriented evaluation. In relation to BOD, WTP bypasses the problem of disability monetization by assessing individual propensity to invest money to avoid adverse health outcomes (Roberts, 2007; EFTEC, 2017). The abovementioned approaches go beyond the mere disability quantification and extend the evaluation across sectors (health care system, production systems, economic sectors and society). Abbreviations and acronyms employed throughout this study - BOD: burden of disease; CBA: cost benefit analysis; COI: cost of illness; COST: Cooperation for Science and Technology; DALY: disability-adjusted life year; EFSA: European Food Safety Authority; EFTEC: Economics for the Environment; EU: European Union; FAO: Food and Agriculture Organization; FBD(s): foodborne disease(s); FBP(s): foodborne parasites; FSCh(s): food supply chain(s); NEOH: Network for the Evaluation of One Health; OECD: Organization for Economic Co-operation and Development; OH: One Health; OIE: (Office International des Epizooties) World Organization for Animal Health ; QALY: quality-adjusted life year; WHO: World Health Organizations; WTP: willingness to pay 1

1

Journal Pre-proof At the empirical level, costs arising from FBDs are manifold. Often they are categorized following certain classification criteria, that may change according to the context (e.g. in animal diseases evaluation, production losses are classified as direct costs, while the loss of animal weight or the reduction of fertility are classified as indirect costs; in the food supply chain (FSCh) costs may be distinguished according to the actors who bear them: compliance costs are specific to producers to comply with food safety rules; while household costs are born by consumers because of the disease) (Carabin et al., 2005; Gadiel 2010; Jansen et al., 2018). Extensive meta-analysis of the scientific literature also provides empirical information about the types of costs that researchers include in their evaluations (Buzby and Roberts, 2009; Belaya et al., 2012; McLinden et al., 2014). Moreover, some studies claim the importance of considering the transnational perspectives, the cost of product recall for distributors, sales reduction following food alerts, cost of compliance, costs due to outbreak control and costs due to restrictions in trade, etc. (OECD-WHO, 2003; Kaferstein, 2007; Ribera et al., 2012; Hussain, 2013). A review of evaluation methods, including their pros and cons or their implementation limits, is not among the aims of this paper. Our main concern here is to stress the fact that, usually, the conceptual framework of cost identification is not made explicit in evaluation works, despite being a preliminary step in the evaluation process (Drummond et al., 2015). Cost identification logically foreruns cost quantification, the latter being the most important and expected result of economic evaluation, which captures the greatest attention at the scientific and political levels. Several reasons justify a detailed focus on cost identification and the way it is performed. Zoonoses determine multiple effects and a wide range of costs, which involve the individuals and expand to the society according to trans-sectoral pathways. Managing this complexity requires specific conceptual approaches which can capture the whole range of costs, as far as the current scientific knowledge allows for. In turn, dealing with complexity requires that inter- and trans-disciplinary work routines are developed among the different actors (e.g. institutions, researchers, health practitioners and administrators, social bodies). This approach can eventually lead to a shared framework of expanded knowledge, which can increase the effectiveness of research on health, the social awareness of the costs, and finally support rational decision making process. The EU funded COST Action “Network for the Evaluation of One Health” (NEOH, COST Action TD 1404) developed a method for the evaluation of One Health initiatives during its mandate (20142018) (http://neoh.onehealthglobal.net/). The NEOH approach focuses in particular on the evaluation of One Health (OH) initiatives with a view of assessing the effectiveness of OH approach in comparison with current traditional approaches to health (Rüegg et al, 2018a). Beside this specific aim, NEOH approach combines a set of existing conceptual tools (namely system approach, interand transdisciplinary, theory of change) into a coherent framework. These tools allow researchers to tackle the complexity of health problems in their context. They can be applied to build up a framework for the identification of FBDs cost reflecting the complexity of FBD effects across the society and following a cross-sectoral systemic view in line with OH concept. The aim of this paper is to propose elements and suggestions about the construction of a general method to identify FBDs costs. In this paper, we use already existing methods and concepts, such as system thinking and interdisciplinarity, epidemiological and economic models, in order to carry out an exercise aimed at a viable identification of FBDs effects and costs in a wide socio-economic context. A key step in this direction is to show how different disciplines, namely epidemiology and economics, can work together by integrating epidemiological models and economic models. In what follows we will briefly recall the basic background concepts of the exercise (§ 2); then we will explain how they can work together by carrying out an exercise of model integration concerning a specific

2

Journal Pre-proof FBD (§ 3); finally we will draw conclusions and make some remarks about the utility of the effectiveness of the proposed method. 2. Conceptual background The conceptual background of this exercise is based on system thinking and inter-/transdisciplinarity, and the meaning of epidemiological and economic models, the latter focusing in particular on the FSCh model. 2.1.

System thinking and inter-/trans-disciplinarity

System thinking and inter-disciplinarity are used to solve complex problems, where traditional approaches based on linear causation, mono- or multi-disciplinarity, fail or show limitations in problem understanding and solving. System thinking covers a wide range of concepts and theories (Hofkirchner and Schafranek, 2011). Adopting a system view implies that a problem is examined as part of a wider context, where it represents an element connected with other elements by complex and dynamic relationships. This approach is increasingly applied to health-related issues and policies (de Savigny and Taghreed, 2009; Anderson, 2016; Hitziger et al, 2018). According to Meadows (2008), a system is “A set of elements or parts that is coherently organized and interconnected in a pattern or structure that produces a characteristic set of behaviors, often classified as its function or purpose.”. This definition suggests that a system can be articulated in units of different nature, which are connected to each other through different kinds of interactions (direct and indirect causation, feedbacks or loops, of different sign and intensity). Sub-systems may be identified within a wider system. A sub-system shows a relative homogeneity in relation to the effect it produces or the role it plays in the general system. With reference to FBDs, relevant sub-systems from a biological or epidemiological point of view can be identified in parasites, animals, plants, and human population. From an economic point of view, a relevant subsystem is the FSCh of a product -which can be further distinguished according to producers, processors, distributors; consumers and health care system. This being said, it should be underlined that the identification of a system is somewhat subjective, and depends from the observer’s point of view or aim (actually, biologists, economists or environment experts may observe the same phenomena with different eyes, outlining different relevant units, sub-systems, relationships). A relevant step in the definition of a system is the identification of system limits. One should indeed pose the question: to what extent should the identification of the relationships in a system be pushed? what parts of a system are relevant and what are not to solve a health problem? Limits are subjective as they depend from the observer’s interpretation of the reality. They may arise from scientific criteria or practical considerations (e.g. the financial availability for the development of a research project or for the implementation of health measures; in a study of foodborne parasites (FBPs), parasites transmitted by plants may be given less importance than those found in meat because of consumers’ habits). Inter- and trans-disciplinarity are functional to system thinking, as system complexity crosses the boundaries of scientific disciplines, sectors or institutional competencies. Differently from monoand even multi-disciplinarity, inter-disciplinarity “involves the integration of perspectives, concepts, theories, and methods to address a common challenge” (Rüegg et al., 2018b), while transdisplinarity goes beyond the boundaries of academic knowledge by involving institutions, communities and social parties in building up new knowledge. Implementing inter-/transdisciplinarity needs participative practices and teamwork organization as well as specific methods to elicit stakeholders’ opinion and synthesize across multiple point of views. These quantitative and qualitative methods are well rooted in team or project management and decision support (i.e. 3

Journal Pre-proof stakeholder analysis, multicriteria analysis, Delphi technique, and similar). Moreover, simple tools can be conceived to facilitate interdisciplinary team working in day-by-day routine (Aragrande and Canali, 2015). 2.2.

Epidemiological models

According to Hethcote (1989) “an epidemiological model uses a microscopic description (the role of an infectious individual) to predict the macroscopic behavior of disease spread through a population”. According to Keeling and Ames (2007) a disease model basically “describes the number of individuals (or proportion of the population) that are susceptible to, infected with and recovered from a particular diseases”, making reference to the “foundations of almost all of mathematical epidemiology: the susceptible-infectious-recovered (SIR) model.”. Though an equivalent definition does not exist in the field of veterinary medicine, in this context an epidemiological model has been defined as a “mathematical and/or logical representation of the epidemiology of disease transmission and its associated processes … among animals, and/or among groups of animals, in time and/or space” (Willeberg et al, 2011). Because of their quantitative nature, epidemiological models are well suited for simulation, to find effective application in the management of health crises and to test the effectiveness of possible intervention strategies (Dubé et al, 2007), given that “Experiments with infectious disease spread in human populations are often impossible, unethical or expensive” (Hethcote, 1989). The above-mentioned definitions allow for the assumption that an epidemiological model can be considered itself a system , where units and subsystems of different nature and dimension show specific behaviours (of biological or social nature) and interact according to complex relationships (transmission mechanisms), determining effects at different levels and in different contexts (human society, animals, the environment, the economic sectors). Epidemiological models are often graphically described, as shown in Figure 1, not only as an alternative form of communication but also as a tool to improve understanding, following a common practice in epidemiology (Joffe et al, 2012; EFSA, 2018; Zinsstag et al., 2015) and in many applications of the system theory (Anderson and Johnson, 1997; Meadows, 2008)

4

Journal Pre-proof

Figure 1 - Elementary epidemiological model of Echinococcus Granulosus The figure is a simplified and more general representation of the epidemiologic model of Echinococcus granulosus. The white boxes are the intermediate and final hosts, the blue arrows represent the ways of transmissions. In this case, bi-directional arrows indicate the possibility that livestock and dogs infect each other through feces or slaughter offal (adapted from EFTEC, 2017)

2.3.

Economic models

An economic model is a conceptual tool to represent economic phenomena, i.e. how economic variables interact to produce economic effects. A Cartesian diagram of the market, in which demand and supply functions are represented in relation to price and quantity, well represents the basic idea of an economic model. In its simplicity, this model (as well as many others in economics) can explain and predicts the result of economic behaviours (e.g. consumer and producer behaviours reacting to price variation) assuming that resources (i.e. anything can be used to create some kind of utility for the individuals and the society) are scarce. Resource scarcity implies that a rational choice is needed to allocate resources to alternative uses to get the maximum utility; and that when a resource is allocated to a use, the utility it could produce in an alternative use is lost (the so-called opportunity cost, which is at the core of the common concept of cost in economics) (Canali et al, 2018). When resources are destroyed or their potential to create utility is limited, resource efficiency (i.e. the amount of utility a resource can create) is lost; this loss is a cost for both individuals and the society. In economic terms, diseases are events which reduce resources efficiency (of human beings and animals) to some extent. In other words, pathologies alter the health status of humans and animals and affect their efficiency in the creation of economic utility, causing welfare losses, or costs that are then object of economic evaluations. By the same token, human actions to prevent, contain or eliminate diseases also imply a decision about the allocation of limited (usually monetary) resources, determining private and public costs. The economic evaluation assesses the cost of the diseases and 5

Journal Pre-proof can support the decisions making of individuals as well as entrepreneurs and public administration about the health measures to undertake to reduce the impact of diseases. Efficiency losses of resources can be assumed as the functional and conceptual link between epidemiology and human and animal health economics (Howe, 1988). A wide list of costs can be identified depending on the type of pathogen, the transmission mechanisms and the social and economic behaviour: e.g. costs of medical and hospital cares, hours of work lost by affected humans and their relatives involved in patient care, costs of veterinary treatment for companion and farm animals and related additional work for farmers, livestock losses and related costs for carcass disposal, losses of values along the FSCh, for example due to food alert (Aragrande and Canali, 2017), animal welfare losses (Vetter et al., 2014; Gibson and Jackson, 2017), costs of disease monitoring and surveillance. As mentioned above, apparently cost listing is made without making explicit the underlying conceptual framework, a tool that could provide health operators (researchers of different academic domains, health administrators, health institutions) with general guidelines to identify diseases consequences and cost before (or independently) from the quantification. While quantification requires disciplinary and technical competences, the mere identification of consequences would benefit from the close cooperation among different disciplines in a comprehensive inter-disciplinary framework. The identification of the economic consequences of any disease may be operated by integrating the epidemiology of the disease (the epidemiological model) and the economic functions of the impacted units (the economic model). Among the many economic models, the FSCh model may prove effective for the aim of this paper. The FSCh identifies a series of technical steps leading from raw material to product(s) across economic sectors (agriculture, processing, distribution). FSCh graphical representation is usually linear but it can also include side complementary activities of the production system, which develop around a product. The FSCh model in agro-food economics (Malassis and Ghersi, 1996) and its more recent applications (Temple et al, 2013) focus on the complex relationships occurring in the FSCh. These relationships determine its structure, functioning and socio-economic performances (i.e. production capacity, competitiveness, distributional effects, job creation, etc.). In this sense, a FSCh (Figure 2) may be seen as a sub-system, nested into the wider context (the agro-food system, the socio-economic system), made of actors controlling technological units (the blue boxes, corresponding to economic functions), and linked to each other by commercial flows of goods (straight arrows). Here, some actors may influence the behaviour of other actors by way of economic, normative and social relationships (dashed lines). This model also allows for an interdisciplinary perspective of the analysis, making FSCh a very flexible tool apt to systemic and interdisciplinary contamination. These features may provide useful elements to understand disease transmission and to identify consequences in a wider context, especially in the case of FBPs and FBDs. In the next section, we will develop an exercise to show how this can be achieved.

Figure 2 – Elementary representation of a food supply chain (FSCh) 6

Journal Pre-proof The figure represents an elementary FSCh. Sectors are the usual partitions of the economic system (e.g. agriculture, food processing, food distribution services). The FSCh concept gathers the activities of each sector which contribute to food production and the consumers (see the dashed line box), outlining possible feedback and relationships among sectors (dashed arrows) beyond the typical product flow regulated by the market (full arrows). This image representing economic relationship is in line with the system approach. 3. Result In this section, we propose an exercise of cost identification based on the conceptual tools described in the former section. We refer to the case E. granulosus. described in Figure 1 and we apply simple reasoning to show how epidemiological and economic models can work together. For the sake of exemplification, we also take some freedom or adopt some simplified assumptions in the identification of the possible scenarios determined by the parasite. The starting point of this exercise may be subjective, determined by the individual understanding or by the observer’s disciplinary background. Bearing Figure 1 in mind and considering that economics deals with the use of resources, a good starting point is the identification of resources, namely human beings, production and companion animals, fresh produce. The specific health outcomes of E. granulosus suggest that, to some exent, human beings may lose their ability to work which means they may lose working days and revenue, and they may bear private health care cost to re-establish from the disease. Depending on the health care system, public healthcare costs may also increase. E. granulosus in livestock may result in production losses (in this case, liver or carcasses condemnation) which ends in reduced meat sales and revenue losses for the farmers. Fresh produce may transmit E. granulosus to humans via ingestion of contaminated materials like salads, fruits and even fruit juice (EFSA, 2018) not subject to heat treatment. Contaminated fresh produce entering the food chain (increasing the exposition and the risk for consumers) does not generate immediate losses for the farmers. Pets generally do not show symptoms of E. granulosus and are not meant to produce marketable goods (just utilities for their owners). Assuming, as a mere fictional scenario, that animals suffer some health consequence, their owners might be willing to spend money for care, thus increasing private health care cost (Figure 3).

7

Journal Pre-proof

Figure 3 – Economic outcomes of Echinococcus Granulosus. In this figure we consider that the basic biological units of the epidemiological model (or system) represented in Figure 1 are economic resources. As such, they have an economic value and a role for the economy (or in the economic system) which is altered by the disease. This directly translates into productivity losses or into expenses for re-establishing the original productivity of the resource, as signalled by the dashed arrows. We can further elaborate on Figure 3 by enlarging the range of relationships from the individual or sector perspective (the micro-economic and sectoral dimension of the economic system, represented by private consumers, agricultural sector, health sector) to the wider inter-sectoral and macro-economic dimension (i.e. identifying the relationship between the sub-system and other subsystem and the wider system). By means of example, should health consequences of E. granulosus reach relevant epidemic dimensions and/or its damages acquire a sectoral dimension, macroeconomic effects could become important: reduction of tax revenue, public budget limit, loss of competitiveness, foreign trade balance deficit should also be considered. This is a fictional scenario and does not pertain to E. granulosus epidemiology, but it is something that happens quite often during disease outbreaks of major relevance when food alert or food scare occur. On such occasions, consumers’ behaviour may change dramatically determining backward consequences along the FSCh with unexpected distributional effects in the economy and the society (Elci, 2006; James, 2006; Otte et al., 2006; FAO, 2016; Ramos et al, 2016;). The above-illustrated process shows how relevant units of an epidemiologic model (or system) involved in the transmission mechanism meet units and categories of the economic system allowing for a first identification of units or actors who suffer economic losses, and the kind of cost they may incur. A further structuration of the exercise on cost identification can be performed adopting the FSCh model and its expansion. Figure 4 summarizes the result of said exercise.

8

Journal Pre-proof

Figure 4 - Cost identification of FBDs based on FSCh model This figure builds up on Figures 2 and 3 diagrams to identify the far-reaching effects of FBDs across sectors, and the associated costs born by farms and firms of the supply chain, and by the economic system. The elements of the epidemiological models are outlined in relation to production technology and consumers’ habits and practices. Grey boxes represent the economic activities suffering losses because of the disease outside the FSCh: production losses and reduced market access for firms may end in reduced tax income and public finance imbalances, as well as in loss of competitiveness and foreign trade imbalance (the grey boxes in the dashed frame stand for the macro-economic costs). On the right side of the figure, we highlight the consequences of the disease on human beings. The BOD (which should be corrected for the under-reporting), increases because of the disease. Deaths might also increase. Disability or reduced ability to work (measured by QALY and DALY) results in work productivity and individual revenue losses (as well as increased fatality does), contributing to lower tax income. At the same time, increased patient healthcare costs reduce the revenue available for other expenses (here considered as revenue losses). Costs of the public health care sector may grow because of the increased number of patients, in turn contributing to public finance imbalances. FBP models show how parasites arising somewhere in human activities may affect different types of food, eventually, reaching human beings. Following the FSCh steps, parasites pass through the processing industry and the food distribution system and arrive at the consumer level. From a FSCh perspective, meat and fresh produce depict various cases because of the different processing technologies they undergo and the different consumption habits of consumers (salads are washed but not cooked; industrial washing systems may not be effective on the parasites, depending on the technology and the biosecurity practices, etc.). Furthermore, not only consumers but also FSCh workers might be exposed to disease risk. Regulation on controls along the FSCh differs between the two FSCh (meat and vegetables). The above-mentioned situations indicate that the same 9

Journal Pre-proof parasite can create different distributional effects of economic relevance, i.e. who bear what kind of cost along and inside the two FSChs. The lower side of the diagram highlights the effects occurring out of the supply chain sectors. Depending on the effects of the diseases on production animals, both farmers and the meat processing industry (e.g. slaughterhouses) may suffer production and productivity losses which determine lower market access and profit losses. Fresh produce processors may be less exposed to these consequences in the short run, but they may nevertheless suffer from the same effects later on, in case of a reduction in demand following a food scare. This consideration suggests another perspective for cost identification as distributional effects and related costs may change over time (immediate, short, medium or long period) and in relation to the reaction of FSCh actors to food scare. Side-effects of a shortening of domestic production can lead to supplier substitution (i.e. food processors and food distributors might change food supplier) and/or product substitution (in case of food alert, consumers might decide to demand alternative products category to satisfy their needs of meat or fresh produce). Paradoxically, this can bring positive effects for competing producers or for economic sectors producing substitute products. Through different paths, the competitiveness of the FSCh would be reduced at some geographical dimension. Depending on the epidemiological dimension of foodborne disease, typical macro-economic variables could be affected: lower domestic production translates into higher import to satisfy the domestic demand (foreign trade imbalance), while, at the same time, increased public health costs may call for more public funding to the detriment of alternative public expenditures. Given the usual dimension of FBDs, the latter scenario is to be considered merely fictional, but local effects might be nonetheless relevant. Finally, economic effects also have a spatial dimension. 4. Concluding remarks The identification of disease effects is a relevant preliminary step of the evaluation, which usually is not revealed or focused upon in evaluation works. In this paper, we put forward the idea that a conceptual framework for the identification of FBD effects and cost would benefit different categories of professionals in this domain. Consequently, we provided some reflections and suggestions on how to support our point of view. In particular, we tried to show that interdisciplinary work and system thinking can be easily implemented by integrating already existing tools, such epidemiological models, and simple well-rooted economic models. In our opinion, the use of the concept of the FSCh can help accomplish this integration. Implicitly or explicitly, FSCh is used and largely referred to by scientists and researchers from different disciplinary domains. In our exercise, we showed how it can be included in a comprehensive conceptual framework to assess FBD far-reaching effects and related costs. Food consumption is the concluding step of various activities occurring at different points in time and space, across different biological, technological and socio-economic systems. This makes it difficult to understand the complexities of FBPs and FBDs for both epidemiology and economics when an evaluation task must be performed. Using the FSCh model as analytical unit for the evaluation of FBD effects may facilitate the task for scientists, administrators, and practitioners, who are not specialists in economics, but would like to embed their specific knowledge in a wider context. From an operational perspective, the exercise we developed led to the identification of the distributional effects of an FBD in different directions (i.e. sub-systems). We discussed about the way FBD may affect consumers, in relation to: 1) consumption habits and food handling practices; 2) economic sectors (agricultural production, industrial processing, food distribution), depending on 10

Journal Pre-proof the production systems and the processing technologies; and 3) social categories, depending on their position and role along the FSCh (consumers of different type of food, and FSCh workers). Other considerations concerned the way well-identified units, which are relevant from an epidemiological perspective (i.e. animals, fresh produce, meat), can initiate different series of economic effects depending on food practices, technologies and regulations. Finally, we suggested that disease effects and costs can be distributed along a timeline (by including possible reactions of consumers and producers to food alert), the space (potential effects of supplier substitution in geographically defined markets) and within the food supply system (product substitution that could paradoxically benefit producers of alternative products during a food scare). Thinking in system is a key step to integrate economic and epidemiological models and to the construction of a conceptual framework for cost identification. This can be achieved by assessing the economic roles of those units (e.g. animals, agricultural products, workers, consumers, food) which also play a relevant function in the mechanism of disease transmission. In this sense, FSCh may play a pivotal role because, as mentioned above, it is a widespread concept among scientists and practitioners, it allows for the identification of a wide range of distributional effects across economic sectors and social activities, both in the linear (from agriculture to consumption as described in Figure 2) and lateral directions (connecting side activities to the main FSCh, as described in Figure 4), as well as across space (various geographical dimensions of the market, from local to transnational markets) and time (e.g. possible feedback of change in consumer behaviour upward the supply chain in relation to food alerts). Furthermore, system thinking is closely linked to inter- and trans-disciplinarity, and FSCh is suitable to interdisciplinary contamination. Both system thinking and inter-/trans-disciplinarity may require a shift in the minds of researchers and practitioners. These approaches entail powerful epistemological background and justification. On the other hand, they are useful if they can be applied in the day-by-day work developed inside research and health institutions. Trying to translate in practice the theoretical principles and integrating conceptual models to elaborate viable working tools is a challenge which involves the daily practice at any level of operation and mainly concerns team working, which is probably the frontline of interdisciplinarity. This requires some investment (usually in terms of time and organization) that should be evaluated in a cost-benefit perspective, i.e. to what extent the above-mentioned investment can improve problem solving? In our opinion, there are ways to minimize the cost of this innovation with the tools we mentioned above and with simple interdisciplinary management tools, such the interdisciplinary matrix (Lerner and Berg, 2015; Aragrande and Canali, 2015). These tools, which can be easily implemented and handled, allow for different disciplines to communicate to each other beyond so called disciplinary silos, finally finding synergies in problem solving. A final consideration concerns a critical aspect of the proposed method, i.e. the lack of data that may lead to failure in practice. The application of system thinking and interdisciplinarity to FBPs and FBDs research goes together with the need of wider and deeper knowledge (i.e. more disciplines involved and more data needed from each discipline’s domain): enlarging the scope of the evaluation can paradoxically make the lack of data more evident than other traditional approaches. Some data are difficult to obtain, especially if they need to be collected at the crossroads of different disciplines (e.g. studies on consumer behaviour -seldom determined by emotional drivers- in relation to food health attributes credence, or fake news). Interdisciplinary studies require complex management and operation and they can progress if researchers share their conceptual models and are open to learn from each other.

11

Journal Pre-proof Box 1 – Terminology Burden of disease

Burden of disease describes death and loss of health due to diseases, injuries and risk factors for all regions of the world. The burden of a particular disease or condition is estimated by adding together: (i) the number of years of life a person loses as a consequence of dying early because of the disease (Years of Life Lost or YLL); and (ii) the number of years of life a person lives with disability caused by the disease (called, Years of Life lived with Disability, or YLD). Adding together (i) and (ii) gives a single-figure estimate of disease burden, called the Disability Adjusted Life Year, or DALY. One DALY represents the loss of one year of life lived in full health (Adapted from: https://www.who.int/foodsafety/foodborne_disease/Q&A.pdf)

Cost

Cost is the amount of money we renounce to get something else (see also under opportunity cost). To produce goods (outputs), we renounce to an amount of money to purchase material resources and services (inputs). Production theories distinguishes among variable costs (the monetary value of variable inputs such work, energy, consumables) and fixed costs (the monetary value of fixed inputs such investments, facilities, durable equipment). By extension, losses of production or productivity, loss of income or salary are considered cost as well (indirect costs). In this sense, diseases determine private and public health costs (e.g. drugs for patients; the use of public health facilities), and indirect costs for the patients and the community (loss of working days; production losses for a sector or the society).

Cost of illness (COI)

Cost of illness estimates direct costs (medical care, travel costs, etc) and indirect costs (value of lost production from lower labour inputs) due to disease and injury (WHO, 2009)

Disability Adjusted Life Year (DALY)

DALY are the number of years of healthy life lost because a disease or injury. The sum of DALYs across the population can be seen as a measurement of the gap between current health status and an ideal health situation where the entire population lives to an advanced age, free of disease and disability (https://www.who.int/healthinfo/global_burden_disease/metrics_daly/en/)

Economic model

An economic model is a simplified representation of how economic variables determine economic phenomena. Economic models are conceptual constructs which adopt functions and mathematic means, to various degree of complexity, to test economic theories and verify empirical result at microeconomic, sectoral and macroeconomic level (see definitions below). Mathematical models have a wide range of application. For example, macro-economic models are often used to simulate the result of economic policy and support political decision.

Economic sector

In Economics, a sector is a partition of the economic system gathering production activities with similar characteristics. The typical sectoral partition adopted in national accounting systems distinguishes among primary sector (based on the use of natural resources: agriculture, forestry, fishing), industrial sector (industries processing goods from the primary sector or from primary industrial processors), services (activities which do not produce material goods but serve the aim of the production, e.g. transportation, distribution of goods, banking, consultancy, public administration, etc.). Based on the same criterion of similarity, other partitions of the economic system are often used (e.g. private sector and public sector; non-profit sector; health sector) to serve specific analytical needs.

Food supply chain

The series of technological operations which allow for the transformation of raw agricultural materials in final products (food and feed). Those operations are implemented by elementary production units (firms, farms) belonging to economic sectors (agriculture, industrial processing, food distribution) linked each other by market and/or other regulatory means.

Interdisciplinarity

The interdisciplinary approach involves the integration of perspectives, theories and methods to address a common challenge (Rüegg et al, 2018b)

Macro-economics

Macro-economics is a branch of Economics which studies the economic system as a whole. Monetary problems and inflation, economic growth and development, taxation and distributional effects, employment, international trade and the performance of the economic system (measured for instance by the Gross Domestic Product) are typical problems analysed by this branch of the Economics.

Micro-economics

Micro-economics is a branch of Economics which studies the behaviours of consumers and producers (i.e. firms) in the allocation of scarce resources, in view of optimizing their utility or revenue; and the behaviour of markets where consumers and producers meet to exchange 12

Journal Pre-proof goods. Theory of demand, Production theory, price formation and market models are typical fields of investigation of micro-economics One Health

One Health is an approach to designing and implementing programmes, policies, legislation and research in which multiple sectors communicate and work together to achieve better public health outcomes. The areas of work in which a One Health approach is particularly relevant include food safety, the control of zoonoses (diseases that can spread between animals and humans, such as flu, rabies and Rift Valley Fever), and combatting antibiotic resistance (when bacteria change after being exposed to antibiotics and become more difficult to treat). (https://www.who.int/features/qa/one-health/en/)

Opportunity cost

The opportunity cost is the value of a good that we renounce when we allocate a resource (e.g. money, time, input) in an alternative use. For example, in a system perspective, an investment in the public sector implies reduction of resources available for private investments (Canali et al, 2018). In the example the amount of private investments renounced is the opportunity cost of the investments in the public sector.

QALY

A measure of the state of health of a person or group in which the benefits, in terms of length of life, are adjusted to reflect the quality of life. One QALY is equal to 1 year of life in perfect health (Rüegg et al, 2018b).

Social cost benefits analysis

A social cost benefit analysis is a method to survey all the impacts caused by an activity (project, policy measure). It comprises not just the financial effects but all the societal effects (e.g. pollution, environment, safety, travel, health, market impacts, etc.). The aim of a social cost benefit analysis is to attach a price to as many effects as possible. Those prices reflect the value a society attaches to the effects, enabling the decision maker to form an opinion about the net social welfare effects of a project. (Adapted from: https://decisio.nl/en/research/social-costbenefit-analysis/)

Transdisciplinarity

Transdisciplinary approach refers to scholarship that transgresses the boundaries between academia and community outside academia. It enables inputs and scoping across scientific and non-scientific stakeholders’ communities (Rüegg et al, 2018a)

Willingness to pay

Willingness to pay is a method to assess the value that people (consumers, patients) associate to goods that have no market (e.g. environmental services, pain or sufferance in patients, disability due to diseases or injuries), so revealing their social utility. The ambition of the method is to put a price on that goods, that would otherwise difficult or impossible to evaluate.

Acknowledgments: no acknowledgments to be declared Funding: This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sector. References 1. Anderson B.R., 2016. Improving health care by embracing Systems Theory. J. Thorac. Cardiovas.c Surg. 152:593–4. http://dx.doi.org/10.1016/j.jtcvs.2016.03.029 (Accessed Sept 15, 2019 at: https://www.jtcvs.org/article/S0022-5223(16)30001-0/pdf) 2. Anderson V., Johnson L.,1997. Systems thinking basics : from concepts to causal loops. Pegasus Communications, Cambridge ISBN: 1-883823-12-9 3. Aragrande M, Canali M., 2017. Animal health and price transmission along livestock supply chains. OIE Rev Sci Tec 2017 36:87–96. doi: 10.20506/rst.36.1.2612 (Accessed Sept 15, 2019 at: http://boutique.oie.int/extrait/08aragrande8796.pdf) 4. Aragrande M., Canali M., 2015. An operational tool to enhance One Health Interdisciplinarity, in: “One Health, One Planet, One Future. Fostering interdisciplinary collaboration for global public and animal health” 3rd GRF One Health Summit 2015, 04-06 October 2015, Davos “Switzerland. Extended abstract & Poster Collection, pp 11-16 (Accessed Sept 15, 2019 at: 13

Journal Pre-proof http://onehealth.grforum.org/fileadmin/user_upload/one_health/programme/2015_Extende d_Abstracts.pdf) 5. Belaya V., Hansen H., Pinior B. , 2012. Measuring The Costs Of Foodborne Diseases: A Review And Classification Of The Literature. 52nd Annual Conference, Stuttgart, Germany, September 26-28, 2012, 138195, German Association of Agricultural Economists (GEWISOLA). doi: 10.22004/ag.econ.138195 (Accessed Sept 15, 2019 at: https://ideas.repec.org/p/ags/gewi12/138195.html) 6. Buzby J.C., Roberts T., 2009. The Economics of Enteric Infections: Human Foodborne Disease Costs. Gastroentetology 2009;136:1851–1862. https://doi.org/10.1053/j.gastro.2009.01.074 (Accessed Sept 15, 2019 at: https://www.gastrojournal.org/article/S0016-5085(09)003412/fulltext) 7. Canali M., Aragrande M., Cuevas S., Cornelsen L., Mihegan B., Rojo-Gimeno C., Häsler B., 2018. Chapter 6. The economic evaluation of One Health, in: Rüegg S.R., Häsler B., Zinsstag J. Integrated approaches to health. A handbook for the evaluation of One Health. Wageningen Academic Publishers, Wageningen, pp. 170-226. doi: 10.3920/978-90-8686-875-9 8. Carabin H., Budke C.M., Cowan L.D., Willingham III A.L., Torgerson P.R., 2005. Methods for assessing the burden of parasitic zoonoses: echinococcosis and cysticercosis. Trends Parasitol., Vol. 21, Issue 7, July 2005, Pages 327-333. https://doi.org/10.1016/j.pt.2005.05.009 (Accessed Sept 15, 2019 at: https://www.sciencedirect.com/science/article/pii/S147149220500125X) 9. Changik J., 2014. Cost-of-illness studies: concepts, scopes, and methods. Clin Mol Hepathol., 2014, 20:327-337. doi: 10.3350/cmh.2014.20.4.327 (Accessed Sept 15, 2019 at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4278062/) 10. de Savigny D, Taghreed A., 2009. Systems Thinking for Health Systems Strengthening (Eds.) Genève. ISBN: 978 92 4 156389 5 (Accessed Sept 15, 2019 at: https://www.who.int/alliancehpsr/resources/9789241563895/en/) 11. Devleesschauwer B., Bouwknegt M., Dorny P., Gabriële S., Havelaar A.H., Quoilina S., Robertson L.J., Speybroeck N., Torgerson P.R., van der Giessenj J.W.B., Trevisan C., 2017. Risk ranking of foodborne parasites: State of the art. Food and Waterborne Parasitol. 8–9 (2017) 1– 13,. https://doi.org/10.1016/j.fawpar.2017.11.001 (Accessed Sept 15, 2019 at: https://www.sciencedirect.com/science/article/pii/S2405676617300124) 12. Dorny P., Praet N., Deckers N., Gabriel S., 2009. Emerging food-borne parasites. Vet Parasitol. 163 (2009) 196–206. doi:10.1016/j.vetpar.2009.05.026. (Accessed Sept 15, 2019 at: https://www.ncbi.nlm.nih.gov/pubmed/19559535) 13. Drummond M.F., Sculpher M.J., Claxton K., Stoddart G.L., Torrance G.W., 2015. Methods for the economic evaluation of health care programmes. Oxford University Press, Oxford. ISBN: 978-0-19-966587-7 14. Dubé C., Garner G., Stevenson M., Sanson R., Estrada C., Willeberg P., 2007. The use of epidemiological models for the management of animal disease. Conf. OIE 2007, pp 13-23 (Accessed Sept 15, 2019 at: https://pdfs.semanticscholar.org/ea0e/618c7d64abd49be49f9ac9d20e7c31a28988.pdf) 15. EFSA, 2018. Scientific Opinion on the public health risks associated with food-borne parasites. EFSA Panel on Biological Hazards. EFSA Journal 2018;16(12):5495, pp.113. doi: 14

Journal Pre-proof 10.2903/j.efsa.2018.5495 (Accessed Sept 15, 2019 at: https://www.efsa.europa.eu/en/efsajournal/pub/5495) 16. EFTEC, 2017. Estimating Quality Adjusted Life Years and Willingness to Pay Values for Microbiological Foodborne Disease (Phase 2). Final report. EFTEC, London March 2017, pp x+197 (Accessed Sept 15, 2019 at: https://www.foodstandards.gov.scot/downloads/Estimating_Quality_Adjusted_Life_Years_an d_Willingness_to_Pay_Values_for_Microbiological_Foodbrone_Disease_Phase_2.pdf) 17. Elci C., 2006. The Impact of HPAI of the H5N1 Strain Economies of Affected Countries, in: International Conference on Human and Economic Resources Proceedings Book. Izmir University of Economics and SUNY Cortland, pp 101-115 (Accessed Sept 15, 2019 at: https://kutuphane.ieu.edu.tr/wp-content/08IntConfHuman.pdf) 18. FAO, 2016. Economic analysis of animal diseases. FAO-Animal Production and Health Guidelines nr. 18, pp xiv+73 (Accessed Sept 15, 2019 at: http://www.fao.org/3/a-i5512e.pdf) 19. Flint J.A., Van Duynhoven T.Y, Angulo F.J, DeLong S.M., Braun P., Kirk M., Scallan E., Fitzgerald M., Goutam K., Adak G.K., Sockett P., Ellis A., Hall G., Gargouri N., Walke H., Braam P., 2005. Estimating the Burden of Acute Gastroenteritis, Foodborne Disease, and Pathogens Commonly Transmitted by Food: An International Review. Clin Infect Dis. 2005 Sep 1;41(5):698-704. https://doi.org/10.1086/432064 (Accessed Sept 15, 2019 at: https://academic.oup.com/cid/article/41/5/698/330923) 20. Gadiel D., 2010. The economic cost of foodborne disease in New Zealand. Applied Economics November 2010. Applied Economics Pty Ltd, Sydney (Accessed Sept 15, 2019 at: http://www.appliedeconomics.com.au/publications/papers/health/referenceshealth.htm#P3_19) 21. Gibson T.J., Jackson E.L., 2017. The economics of animal welfare. Rev. Sci. Tech. Off. Int. Epiz., 2017, 36 (1), 125-135. doi: 10.20506/rst.36.1.2616 (Accessed Sept 15, 2019 at: http://boutique.oie.int/extrait/12gibson125135.pdf) 22. Hethcote H.W. (1989) Three Basic Epidemiological Models, in: Levin S.A., Hallam T.G., Gross L.J. (Eds) Applied Mathematical Ecology. Biomathematics, vol 18. Berlin-Heidelberg. pp. 119-144. https://doi.org/10.1007/978-3-642-61317-3_5. (Accessed Sept 15, 2019 at: https://link.springer.com/chapter/10.1007/978-3-642-61317-3_5) 23. Hitziger M, Esposito R, Canali M, Aragrande M, Häsler B, Rüegg SR., 2018. Knowledge integration in One Health policy formulation, implementation and evaluation. Bull World Health Organ (2018) 96:211–218. doi:10.2471/BLT.17.202705 (Accessed Sept 15, 2019 at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5840631/) 24. Hodgson T.A., Meiners M.R., 1982. Cost-of-illness Methodology: A guide to Current Practices and Procedures. Milbank Memorial Fund, Quarterly/Health and Society, Vol. 60, No. 3, 1982 (Accessed Dec 2, 2019 at: https://www.milbank.org/wp-content/uploads/mq/volume60/issue-03/60-3-Cost-of-Illness-Methodology.pdf) 25. Hofkirchner W., Schafranek M., 2011. General System Theory. Philos Complex Syst (2011)177– 194. doi:10.1016/B978-0-444-52076-0.50006-7 (Accessed Sept 15, 2019 at: https://www.sciencedirect.com/science/article/pii/B9780444520760500067) 26. Howe KS., 1988. Conceptual affinities in veterinary epidemiology and economics. Acta Vet Scand Suppl (1988) 84:347–9. (Accessed Sept 15, 2019 at: http://www.ncbi.nlm.nih.gov/pubmed/3232635) 15

Journal Pre-proof 27. Hussain M.A., Dawsnon C.O., 2013. Economic Impact of Food Safety Outbreaks on Food Businesses. Foods, 2013, 2, 585-589; doi:10.3390/foods2040585 (Accessed Sept 15, 2019 at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5302274/) 28. James S., Sargent T., 2006. The Economic Impact of an Influenza Pandemic. Department of Finance, Canada, Working Paper, 2007-04 December 12, 2006 (Accessed Sept 15, 2019 at: https://www.fin.gc.ca/pub/pdfs/wp2007-04e.pdf) 29. Jansen F., Dorny P., Trevisan C., Dermauw V., Laranjo-González M., Allepuz A., Dupuy C., Krit M., Gabriël S., Devleesschauwer B., 2018. Economic impact of bovine cysticercosis and taeniosis caused by Taenia saginata in Belgium. Parasites & Vectors (2018) 11:241. https://doi.org/10.1186/s13071-018-2804-x. (Accessed Sept 15, 2019 at: https://parasitesandvectors.biomedcentral.com/articles/10.1186/s13071-018-2804-x) 30. Joffe M, Gambhir M, Chadeau-Hyam M, Vineis P., 2012. Causal diagrams in systems epidemiology. Emerg Themes Epidemiol. (2012) 9:1. doi:10.1186/1742-7622-9-1 (Accessed Sept 15, 2019 at: https://ete-online.biomedcentral.com/articles/10.1186/1742-7622-9-1) 31. Käferstein F.K., Motarjemi Y., D. W. Bettcher D.W., 1997. Foodborne Disease Control: A Transnational Challenge. Emerg Infect Dis., Vol. 3, No. 4, October–December 1997. doi: 10.3201/eid0304.970414 (Accessed Sept 15, 2019 at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2640096/pdf/9368787.pdf) 32. Keeling M.J., Eames K.T.D., 2005. Networks and epidemic models. J. R. Soc. Interface (2005) 2, 295–307, doi:10.1098/rsif.2005.0051. (Accessed Sept 15, 2019 at: https://royalsocietypublishing.org/doi/full/10.1098/rsif.2005.0051) 33. Lerner, H., Berg C., 2015. The concept of health in One Health and some practical implications for research and education: what is One Health? Infect Ecol and Epidemiol. [S.l.], v. 5, feb. 2015 art. Nr 25300. http://dx.doi.org/10.3402/iee.v5.25300 (Accessed Sept 15, 2019 at: https://www.tandfonline.com/doi/full/10.3402/iee.v5.25300) 34. Malassis L., Ghersi G. (1996) Initiation à l'économie agro-alimentaire. Hatier, Paris 1992. ISBN: 2-218-05014-5 35. McLinden T., Sargeant J.M., Thomas M.K., Papadopoulos A., Fazil A., 2014. Component costs of foodborne illness: a scoping review. BMC Public Health 2014, 14:509 (Accessed Sept 15, 2019 at: http://www.biomedcentral.com/1471-2458/14/509) 36. Meadows D.H., 2008. Thinking in system. A primer. Earthscan, London. ISBN: 978-1-84407726-7 37. OECD-WHO, 2003. Foodborne diseases in OECD Countries. Present state and economic cost 2003. OECD, Paris. https://dx.doi.org/10.1787/9789264105386-en (Accessed Sept 15, 2019 at: https://read.oecd-ilibrary.org/agriculture-and-food/foodborne-disease-in-oecdcountries_9789264105386-en#page1) 38. Otte J., Hinrichs J., Rushton J., Roland-Holst D., Zilberman D., 2006. Impacts of avian influenza virus on animal production in developing countries. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources 2008-3, No. 080. doi: 10.1079/PAVSNNR20083080 (Accessed Sept 15, 2019 at: https://pdfs.semanticscholar.org/7004/9c2ec7834c20556d7b777da38e2fd5bf1bc4.pdf?_ga=2. 5707443.1630480662.1568460796-98204385.1568460796)

16

Journal Pre-proof 39. Ramos S., MacLachlan M., Melton A., 2016. Impacts of the 2014-2015 Highly Pathogenic Avian Influenza Outbreak on the U.S. Poultry Sector. USDA-Economic Research Service, LDPM 282-02 December 2017 (Accessed Sept 15, 2019 at: https://www.ers.usda.gov/webdocs/publications/86282/ldpm-282-02.pdf?v=0) 40. Ribera L.A.., Palma M.A., Paggi M., Knutson R, Masabni J.G., Anciso J., 2012. Economic Analysis of Food Safety Compliance Costs and Foodborne Illness Outbreaks in the United States. Hortic Technol., vol 22, issue 2, April 2012, pp. 150-156. https://doi.org/10.21273/HORTTECH.22.2.150 (Accessed Sept 15, 2019 at: https://journals.ashs.org/horttech/view/journals/horttech/22/2/article-p150.xml) 41. Roberts T., 2007. WTP Estimates of the Societal Costs of U.S. Foodborne Illness. Am J Agric Econ., Vol. 89, No. 5, Proceedings Issue (Dec., 2007), pp. 1183-1188. https://doi.org/10.1111/j.1467-8276.2007.01081.x (Accessed Sept 15, 2019 at: https://academic.oup.com/ajae/article-abstract/89/5/1183/117231?redirectedFrom=fulltext) 42. Robertson L. J., Torgerson P.R., van der Giessen J., 2018. Foodborne Parasitic Diseases in Europe: Social Cost-Benefit Analyses of Interventions. Trends Parasitol., November 2018, Vol. 34, No. 11 pp 919-923. https://doi.org/10.1016/j.pt.2018.05.007 (Accessed Sept 15, 2019 at: https://www.sciencedirect.com/science/article/abs/pii/S1471492218301168) 43. Rüegg S.R., Häsler B., Zinsstag J., 2018a Integrated approaches to health. A handbook for the evaluation of One Health. Wageningen Academic Publishers, Wageningen. doi: 10.3920/97890-8686-875-9 44. Rüegg S.R. Rosenbaum Nielsen L., Buttigieg S.C:, Mijalche Santa M., Aragrande M., Canali M., Ehlinger T., Chantziaras I., Boriani B., Radeski M., Bruce M., Queenan K., Häsler H., 2018b. A Systems Approach to Evaluate One Health Initiatives. Front Vet Sci, Feb 2018, 5:23. doi: 10.3389/fvets.2018.00023 (Accessed Sept 15, 2019 at: https://www.frontiersin.org/articles/10.3389/fvets.2018.00023/full) 45. Scharff R.L., 2010. Health-related costs from foodborne illness in the United States. The Produce Safety Project at Georgetown University (www.producesafetyproject.org). https://doi.org/10.1016/S0140-6736(10)60367-4. (Accessed Sept 15, 2019 at: https://www.pewtrusts.org//media/legacy/uploadedfiles/phg/content_level_pages/reports/pspscharff20v9pdf.pdf) 46. Seimenis A., Battelli G., 2018. Main challenges in the control of zoonoses and related foodborne diseases in the South Mediterranean and Middle East region. Veterinaria Itali 2018, 54 (2), 97-106. doi: 10.12834/VetIt.1340.7765.1 (Accessed Sept 15, 2019 at: http://www.izs.it/vet_italiana/2018/54_2/VetIt_1340_7765_1.pdf) 47. Suijkerbuijk A.W.M, van Gils P.F., Bonačić Marinović A.A., Feenstra T.L., Kortbeek L.M., Mangen M.-J.J, Opsteegh M., de Wit G.A., van der Giessen J. W. B., 2018. The design of a Social CostBenefit Analysis of preventive interventions for toxoplasmosis: An example of the One Health approach. Zoonoses Public Health. 2018;65:185–194. doi: 10.1111/zph.12417 (Accessed Sept 15, 2019 at: https://onlinelibrary.wiley.com/doi/epdf/10.1111/zph.12417) 48. Tarricone R., 2006. Cost-of-illness analysis. What room in health economics. Health Policy 77 (2006) 51–63. oi:10.1016/j.healthpol.2005.07.016 (Accessed Dec 3, 2019 at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.515.4983&rep=rep1&type=pdf) 49. Temple T., Lançon F., Palpacuer F., Paché G., 2011. Actualisation du concept de filière dans l’agriculture et l’agroalimentaire. Économies et sociétés, Développement, croissance et 17

Journal Pre-proof progrès - Presses de l’ISMEA - Paris, 2011, AG (33), pp.1785-1797. HAL-00802690 (Accessed Sept 15, 2019 at: https://hal.archives-ouvertes.fr/hal-00802690/document) 50. Todd E.C.D., 1989. Preliminary Estimates of Costs of Foodborne Disease in the United States. J. Food Prot., Vol. 52, No. 8, 595-601 (August 1989) https://doi.org/10.4315/0362-028X-52.8.595 (Accessed Sept 15, 2019 at: https://jfoodprotection.org/doi/pdf/10.4315/0362-028X-52.8.595) 51. Van DeVenter T., 2000. Emerging food-borne diseases: a global responsibility. Food Nutri. Agric. 26, 4-13. ISSN 1014-806X (http://www.fao.org/3/x7133m/x7133m00.htm) 52. Vetter S, Vasa L, Ózsvári L., 2014. Economic Aspects of Animal Welfare. Acta Polytech Hung (2014) vol 11, nr 7, 119-134 : doi: 10.12700/APH.11.07.2014.07.8 (Accessed Sept 15, 2019 at: https://www.uni-obuda.hu/journal/Vetter_Vasa_Ozsvari_53.pdf) 53. WHO, 2009. WHO guide to identifying the economic consequences of diseases and injury. Geneva. ISBN 978 92 4 159829 3 (Accessed Dec 16, 2019 at: https://www.who.int/choice/publications/d_economic_impact_guide.pdf) 54. WHO, 2015. WHO estimates of the global burden of foodborne diseases: foodborne disease burden epidemiology reference group 2007-2015. World Health Organization, Geneva. ISBN: 978-92-4-156516 5 (Accessed Sept 15, 2019 at: https://apps.who.int/iris/bitstream/handle/10665/199350/9789241565165_eng.pdf) 55. Willeberg P., Grubbe T., Weber S., Forde-Folle K., Dubé C., 2011. The World Organisation for Animal Health and epidemiological modelling: background and objectives. Rev. sci. tech. Off. int. Epiz., 2011, 30 (2), 391-405 (Accessed Sept 15, 2019 at: http://web.oie.int/boutique//extrait/02willeberg391405.pdf) 56. Zinsstag J., Schelling, E., Waltner-Toews D., Whittaker M., Tanner M., 2015. One Health. The Theory and Practice of Integrated Health Approches. CABI International, Wallingford-Boston. ISBN-13: 978 1 78064 341 0 Auhtors’ biography Maurizio Aragrande is associate professor at the Bologna University where he teaches Agrofood economics and Agricultural policy (Veterinary Faculty). He also teaches Political economy at the University of Modena, Faculty of law. Research concerns the economics of the agro-food system, animal product supply chains, animal health economics and policy measures evaluation. He collaborated with FAO to built up a system, interdisciplinary approach to study urban food distribution systems in Wester Africa; developed evaluation studies for the European Commission and the European Parliament; and is currently involved in national and international evaluation studies on animal health policies and One Health. Massimo Canali is senior researcher at the University of Bologna, where he teaches agricultural economics and policy, environmental economics and economic geography. His research is mainly concerned with sustainable development of agriculture and food systems by focusing on animal and environmental health economics and One-Health. He has a large experience of international research and development projects and is presently involved in economic evaluations related to the control of animal and zoonotic diseases in Italy and is Work Package Leader in the European H2020-project ROADMAP for a more prudent use of antimicrobials in livestock farming. 18

Journal Pre-proof Conflict of interest statement Concerning the paper: Integrating epidemiological and economic models to identify the cost of food-born diseases The Authors, Maurizio Aragrande and Massimo Canali, confirm that they have no conflict interest to declare

Bologna, September 17, 2019

Maurizio Aragrande Massimo Canali

Journal Pre-proof Authors’ Statement Maurizio Aragrande and Massimo Canali equally contributed to Conceptualization, Methodology and to Writing-Review&Editing. In particular: Maurizio Aragrande was also responsible for Supervision, Validation, Writing-Original Draft; Massimo Canali was also responsible for Investigation, Data curation and Visualization.

Journal Pre-proof Highlights  Identifying food-born diseases cost needs system thinking and interdisciplinarity  Epidemiological and economic models can be integrated for cost identification  Food supply chain model can guide to identify cost across sectors and the society  The proposed approach provides conceptual and practical tools to facilitate system thinking and interdisciplinarity team working  This approach can support interdisciplinary collaboration in research team work