An Operational Tool To Enhance One Health Interdisciplinary Massimo CANALI
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Presentation at 3rd GRF One Health Summit 2015 Session The One Health Approach in Action
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An Operational Tool To Enhance One Health Interdisciplinary Massimo CANALI
- 1. An operational tool to enhance One Health Interdisciplinarity Maurizio Aragrande Massimo Canali This paper is based upon work from COST Action TD1404 âNetwork for Evaluation of One Health (NEOH)â, supported by COST (European Cooperation in Science and Technology). COST is supported by the EU Framework Programme Horizon 2020â 3rd GRF One Health Summit 2015 Fostering interdisciplinary collaboration for global public and animal health Davos, 4-7 October 2015
- 2. Background 2
- 3. One Health and One Health evaluations 3 ⢠The One Health (OH) concept requires the removal of disciplinary barriers for a global understanding of complex health problems. ⢠Similarly, a comprehensive evaluation of OH initiatives (actions, programs, measures, policies, or whatever) involves the analysis of different systems and sub- systems of the social, environmental and socio-economic context through an Interdisciplinary approach ⢠On a methodological point of view, OH evaluations require the same capacity of managing complexity and Interdisciplinariry needed to conceive and implement OH initiatives
- 4. Interdisciplinarity in sciences ⢠Interdisciplinarity (ID) in sciences is usually called into question, when disciplinary or traditional silo approaches fail or show limits. ⢠Available quantitative studies on ID in sciences are based on bibliometric analysis of publications ⢠These studies show that, in general: â ID research is increasing in the long term, but at a very slow rate â There are relevant gaps among the different scientific domains in the production of ID research ⢠In fact, ID research is still the exception and not the rule in sciences
- 5. ID scientific teams ⢠It may seem simplistic to say that ID research has to be developed by ID scientific teams ⢠Within an ID team, scientists from different fields should integrate their different visions and approaches to understand and intervene on complex problems ⢠It is less simple to find out how this could concretely and effectively be done, and run the day-to-day work of an ID scientific team ⢠Working effectively in an ID team requires personal involvement, aptitude and organization. These qualities and capacities need to be shaped and developed ⢠ID team work is the front line of OH evaluation, but ⌠building up ID teams requires investment in human capital 5
- 6. Current status of evaluation 6 Current policy evaluation tools ususally adopt sectoral rather than multi-sectoral perspectives, and often reflect disciplinary vision. They usually miss the link among sectors and with the socio-economic context where added values and costs appear
- 9. Moving toward ID research ⢠The formation of a ID scientific team requires: â Satisfaction of preliminary conditions (researchersâ motivation , personal aptitude, etc.) â Investment in human capital (selection, training, etc.) â Adequate procedures for day-to-day work within the ID scientific team (e.g. information exchange, task distribution, timing, reporting, etc.) 9
- 10. Preliminary conditions ⢠Motivation â Traditional âsiloâ approaches, individual skills and excellence in one discipline are not enough to understand complexity ⢠Personal aptitude â Mind opened to different perspectives â Identify and accept limits and implicit assumptions of individual/disciplinary views â Let the others go through individualsâ view with new ideas and alternative approaches 10
- 11. Investments ⢠Methodology â System thinking â Information sharing â Mutual training among scientists â Participatory approach ⢠Problem modelling â Cross sector models ⢠Human â Animal â Environmental 11
- 12. ID team work tools ⢠Leadership ⢠Scientific skills of participants â Current status of the knowledge about specific problems ⢠ID communication procedures and routine ⢠Flexibility â the ID scientific team expands its scientific skills and competencies to adapt to the complexity of the problems 12
- 13. The strategy 13
- 14. Assumptions ⢠For any scientist, his own discipline almost becomes self evident ⢠Acquiring alternative views may be a problem â Where is my disciplineâs border? â What is my contribution to complexity? â How may other disciplines contribute? ⢠Understanding complexity is a process 14
- 15. From disciplinary approaches to complexity ⢠Complexity can be understood starting from disciplinary competences ⢠Adequate tools must support this process 15
- 16. Proposed tool: Interdisciplinary Matrix 16
- 17. Basic evaluation question ⢠How far an action will produce effects within the society? â Which is(are) the starting point(s)? ⢠Inputs â Which sector(s) is(are) involved? ⢠Outputs â How are they involved? ⢠Causal relationship 17
- 18. Interdisciplinary matrix (IDM) ⢠The ID Matrix is an input/output table â An inputs is a starting point ⢠E.g. a policy measure to address a problem of human or animal health â An output is an effect of the measure ⢠It may belong to the same health sector/dimension or not ⢠Inputs and outputs are the relevant health sectors and aspects â They reflect disciplinary competences â They can be further specified accordingly ⢠The IDM includes the links among health sectors/dimensions â Cross-sector relationship 18
- 19. A tentative ID-Matrix 19 ⢠= Inputs or Starting points +/- = Outputs or Effects (increasing/decreasing) = Relationships (based on animal health measures to fight Brucellosis)
- 20. Possible advantages The ID Matrix: ⢠may guide the process of understanding complex problems starting from simple procedures and tasks ⢠may create ID team competences, by starting from individual competences (disciplinary) ⢠does not require preliminary methodological and theoretical competences of team participants â But strong governance and preliminary work by promoters and process leaders is fundamental 20
- 22. Network for the Evaluation of One Health 22 NEOH (Network for the Evaluation of One Health) brings together people from various disciplines to elaborate a OH evaluation framework Essay in interdisciplinary team work routine http://neoh.onehealthglobal.net/
- 23. Thank you for attention! This paper is based upon work from COST Action TD1404 âNetwork for Evaluation of One Health (NEOH)â, supported by COST (European Cooperation in Science and Technology). COST is supported by the EU Framework Programme Horizon 2020â
Hinweis der Redaktion
- The background of this proposal basically lies in few considerations about the status of interdisciplinarity
- OH definition is not among the objectives of this presentation. Different definitions exist. Our first consideration is that OH concept and practice basically calls into question complexity (scientific complexity, operational complexity) . Complexity stems from the need to take into account the various link occurring among different sectors (salutis loci) such as human health, animal health and environmental health. When OH activities (actions, measures, policies, or whatever) have to be evaluated, the same complexity does concern the evaluation process as causes and effects have to be evaluated across different sectors. This means that at methodological level OH evaluation has the same implications of OH.
- Non ci si improvvisa interdisciplinari, perchÊ ciò richiede investimento (tempo e risorse). Interdisciplinarity in science is usually called into question when disciplinary or traditional approaches fail or show limits. Actually interdisciplinarity is a trend in science making but many signs show that it is not as diffused as one may expect. Actually quantitative studies based on bibliometric analysis of scientific publications show that this trend is increasing at a slow rate in the long term and that it is not well diffused among different scientific domain. To assume that ID is still an exception and not the rule in scientific practice is realistic.
- Where we can do ID? Itâs probably trivial and simplistic to say that ID studies are developed by ID teams, the places where scientists integrate different disciplinary visions of the same problem and where new, integrated vision of that problem are produced. Itâs probably less simplistic deducing that team work is a crucial aspect of producing ID scientific vision of a problem. This means that the way skilled scientists work in a team deserve probably more attention than is now dedicated. For this reason we consider, and we assume in this presentation that team work is the front line of interdisciplinarity. Given the poor diffusion of an ID aptitude, itâs very likely that facing ID approaches to a problem probably requires former investment in human capital or in other words that the aptitude to work in ID team requires time and different kind of resources that will produce result over time. That is indeed an investment in economic terms
- Still at present evaluation in many sectors and in health is mainly based on sectoral or disciplinary vision. In human health, direct effects of measures obviously exist. Effects are also assessed against cost: cost-effectiveness analysis and cost-utility analysis are used in human health to evaluate policies. The effects of animal health interventions are often assessed at farm and sector level; less frequently interventions on animal health are also evaluated against their effects on human beings. Measures concerning the environment seem to have a wider scope in evaluation, but most often they adopt a consumer oriented approach, based on the evaluation on environmental services from a consumer perspective. The common trait of evaluation in the above mentioned sectors is that they do not cross sectoral borders (despite the fact that cross sector relationship are known at scientific level). And they miss the link with the wider socio-economic context, despite the fact that evaluation tools to this aim does exist (e.g. Cost benefits analysis, multi-criteria analysis).
- According to OH concept Evaluation should take into account cross sector relationship and provide means to improve policy effectiveness accordingly, as shown in the figure. The way to reach this aim is not easy and requires theoretical elaboration as well as viable implementation tools. In this presentation we mainly focus on the second aspect.
- As mentioned before, we assume that ID research requires ID teams, that most often ID mind must be trained also when it is formed by motivated researches. In our view building up ID teams requires some preliminary conditions, human capital training or investment, and team protocol to guide day by day routine. We will briefly go through this elements which are incomplete indeed but probably outline the most relevant aspects, usually disregarded in the literature.
- Consapevolezza di voler superare questi limiti Preliminary conditions are motivation and personal aptitude. Motivation is related to the perception of the need for ID. This often originates from the inadequacy of traditional silo approaches, to which the excellence in one single discipline or sector cannot give solutions. Personal aptitude relates to the fact that individual scientists may admit this inadequacy and accept to open themselves to visions, point of views, opinion of other disciplines on the same problem. This may not be an easy task for very specialized researchers, which tend to adopt their own vision as the most relevant one.
- Investments in human capital mainly concern: the need to become acquainted with system thinking, that is the ability to investigate complexity through a scientific method sharing information of different nature about a problem with other scientist, which usually implies mutual training, and .. being acquainted with participatory approach, to collect the different vision of the problem Finally, this should lead to a problem modelling which reflects the different disciplinary visions of the problem and summarizes them in a coherent scientific framework, which involves the different health sectors.
- Expand = acqiuosisce nuove competenze The resources of the team are: - the scientific skills of the members that should reflect the status of the different knowledge and disciplines on the problem; the ability to communicate, as mentioned before, and the flexibility, that is the aptitude of a team to adapt to the growing vision of the problem, to include new competences or exclude redundant ones. This requires of course a careful leadership to manage the team constitution and further development.
- A relevant aspect of building up an ID scientific team is how to do it or in other terms the strategy to reach the objective.
- Also in this case we started from some basic assumption. We can assume that any skilled scientist is well acquainted with its scientific domain. Problems may rise when this scientific status is confronted with other scientific domains, when rooted knowledge and methods are criticized or must be adapted in a wider knowledge system. Border problems may appear, as well as conflicting visions. Confrontation takes resources and understanding complexity may be seen as a process
- In our opinion complexity can be understood starting from disciplinary competences if the process is adequately managed and adequate tools are crafted to go with this process.
- For this reason we propose the ID matrix, a sort of input/output matrix which should allow team participants to start the process of understanding and managing complexity to approach OH evaluation
- The evaluation process may be reduced to few simple questions: What is the starting point: what is/are the actions that start effects (inputs)? Where this inputs its and produce effects (outputs)? How inputs and outputs are related (causal relationships)?
- Based on this simplified vision of the evaluation problem, inputs and outputs can be identified. Ideally sectors correspond to disciplinary visions, so inputs and outputs can be identified according to disciplinary competences, and joint team work help identifying cross sector relationships. In the next table we provide a tentative exercise of IDM. It concerns a typical case of zoonoses (e.g. Brucellosis) and the evaluation of measures to fight it.
- In this context, which is related to specific actions to address animal health, prevention measures are inputs (first column). Prevention are implemented by public agencies and imply expenditure from the public budget. They are articulated in animal surveillance, animal vaccination, and consumer information on the risks of infected animal products. (Description of the link) We treated the case in a very simple way, cause to effect chain may be imprecise or incomplete. But the relevant aspect of the exercise is that it is developed by scientists of different sectors, working together, to identify the relevant effects of the prevention measures (in this case they might be: veterinarians, epidemiologists, physicians, nutritionists, economist, animal production scientists). The relevant aspect is the method and the final product is the global vision of the problem.
- In late 2014 an interdisciplinary network was established to sut up widely agreed protocols for the evaluation of OH initiatives. The Network for the Evaluation of One Health (NEOH) is funded in the framework of the COST Action of the European Commission.
- NEOH is a 4-years project, based on 4 pillars which correpond to 4 Working Groups: the development of the overall evaluation framework, a One Health index and an evaluation protocol; the application of the evaluation framework, the OH protocol and the evaluation index to different One Health initiatives or case studies, using primary and secondary datasets; the development of a meta-analysis on the available case studies to facilitate international comparisons and the elaboration of policy recommendations; the development of a dialogue with national governments, NGOs, research organisations, and industries to ensure that the evidence produced will address decision-makersâ needs. At present we are focusing on the first step of the process, to build up an interdisciplinary methodology for evaluation and drafting a methodological textbook practitioners. NEOH adopts available COST Action tools to strengthen collaboration among scientists and professionals from different disciplinary fields, such as regular meetings, short terms scientific missions (STMS) and training schools (TS). In particular STMS and TS give researchers of different disciplines the opportunity to work together and exchange experience and acquire skills relevant to NEOH. The first TS on âEvaluation: Best practice approaches and applications in multiple disciplinesâ was successfully implemented (Cluj-Napoca, Romania, 23-26 June 2015). It involved scientists and professionals with rooted experience in different evaluation contexts, from inside and outside the network, to allow crossing discipline borders and learn about evaluation in general and to reflect on the suitability and effectiveness of different evaluation methods and approaches for interdisciplinary initiatives (http://neoh.onehealthglobal.net/training-schools/). Beside the expected project outcome, an important value of NEOH lies in the process itself, and in the human capital investment of each researcher to activate a living ID platform.