efsa scientific colloquium summary report food producing animals

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ISSN 1830-4737
EFSA SCIENTIFIC COLLOQUIUM
SUMMARY REPORT
FOOD
PRODUCING
ANIMALS
Principles of risk assessment of food
producing animals: current and future approaches
1-2 December 2005, Parma, Italy
EFSA SCIENTIFIC COLLOQUIUM
SUMMARY REPORT
4
Food Producing
Animals
Principles of risk assessment of food producing animals:
current and future approaches
© European Food Safety Authority – October 2006
Reproduction is authorised, provided the source is acknowledged, save where otherwise
stated.
The views or positions expressed in this booklet do not necessarily represent in legal
terms the official position of the European Food Safety Authority. The European Food
Safety Authority assumes no responsibility or liability for any errors or inaccuracies that
may appear.
ISBN: 92-9199-030-2
.
Summary Report EFSA Scientific Colloquium 4, 1-2 December 2005, Parma, Italy
About EFSA
The European Food Safety Authority (EFSA) was established and funded by
the European Community as an independent agency in 2002 following a series
of food scares that caused the European public to voice concerns about food
safety and the ability of regulatory authorities to fully protect consumers.
In close collaboration with national authorities and in open consultation with
its stakeholders, EFSA provides objective scientific advice on all matters with a
direct or indirect impact on food and feed safety, including animal health
and welfare and plant protection. EFSA is also consulted on nutrition in
relation to Community legislation.
EFSA’s work falls into two areas: risk assessment and risk communication.
In particular, EFSA’s risk assessments provide risk managers (EU institutions
with political accountability, i.e. the European Commission, European Parliament
and Council) with a sound scientific basis for defining policy-driven legislative
or regulatory measures required to ensure a high level of consumer protection
with regards to food and feed safety.
EFSA communicates to the public in an open and transparent way on all
matters within its remit.
Collection and analysis of scientific data, identification of emerging risks and
scientific support to the Commission, particularly in case of a food crisis,
are also part of EFSA’s mandate, as laid down in the founding Regulation (EC)
No 178/2002 of 28 January 2002.
For more information about EFSA, please contact EFSA
Official seat:
Palazzo Ducale
Parco Ducale 3
I-43100 Parma
Italy
Operational and postal
address:
Largo N. Palli 5/A
I-43100 Parma
Italy
Tel: +39 0521 036 111
Fax: +39 0521 036 110
E-mail: [email protected]
www.efsa.europa.eu
.
.
CONTENTS
I
Introduction
9
II
Approach
11
III
Results
15
Risk Assessment Methodology
15
Data
24
Management of the risk assessment process
28
Communication of Results
29
IV
Summary and Conclusions
31
31
Data
33
Expertise and other Resources Needed
33
33
V.
Recommendations
VI. References
35
37
VII. ANNEXES
Annex 1: Programme of the EFSA Colloquium
43
Annex 2:
Participants at the Colloquium
47
Annex 3: Presentations made at the Colloquium 53
Annex 4: Slides of Discussion Groups Summary Report EFSA Scientific Colloquium 4, 1-2 December 2005, Parma, Italy
135
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.
I
Introduction
The fourth meeting of the EFSA Scientific Colloquium Series addressed the
principles of risk assessment (RA) in food producing animals.
EFSA colloquia reach out to find a better understanding of the fundamental scientific
issues related to risk assessment on subjects that come within the EFSA remit for
evaluation. They cover a broad range of topics grouped under the headings:
food, feed, zoonoses, animal and plant health, and animal welfare. Each colloquium is
organised in a way that is sufficiently informal to allow for substantial debate and a
lively interactive exchange of expert views, while at the same time, being properly
structured and managed to enable participants to reach conclusions and make the
appropriate recommendations.
Methodologies for the conduct of risk assessment have been, or are in the process
of being, established for the risks posed by various types of hazards. Risks involving
microorganisms (bacteria or viruses) are influenced by the following facts:
 The behaviour of microorganisms along the food chain (in feedstuffs, animals,
and food) which is dynamic and is itself influenced by the environment as well
as the processes involved;
 Micro-organisms may cause adverse events through single particle exposure;
 Hosts may develop immunity against microorganisms or suffer from
secondary infections;
 The initial host may also be the source of release for new infections; and
 Consumers may themselves prevent or control the risks involving microorganisms to which they are exposed.
Methodologies for the conduct of risk assessment have been applied to assess the
risk of various types of hazards due to micro-organisms. In microbiological risk
assessment of food, the framework developed by Codex Alimentarius and the
former Scientific Steering Committee of the European Commission is used (Codex
Alimentarius, 1999, and EC, 2003).
In addition, the World Animal Health Organisation (OIE) has issued guidelines
regarding the assessment of the risk of importing exotic diseases (OIE, 2004a,
See http://www.codexalimentarius.net/download/standards/357/CXG_030e.pdf.
.
and OIE, 2004b). However, in the areas of animal welfare and of the control of
endemic diseases there are currently no specific international guidelines on risk
assessment . Since the principles behind any risk assessment are broadly similar
and based on “logic chains” of events, the guidelines already in existence can be
adapted for risk assessments in other situations. For example, the OIE guidelines
are frequently used for endemic disease assessments.
The objectives of this colloquium were:
 To have an open scientific debate on the conduct and components of risk
assessment of diseases in food animals and of animal welfare; and
 To explore options for a guidance document in these areas.
The enthusiasm of the participants was greatly appreciated by EFSA, particularly
because it led to very open, lively discussions and a good outcome from the meeting.
Dr. Herman Koëter and Dr. Juliane Kleiner (European Food Safety Authority) were the
overall chairmen and Dr. Pierre Le Neindre (INRA, France) was the overall co-chair.
Dr. Dan Collins (University College of Dublin, Ireland), Jörg Hartung (University of
Veterinary Medicine of Hannover, Germany), Dr. Philippe Vannier (French Food Safety
Agency) and Dr. David Morton (University of Birmingham, United Kingdom) offered
to be discussion group chairs while Dr. Ivar Vågsholm (National Veterinary Institute,
Sweden), Dr. Mo Salman (Colorado State University, USA), Dr. Marion Wooldridge
(Veterinary Laboratories Agency, United Kingdom), Dr. Lis Alban (Danish Bacon and
Meat Council) and Matthias Greiner (Danish Institute for Food and Veterinary Research)
were the corresponding working group rapporteurs. Special appreciation is expressed
to Dr. Michael Gunn (Central Veterinary Research Laboratory) and Dr. Hubert Deluyker
(European Food Safety Authority), who acted together as overall rapporteurs and
who drafted this summary report.
In the context of this summary, endemic disease is used in contrast to exotic disease. In the
geographical area of interest the former has a predictable pattern of occurrence and is never
considered to be completely absent. The latter occurs with a frequency which is irregular and is
considered to originate from outside this geographical area.
10.
II
Approach
The EFSA scientific colloquium on risk assessment in food producing animals
took place on 1 and 2 of December 2005 in Parma, Italy to discuss the state-of-thescience. Some 80 experts representing relevant expertise in the field participated
in an exchange of views of various aspects of the colloquium theme.
The programme is attached (Annex 1).
The list of participants is attached (Annex 2).
First a number of introductory presentations were given by keynote speakers.
Copies of these presentations are attached (Annex 3). Then, participants were
assigned to one of the following discussion groups.
 Risk assessment on spread of endemic animal infections and diseases
 Risk assessment of importation of animal pathogens
 Risk assessment of animal welfare
While sharing a common interest in animal disease risk assessment, the emphasis
differed somewhat between these three groups. For endemic diseases, the focus
was frequently on choosing the most appropriate strategy to control or eradicate
a disease. In import risk assessment, the focus adjusted to reducing the risk of
introducing the disease. But on questions of animal welfare, the perspective
widened, turning from a narrow focus to the consideration of animal disease as
just one of several indicators of less than optimal animal well-being.
Each group addressed the following key components of risk assessment:
 Data needs, including the need for setting up data collection systems,
epidemiological data analyses and dealing with incomplete information;
 Methodology for the conduct of risk assessment in various areas of animal disease
and animal welfare, including similarities and differences when compared with
other areas such as microbiological risk assessment of food and feedstuffs;
 Expertise and other resource requirements to conduct risk assessments in
these fields; and
 Communication of results, including their uncertainty.
The following is a resume of the topics that were suggested for consideration by
each group.
11.
1Discussion groups 1 and 2: Endemic animal infections
and diseases
1.1
Risk assessment methodology
 Specific elements to consider in risk assessment of control strategies of endemic
diseases may include risk factors for disease, safeguard measures, etc.
 Role of qualitative risk assessment versus quantitative risk assessment (QRA);
 Rationale for “farm-to-fork” analysis; and
 Potential use of previous risk assessments, i.e. relevance for a new mandate.
1.2
Quality and availability of relevant information
 For endemic diseases, data are often collected in the framework of disease
control and meat hygiene inspection programmes. However, data needs to be
accessible and reliable i.e. of sufficient quality, consistency and uniformity.
 Approaches to dealing with missing information (e.g. seeking expert opinion,
scenarios, etc.) were also proposed for consideration.
1.3
Expertise and other resource requirements
 Rationale for the resources used and the timeline set for the completion of a
risk assessment versus the “importance” of the question and the expectations
of the risk manager i.e. fitness for purpose.
2Discussion group 3: Importation of animal pathogens
2.1
 The OIE guidelines provided an approach. Since these guidelines have been in
place for a number of years, it was useful to reflect on the experience gained
while using them.
 The need for scientifically-based approaches to determine “design prevalence”
to establish freedom of disease was also considered. The conditions under
which previous risk assessments could be used, were discussed.
12.
Approach
2.2
Quality and availability of required information
 Key data often does not exist. Hence, discussion centred around the need to take
a proactive approach towards setting up relevant data collection systems.
 Approaches for dealing with missing information e.g. seeking expert opinion,
were also considered.
2.3
Expertise and other resource requirements
 As in discussion groups 1 and 2, this group included the rationale for the
resources used and the timeline set for the completion of a risk assessment
versus the “importance” of the question and the expectations of the risk
manager (fitness for purpose).
3Discussion Group 4: Animal welfare
3.1
 The group was invited to consider different options for carrying out risk
assessments on animal welfare as there may not necessarily be a
“one size fits all” option. For example, the assessment might only relate to
animal welfare in a strict sense or it could equally include an animal disease
component as well as a human wellbeing aspect.
 The group looked also at how hazards should be considered in this context,
taking into account their effects on animal health and behaviour.
3.2
Type, quality and availability of required information
 The discussion considered criteria that could provide useful indicators that
would make it possible to set parameters for measuring animal welfare.
 The discussion on data needs looked at existing data collection systems and
the need to actively set up new ones.
13.
14.
III Results
1
Common principles
In the initial stage of a risk assessment the objectives are defined. The risk manager
as “owner of the risk question” has a primary responsibility in the construction of
the risk question(s). As several agencies may have ownership of the management
of an infection or disease problem, it is important - at the very beginning of the
process - to identify who all of the risk managers are and which one will take
the leading role for the question at hand.
Whereas the risk manager is the owner of the risk question, the risk assessor is the
“owner of the risk assessment” process. Once a request has been received, it is
necessary for the risk assessor to discuss the issues involved with the risk manager
and seek agreement on them. This is viewed more as an ongoing process rather
than a once-off discussion.
This gathering of information by the risk assessor serves a number of purposes.
 It improves understanding of the question posed and the expectation about
the outcome, for both the risk assessor and the risk manager.
 It serves as a guide during the risk assessment process as it sets the scenario in
which the risk assessment is to be done. In straightforward cases, the risk profile
may even suffice for a risk management decision to be made.
 It helps to clarify the scope of the project, to gain consensus on timelines,
and to identify resources needed which are consistent with the scope and the
timelines of the project.
The information is used to develop a risk profile which will include some or all of
the elements described below.
 The risk question of interest is defined as well as the expectations once the
question is addressed.
 Hazard identification: the hazard is identified, defined, and where
necessary described. In the OIE system the hazard is defined as a pathogen
(or chemical); in other systems the definition may be broader to include
situations or events.
15.
 Hazard characterisation covers the range of unwanted consequences (adverse
events) which the hazard might cause. These are identified and characterised
in time and space.
 Current understanding describes the exposure routes and the effects of the
infection or disease problem.
 Management options are those that potentially exist to control or eradicate
the infection or disease along with the factors that will influence the choice
to be made by the risk manager e.g. their economic impact, promotion of
animal welfare.
 Stakeholders need to be identified and their level of involvement defined.
These elements are further developed below.
The risk manager must ensure that the risk question is interpreted in the
same way by all involved and that the risk assessor is addressing the question in
the appropriate manner as it has been put forward by the risk manager. It should
be clearly stated whether or not public health issues are to be addressed and the
reasons why should be given. It is important that lack of clarity be resolved through
interaction between risk assessor and risk manager so that an unclear question can
be rephrased if necessary.
The nature of the hazard merits careful consideration. Some micro-organisms are
highly infectious and others are much less so. In the latter case, disease is often
caused by a combination of several factors i.e. it is a multifactorial disease.
For the purpose of hazard characterisation, the concept of animal disease should
initially be interpreted in the broadest sense. This may be particularly important
when assessing animal welfare i.e. mental health, psychological well-being.
Hence, at this stage consideration should be given as to whether to include only
infectious diseases or whether non-infectious conditions e.g. metabolic diseases,
genetic conditions or diseases caused by chemicals, are within the scope of
the mandate.
 To determine the risk question, there is a need to have a clear definition of the
endpoint and thus of what constitutes a “case”. Is it the disease or the infection
that is the endpoint of interest? Is it all species of a bacterial genus? Are all
serotypes of a virus relevant?
16.
Results
 Furthermore, when defining a “case” it should be determined whether the
task at hand is concerned with a clinical disease and/or sub-clinical forms of
the infection, or the presence of an infective agent of concern. For example,
for the study of the impact of salmonella on public health, the prevalence of
animals that are shedding the organism in the faeces is much more relevant
than the prevalence of animals that are clinically diseased as a result of
salmonella infection.
 Is there a certain minimum threshold above which the disease is deemed to be
a problem?
Current understanding of the importance of the infection and, where appropriate,
of the disease(s), is essential. This should be described in time and space.
The time component refers to the incidence over time, while space means
the description of the geographical entities of interest with meaningful
epidemiological or political boundaries. The latter often determine the disease
control policy. This aspect is particularly relevant in an international context.
Based on the prevailing epidemiological situation and the control policies in
place, regions which group together several countries or parts of countries may
be identified as a single epidemiological unit of interest.
The information provided on infection and disease management and the
advantages and disadvantages of various risk management options should
enable an evaluation of each of them as needed to be addressed in the risk
assessment. In particular, management measures that are considered realistic
by the risk manager merit consideration in the risk assessment. Hence, although
cost-benefit analysis may lie outside the scope of a risk assessment, it is essential
that the risk assessment team has a clear understanding of the options for
control of the infection and the disease e.g. their practicality (time and cost)
effectiveness, animal welfare, and public health consequences.
When conducting a risk assessment, it is common practice to describe the risk
pathways. These are also known as risk scenarios. They represent a logic chain
(or series of such chains) linking the source of the hazard, the exposure route(s)
relevant to that hazard, and its consequence(s). Such a pathway, when illustrated,
may indicate enabling or predisposing management factors such as hygiene,
environmental factors such as housing, and animal factors such as genetics
(e.g. sex, breed), and physiological status (e.g. stage of pregnancy).
17.
Throughout the risk assessment process, fitness for purpose should be
considered. This applies for example to the decision regarding the choice of the
model and, more specifically, whether it is always appropriate to use a farm-tofork approach.
 The farm-to-fork approach consists of a logic chain beginning at the farm and
ending with the consumer. Modelling every step of the full process may be
justifiable when investigating public health issues. Depending upon the risk
question and the knowledge and resources available, alternative approaches
requiring fewer resources may need to be considered.
 An approach whereby a limited part of the food chain is considered may be
adequate for an animal disease which does not give rise to human disease
via the food chain.
 In marginal models the impact of various inputs and the output
(the probability of the occurrence of the adverse event) is modelled by
establishing their direct relationship or empirical relationship without
modelling every single step linking these inputs with the output. In reality this
is the case in any risk assessment as any major step in a risk assessment
represents an aggregate of smaller steps which are not individually
modelled. However, in marginal models the aim is to reduce the complexity of
the model to the minimum appropriate level for the question of interest.
For example, the impact of smoking on the risk for lung cancer was
demonstrated without a full understanding of the underlying causal
relationship. Intervention measures can be proposed, and their effect
assessed on the relevant part of the risk pathway, without a full understanding
of the disease process. This was also the case with BSE. This approach may
also be relevant in describing the link between the farm disease situation and
consumer exposure to e.g. salmonella.
While describing this empirical relationship, it is considered essential that the
proposed underpinning for this relationship be provided. This helps explain
the boundaries within which empirical evidence is available.
Previous risk assessments can be of use as they avoid “re-inventing the
wheel”. While it is recommended that such previous works be considered,
they nevertheless need to be subject to a critical evaluation to assess their
relevance for the purpose of the current risk assessment.
18.
Results
The principle of fitness or suitability for purpose also applies to the choice of
the risk assessment method. Qualitative and quantitative approaches are often
contrasted. However these approaches are better viewed as part of a continuum.
The choice should be based on their usefulness for the specific assessment.
It may mean that one of the two approaches is used, or that both should be used,
or that first a qualitative assessment is conducted and after that a decision is
made on whether to proceed with a quantitative assessment. In the latter case,
the quantitative risk assessment (QRA) could be conducted on a part of the
risk assessment that was identified to be the most critical in the qualitative
risk assessment.
A QRA is sometimes considered too demanding in time and effort because of
the perceived modelling complexity and the availability of resources to conduct
a QRA in a timely manner. However, a qualitative assessment can also be
time-consuming.
Qualitative risk assessments may offer a lot of detail for every individual step but
the expression of the overall probabilities associated with the risks poses a specific
challenge. Possible choices include the following:
 Describing the probabilities in “simple” terms e.g. high, medium, low, negligible
etc. These can be considered provided their meaning is clarified. Nevertheless,
this is not a straightforward approach as the risk manager needs to understand
the full range of evidence on which this description is based.
 Lengthier blocks of text describing the risk level in detail, including attendant
uncertainty and variability, are more appropriate. This reduces the likelihood
of misinterpretation of, for example, different categories of risk and thus
enhances transparency.
 Matrices using combination rules between, for example, the probability of an
unwanted event and the impact if it occurs. Matrices can easily lose
transparency if inappropriate mathematical combinations of arbitrary scoring
systems are used. They are not generally recommended as a result.
 Relative risks (higher, lower), instead of absolute risks, often represent a
useful system.
In contrast, in a QRA the overall results may appear to be more revealing
than those of a qualitative assessment since they are given in a numerical format –
19.
either as a number or as a range. However, the very precision of this format can
also be misleading. This is especially the case with very low probability
consequences and it may be very difficult for the risk manager and others to
conceptualise what such a result really means. In addition, the choice of data inputs
and other modelling considerations are subjective and this will affect the outcome.
Although neither system is free from subjectivity, it can be seen that each has its
advantages and disadvantages.
Regardless of the choice, attention should be paid to the units used to express the
results e.g. per animal, per quantity, such as tonnes, imported annually, etc.
It is recommended that a sensitivity analysis be conducted to determine
to what extent various uncertainties affect the conclusions and recommendations.
This can be an important message for the risk manager and it may help to decide
where to focus resources in order to reduce the uncertainty about the final risk.
Validation of results represents an integral part of the risk assessment.
While highly desirable, validation may be difficult or, in the case of rare events,
impossible to carry out. Statistical methods to validate results have been proposed.
Also, experimental or epidemiological data, that were not available at the time
the risk assessment model was developed, may be used. However, it must be
remembered that where risk assessment conclusions differ from observed data,
this may be because the observed data are obtained from a scenario which is
different from the one modelled originally.
Aspects specific to endemic animal infections and diseases
For the control of animal diseases, epidemiological analyses are classically
used to identify risk factors that affect the spread of these diseases.
Risk managers use this knowledge about risk factors to lower the risk for spread
of endemic diseases. Risk assessment is a useful additional tool in this area
because it allows the risk manager to assess which of the available options for
control or elimination of the disease is the most efficient.
However, the risk assessment process needs to be refined to make it appropriate
for use in an endemic disease risk assessment.
 This requires agreement on what constitutes an endemic disease and
what is a control measure. For example, whereas it is understood that
20.
Results
an endemic disease means one that remains prevalent over time, it is proposed
that sporadic diseases also be included.
 It may need to be clarified as to whether or not the control measures only
pertain to control the disease or infection or to other risk reduction measures
that e.g. reduce the salmonella load on a poultry carcass, as well. Decisions on
control measures weigh the benefits against the costs. In this regard a risk
benefit model, weighing the advantages and disadvantages of various control
measures, may help the manager.
 Eradication of an infection in animal is quite often made more difficult due to
its presence in wildlife e.g. avian influenza, brucellosis, rabies, swine fever,
trichinella, and tuberculosis. The specific interface between farm animals held
under commercial conditions and wild animals which, when infected, can have
major impact in this disease should be identified with every risk assessment,
where appropriate.
Aspects specific for importation of animal pathogens
In the context of imports by European Union (EU), import risk assessment may
often focus on imports at the EU level.
For import risk assessment for infection and disease in animals and
their products the OIE has developed specific guidelines which are most often
used as the reference. They cover basic principles for use by all OIE members,
i.e. in a broad range of environments. Additional clarification may be required in a
few specific areas. Concerns have been expressed over three areas in particular,
these being the definition of borders, the concept of “border gradients”,
and hazard characterisation in the trading partners.
As far as borders are concerned, there is a need for clarification on whether
quarantine facilities, physically situated within the importing country, are to be
considered as being located pre or post-border. The way they are considered in
the model will depend upon the risk question and the underlying issue. On the one
hand, in a legal sense, such quarantine stations are “pre-border” to the importing
country. On the other, if the biosecurity is poor, a pathogen may escape directly
within the importing country as it would from any other type of animal
accommodation. The risk manager may be interested in how likely this is to occur.
The recommendation is that there should be no overall rule and that this aspect
must be considered on a case-by-case basis.
21.
The term border gradients refers here to a situation that can arise when the
importing country is itself not free of the infection or disease of interest.
The question arises whether import poses an additional risk. This additional risk
could thus result in an increase in infection and disease prevalence. Currently the
OIE guidelines do not specifically consider such a scenario although the general
methodology remains applicable.
As further discussed in the section on data, related to this is the question
about knowledge of occurrence and prevalence of infection and disease in
importing and exporting countries. This hazard needs to be described using
common and agreed valid parameters. Currently, the OIE code only specifies
notification of the presence of an infection or disease whereas, for risk
assessment purposes, both their temporal levels and geographical distribution
need to be considered.
Aspects specific to animal welfare
The risk assessment process is not commonly used in animal welfare.
It is considered that its introduction in this field may be beneficial in helping
identify risk pathways and quantifying the importance of a hazard. Animal welfare
risk assessment should follow the general risk assessment principles.
As in other areas of risk assessment, the welfare consequences are the result of
the nature of the hazard and the nature of the exposure to it whereby the latter
includes the frequency (incidence), intensity, and the duration.
For animal welfare hazard characterisation, it is considered essential
that the key welfare indicators be directly animal based e.g. cow lame,
rather than allowing for what have been described as “indirect parameters”
of animal wellbeing e.g. housing system. The measurable factors that
are proposed as directly relevant include health and behaviour. This raises the
question of whether physiological parameters such as immune competence and
homeostasis can be used routinely as a measure of welfare. The difficulty with
such parameters lies in their interpretation as expressions of welfare. The use of
physiological parameters can be recommended instead of observations on
health and behaviour when they cannot be measured directly in a particular study
and also if their relationship with these health or behaviour parameters has been
validated in previous studies.
22.
Results
Behaviours originate from both intrinsic and extrinsic requirements (needs) that
animals seek to satisfy. These needs may be considered to be an ordered hierarchy.
For behavioural aspects expression of both “good” behaviour, e.g. grooming and lack
of “bad” behaviour, e.g. stereotypic behaviour have to be considered. A list of key
welfare indicators can be identified for all species. However, it is necessary to consider
indicators that are specific to species, breed, age, physiological state, and production
system. The term “hazard” should therefore not be interpreted as always referring to
a detrimental consequence as it may not necessarily be negative.
Animal welfare risk assessment can be perceived as assessing risks for beneficial or
adverse consequences to four major outcome areas that could be affected: animal
health, animal behaviour, public health, and productivity/economics. This is illustrated
in the following fictitious examples (Figures 1, 2, and 3) of three housing systems.
Each housing system is then assessed for the risk (by assigning a score) to each of the
four proposed areas. The scores that were assigned are hypothetical. Instead of
the three classes of risk scores used in the example (high, moderate or low), it is
recognised that in reality there may be a continuum instead.
Figure 1. Hens in barren battery cages
+ Public Health
Risk of poor welfare
Production/ +
Economics
+ Behaviour
low
moderate
high
+ Animal Health
Figure 2. Hens in furnished cages
+ Public Health
Production/ +
Economics
+ Behaviour
low
moderate
high
+ Animal Health
23.
Figure 3. Hens in free range
+ Public Health
Production/ +
Economics
+ Behaviour
low
moderate
high
+ Animal Health
2Data
Common issues
Once the risk pathways are established, as a direct result, the ideal initial data
requirement also needs to be identified.
A commonly encountered problem when searching for data, is that the
ideal data are simply not available. Data quality problems often include lack of
precision, bias, incompleteness, and lack of relevance for the intended use.
Also, data generated for a different purpose should be carefully evaluated prior to
their use in a risk assessment. Occasionally an “intelligent analysis” of the
available data may allow one to make the correct inferences for their use in
the risk assessment at hand. The numerical nature, i.e. if it is continuous, discrete,
ordinal, interval (bands), binary, or ratio, is not an issue unless a quantitative
assessment is being undertaken.
Critical data are often missing. Lack of data is not unexpected and the identification
of data gaps is in fact a purpose of risk assessment. Often “dose-response” studies
have not been carried out. For example, in regard to animal welfare risk
assessment, the necessary studies to assess the effects of transport time of
slaughter animals (dose) on animal welfare and meat quality (responses) are
often lacking. If no experimental data are available, disease outbreak
investigation may sometimes be useful for obtaining an indication of the doseresponse relationship.
24.
Results
Thus, gaps should be identified and their potential impact on the magnitude of the
uncertainty of outcome should be described and explained to the risk manager.
Where data is missing or needs interpretation, expert opinion can be sought.
Data generated by expert opinion should meet specific quality criteria. There is
also a need for transparency as to what was sought, how this was acquired and
who provided it. The choice of modelling techniques used to summarise expert
opinion merits careful consideration. In the end, any interpretation can be
criticised. The main point, however, is that the process used to make an assessment,
e.g. on the prevalence of an exotic infection in various regions of the world, should
be well documented so that the process used is transparent.
Data is required on the frequency of occurrence of the hazard. This is often
described either as:
 The prevalence of infection in an animal population; or
 The prevalence of contamination of animal product along with the distribution
of the concentrations of the organism that are present i.e. the viral, bacterial or
parasitic load.
Sources of such information originate from observational studies such as casecontrol studies, outbreak investigations or experimental studies. In general, there
is an absence of standardisation of data definitions as well as a lack of standard
methods used to collect these data.
Also, for incidence data, the characteristics of the surveillance system
used to generate the data, needs to be understood. Knowledge of the
characteristics of e.g. the nature of the diagnostic tests used, the number of
samples taken, and the sampling frame used, is necessary to be able to address
questions such as the ones described below.
•
What is the sensitivity of the surveillance system and what is the minimum
infection prevalence that can be detected given the proportion of the population
being tested? The answers to these questions are particularly important when
demonstration of freedom of infection or disease is needed. If the prevalence of
infection is very low, the numbers of animals that need testing (and the costs
associated with this) may make it impractical to find all new cases. Thus, the concept
of “disease freedom” may need to be replaced by the concept of “probability of
disease freedom”. Often this can be based on a range of evidence rather than on a
single study.
25.
 Will sub-clinical infection be detectable?
 Can diseases that up to now have been termed “exotic” be detected with the
diagnostic tests that are [commonly] used? For example, sero-surveillance
may be inappropriate for rapid detection of the emergence of such a disease in
an area that was previously free.
The epidemiology of the infection or disease is often heavily influenced by human
intervention such as for example the structure of the animal production industry.
Thus, in order to interpret survey results correctly, such aspects need to be
considered as well. Finally, it is always useful to have an understanding of the
natural history of the infection.
Relevant exposure data usually involve, amongst many other things,
knowledge of the movements of animals and animal products. Databases on
these aspects are the responsibility of risk managers. An example is the TRACES
database on imports and on intra-Community trade in animals and animal
products. Hence, at the onset, when the risk assessor and the risk manager are
defining a risk assessment project, the extent of the risk assessor’s access to the
relevant data needs to be clarified and agreed upon. However, the risk assessor
needs to provide assistance in this process, giving guidance to the risk manager
by indicating precisely what data is needed.
Another aspect that may need careful consideration is the proportion of
total exposure resulting from imports. For example, a given country or
region poultry may be free of infection such as salmonella. Thus, the potential
routes of humans exposure to salmonella via poultry meat, will then
include poultry meat imports from infected areas and travel abroad to such
areas. The magnitude of such exposure can be ascertained through
official trade databases such as those managed by Eurostat. However, it should
be recognised that these do not account for illegal trade. Illegal imports
could be associated with a higher risk of infection or contamination than in the
case of legal trade.
When required, there should be a thorough evaluation of the data available from
wildlife. Instead of authorised surveys of wildlife, hunters may be the main source
of such data. The latter data may be incomplete, biased, and carry a high degree
26.
Results
of uncertainty. To interpret and summarise such data, expert evidence and expert
opinion may be needed.
Also, the required data may be held locally rather than at a national level,
or by agencies other than those to which the risk manager is affiliated.
Aspects specific to the import of animal pathogens
The collection of data on exotic infections is a difficult task as there often is a
paucity of such data. For key exotic infections, the OIE is an obvious source of
information. This will need to be supplemented with other data, for example from
the EU reference laboratories, other laboratories in Europe or elsewhere,
from missions carried out by the European Commission and by the EU Member
States. World-wide disease surveillance networks would be useful.
In addition, the quality of the surveillance system is dependent on the resources
devoted to it and these may vary considerably. The assessment of the competence
of the governmental authorities responsible for the conduct of surveys is an issue
that also merits careful consideration. The European Commission’s Food and
Veterinary Office collects data on these aspects for countries that wish to export
to the EU. Such an assessment is another example where interpretation based on
expert opinion is needed. A decision must therefore be made on a case-by-case
basis as to whether the surveillance system can be trusted. In the end, the data on
disease occurrence may not be considered to be very reliable. Such interpretation
may result in a trade dispute.
To assess animal welfare it is necessary that welfare be measured directly on the
animals e.g. lameness in cows. On these types of variables, there are usually a
large body of qualitative data (e.g. scores) available, but little in the way of
quantitative data.
An increasing proportion of the food of animal origin consumed in the
EU is produced outside the EU. Hence, when assessing some animal
welfare issues, the global food chain, including farming of domesticated and
wild animals in various regions of the world outside the EU, might need to
be considered.
27.
3
Management of the risk assessment process
Expertise and other resources needed for the conduct
of a risk assessment
Risk assessment is, by its very nature, a multidisciplinary activity. The selection of
team members should take into account the various areas of expertise required.
This may have to address any of the following needs:
 Microbiologists with expertise in the hazard of interest;
 Epidemiologists to collate, assess, and interpret the epidemiological data;
 Mathematical and statistical expertise to model the data quantitatively; and
 Expertise in control of the infection and disease to make sure that the risk
mitigation strategies that are evaluated are interpreted correctly.
Once the areas of required expertise are identified, the actual choice of experts will
depend on the availability of individuals. Also, any potential conflicts of interest
which lead to bias need to be addressed in advance of such appointment.
Thus the issue of the various resources that need to be brought to bear in the
conduct of a risk assessment offer another example of the fitness for purpose
principle. The provision of these resources should be commensurate with the
magnitude of the task at hand and the need to complete the risk assessment in a
timely manner. In some cases it may be that the risk assessment needs to address
an urgent public health question requiring an immediate decision. On the other
hand, for detailed research-oriented questions, the timeframe may require and
allow for a more in-depth evaluation.
During the risk assessment process findings can emerge which may affect the
further conduct of the risk assessment. For example, it may be concluded that
the risk assessment is not feasible or cannot be carried out within the required
timeframe. This is why the dialogue between the risk assessor and the risk
manager needs to continue after the initiation of the risk assessment. In effect,
there is an absolute need for continuous dialogue between risk assessor and risk
manager throughout the risk assessment process.
28.
Results
4
This phase also requires dialogue between risk assessor and risk manager.
Communication of the results should be planned and undertaken as a shared
responsibility. This approach ensures that the results of the risk assessment are
presented in the report in a way that is clear and meaningful to stakeholders,
as well as to a more general audience, i.e. fit for purpose.
Aspects that merit attention in order to achieve transparency include
the following:
 The identification of data weaknesses, their impact on the uncertainty of the
results and recommendations and the need for improvement in data collection
- where appropriate;
 The necessity to point out assumptions; and
 To highlight the results of the sensitivity analysis, along with the implications.
A question that is sometimes posed is what weight should be attached to a worstcase scenario. The justification for considering reasonable worst-case assumptions
should be transparent. One approach is to weigh the likelihood of the various
possible outcomes with the magnitude of the consequences that would result
from them. For example, the importation of a particular exotic pathogen may be
unlikely; however the consequences of this unlikely event may be dramatic.
Finally, there is also a need for feedback from the risk manager about the outcome
of the risk assessment. For example, with regard to safeguards, the evaluation of
whether and why proposed management measures worked or did not work as
predicted is important information for a future risk assessment.
29.
30.
IV Summary and Conclusions
In each section the conclusions that apply to all three sub-topics
are presented first and then the conclusions that apply specifically to one
area are presented in turn.
1
At the initial stage of the risk assessment the hazard of interest is identified,
the nature of the unwanted consequences (adverse events) is described, including
temporal and spatial aspects, and potential control measures are identified.
This information is used to develop a risk profile. A risk profile also includes the
risk question, the current understanding of the infection and disease, and realistic
management. There is a need for risk managers to provide input into this process
to make sure that the risk question is interpreted in the same way by all parties
from the start of the process.
With increasing levels of imports into the EU, it is important to consider the food
chain globally, rather than at the local level alone.
Throughout the risk assessment process fitness for purpose should be taken into
consideration. This affects decisions on:
 The choice of qualitative and/or quantitative approaches, which should be
based on their usefulness for the specific assessment;
 The rationale for using a farm-to-fork or any other approach; and
 The availability of resources devoted to the project.
Previous risk assessments may be very beneficial but their use merits
careful evaluation.
Validation of results represents an integral part of the risk assessment.
While highly desirable, validation may be difficult or, in the case of rare events,
impossible to carry out. Statistical methods to validate results have been proposed.
Also, experimental or epidemiological data, not available at the time the risk
assessment model was developed, may be used.
31.
The issue and the concerns regarding endemic disease risk assessment are
different from those that apply to exotic disease risk assessment. The OIE import
risk assessment guidelines are not directly applicable and would need to be
revised in order to make them suitable for use in endemic disease risk assessment.
This would include e.g. a definition of what constitutes an endemic disease and of
what constitutes disease control.
The interface between production animals and infected wild animals should be
identified and addressed, where appropriate.
Aspects specific for importation of animal pathogens
OIE guidelines need to be considered to determine where clarification may be
beneficial. With respect to post-import quarantine facilities, and their relationship
with country borders, a case-by-case approach taking into account the risk
question and practical situation is probably the most appropriate strategy.
Welfare encompasses two major indicators i.e. health and behaviour. Physiological
responses, such as immunological competence also merit consideration. The difficulty
with the latter type of parameters however, lies in the interpretation of them as
expressions of welfare. The use of physiological parameters can be recommended
instead of observations on health and behaviour when the latter can not be measured
directly in the particular study and also if the relationship between the physiological
parameters with these health or behaviour parameters has been validated in
previous studies.
Behaviour originates from the intrinsic and extrinsic requirements (needs) of the
animal. These needs can be ordered hierarchically. For behavioural aspects
expressions of both good behaviour, e.g. grooming, and lack of bad behaviour,
e.g. stereotypic behaviour, have to be considered. A list of key welfare indicators
can be identified for all species. However, it is necessary to consider specific
indicators depending on the species, breed, age, physiological state, and
production system.
32.
Summary and conclusions
2Data
Critical data are often either missing e.g. dose-response studies or incomplete
e.g. surveillance data. Lack of data is not unexpected and the identification of data
gaps is in fact a purpose of risk assessment. Thus, gaps should be identified and
their potential impact on the magnitude of the uncertainty of outcome should be
described and explained to the risk manager.
There is a general need for standardisation of disease data definitions and
collection methods. Data generated for specific purposes should be carefully
evaluated and their suitability assessed prior to their use in a risk assessment.
3
Expertise and other Resources Needed
The type of expertise and the magnitude of the resources allocated to the risk
assessment should be commensurate with the expertise required, the availability
of experts, the magnitude of the tasks, and the need to complete a risk assessment
in a timely manner.
Also, there should be ongoing dialogue between the risk assessor and the risk
manager throughout the risk assessment.
4
The communication of the results should be planned and undertaken as a shared
responsibility between risk assessor and risk manager. This approach ensures
that the results of the risk assessment are presented in the report in a way that is
clear and meaningful to stakeholders, as well as to a more general audience,
i.e. fit for purpose.
33.
34.
V
Recommendations
The major general recommendations regarding the process used to conduct risk
assessment in the areas of animal welfare and animal diseases are listed below.
 There is a need for dialogue between the risk assessor and the risk manager
throughout the risk assessment process.
 Risk profiles need to be created at the start of the project.
 Data gaps should be identified and addressed. To further this aim,
research projects may need to be developed to address key data needs,
methods and agreements.
 The rationale for using a farm-to-fork - or any other approach - should be
based on its fitness for purpose. Whether the whole chain is modelled
explicitly or not, communication is necessary to explain the model that
was used.
 Ensure that processes are developed to validate the results of the
risk assessment.
Specific aspects identified by the subgroups for consideration.
 To clarify some parts of the OIE guidelines for import risk assessment.
These areas are outlined in this report.
 To adapt the risk assessment process recommended by the OIE for import risk
assessment to make it more appropriate for use in endemic disease control.
 That it would be worthwhile to set up a working group to further investigate
methodologies for risk assessment of animal welfare. EFSA may consider
developing guidelines in this area.
35.
36.
VI References
Codex Alimentarius, 1999. Principles and Guidelines for the Conduct
of Microbiological Risk Assessment. CAC /GL-30, 7 pp. (available at
http://www.codexalimentarius.net/download/standards/357/CXG_030e.pdf)
EC (European Commission), 2003. Second report on harmonisation of risk
assessment procedures by the Scientific Steering Committee (SSC). Appendix 3:
Report on risk assessment of food borne bacterial pathogens: a quantitative
methodology relevant for human exposure assessment.
OIE 2004a. Handbook on Import Risk Analysis for Animals and Animal Products.
Volume 1. Introduction and qualitative risk analysis. pp. 57.
OIE 2004b. Handbook on Import Risk Analysis for Animals and Animal Products.
Volume 2. Quantitative risk assessment. pp. 126.
37.
38.
ANNEXES
39.
40.
VII ANNEXES
Annex 1:
Programme of the EFSA Colloquium
Annex 2:
Annex 3:
Presentations made at the Colloquium
Annex 4:
Slides of Discussion Groups
41.
42.
Annex 1:
Programme of the EFSA Colloquium
EFSA Scientific Colloquium on Principles of Risk Assessment of Food
producing Animals: Current and future approaches
Programme
Chairs:
Co-chair:
Rapporteurs:
Herman Koëter /Juliane Kleiner
Pierre Le Neindre
Michael Gunn, Hubert Deluyker
Thursday 1 December 2005
08.30-9.00Briefing meeting with overall chair and
rapporteurs, discussion group chairs
and rapporteurs
09.00- 12.15
Session 1:
Introductory Plenary session
09.00-09.15
Welcome and Introduction to EFSA
09.15-09.35Risk assessment of biohazards in food and feed with a focus on issues relevant
for risk assessment of animal diseases
Herman Koëter
Jean Louis Jouve
09.35-09.40
Discussion
09.40-10.00
Geographical BSE/TSE Risk Assessment
Mo Salman
10.05-10.25
Risk assessment of animal diseases: import risk assessment and endemic diseases
Matthias Greiner
10.25-10.30 10.30-11.00
Discussion
Coffee/Tea Break
43.
11.00-11.20
Issues related to animal welfare risk assessment
Linda Keeling
11.20-11.25
Discussion
11.25-11.45
Methodologies and challenges in animal diseases and biological risks
11.45-11.50
Discussion
11.50-12.00
Instructions for discussion groups
12.00-13.00
Session 2:
Discussion groups (DG) David Vose
Hubert Deluyker DG 1 and 2
Risk assessment of endemic diseases
Chairs:
Dan Collins (DG 1)
Jörg Hartung (DG 2)
Rapporteurs:
Ivar Vågsholm (DG 1) Mo Salman (DG 2)
DG 3
Risk assessment of import
of animal diseases
Chair:
Philippe Vannier
Rapporteur:
Marion Wooldridge
DG 4
Risk assessment of animal welfare
Chair:
David Morton
Rapporteur:
Lis Alban/Matthias Greiner
All discussion groups should discuss types of data as well as aspects of
data collection, epidemiology, and risk assessment methodology.
13.00-14.00
LUNCH
14.00-16.00
Continuation of DG session 2
16.00-16.30
COFFE/TEA BREAK
44.
Annex 1 – Programme of the EFSA Colloquium
16.30-18.30
Session 3:
REPORT BACK FROM DISCUSSION GROUPS TO PLENARY
16.30-16.45
Report back from DG 1 on endemic diseases
Ivar Vågsholm
Mo Salman
16.45-17.00
Report back from DG 2 on endemic diseases
Marion Wooldridge
17.00-17.20
Discussion
17.20-17.35
Report back from DG 3 on import diseases
17.35-17.45
Discussion
17.45-18.00
Report back from DG 4 on animal welfare
18.00-18.20
Discussion
18.20-18.40
Current activities of DG Research in the
area of animal health and welfare
20.00
Dinner
Lis Alban
Jean-Charles Cavitte
45.
Friday 2 December 2005
09.00-11.00
Session 4:
discussion groups to identify similarities
and differences of various approaches,
aiming at consistency with other areas
09.00-10.00
Evaluation of other discussion groups’ outcome
10.00-11.00
Discussion groups to prepare their conclusions
and recommendations
11.00-11.30
Coffee/TEA break
11.30-13.30
Session 5:
Final Plenary Session - Discussion
and Conclusion
11.30-12.30
Report back to Plenary
12.30-13.30
Discussion, conclusion and
recommendation from the colloquium
13.30-14.30
LUNCH
14.30
Colloquium adjourns
46.
Ivar Vågsholm
Mo Salman
Marion Wooldridge
Matthias Greiner
Annex 2:
Name
Affiliation
Country
Discussion Group (DG)
Dr. Lis Alban
Danish Bacon & Meat Council
DK
4
Prof. Bo Algers
Swedish University
of Agricultural Sciences
SE
4
Dr. Marta Bedriova
State Veterinary and Food
Administration of the
Slovak Republic
SVK
1
Dr. Silvia Bellini
Istituto Zooprofilattico Sperimentale della Lombardia
e dell’Emilia Romagna
IT
3
Prof. Dirk Berkvens
Institute of Tropical Medicine
BE
1
Mr. Francesco
Berlingieri
OIE, International Trade
Department
FR
3
Prof. Sheila BirdMedical Research Council
UK
3
Prof. Crtomir Borko
National Veterinary Institute
SVn
3
Dr. Gianfranco
Brambilla
Istituto Superiore della Sanità
IT
4
Dr. Birte Broberg
Danish Veterinary Service
DK
2
Dr. Sygrid Brynestad
Det Norske Veritas
NO
4
Dr. Denise Candiani
University of Torino
IT
4
Dr. Elisa Carrilho
Agencia Alimentar
PT
1
Mr. Jean-Charles
Cavitte
European Commission
BE
1
47.
Name
Affiliation
Country
Dr. John Daniel Collins
University College of Dublin
IE
1
Dr. Antonio Di Nardo
Istituto Zooprofilattico Sperimentale dell’Abruzzo
e del Molise
IT
4
Prof. Wenche Farstad Norwegian Scientific Committee on Food safety
NO
4
Dr. Maurizio Ferri
National Veterinary Service
IT
3
Dr. Matthias Greiner
Danish Institute for Food and Veterinary Research
DK
4
Dr. John Griffin
Department of Agriculture and Food
IE
1
Dr. Michael Gunn
Central Veterinary Research Laboratory
IE
3
Prof. Jörg Hartung
University of Veterinary Medicine of Hannover,
Foundation
DE
2
Mrs. Ruth Hauser
Swiss Federal Veterinary Office
CH
3
Dr. Per Have
Danish Institute for Food and Veterinary Research
DK
2
Dr. Judith Hilton
Food Standards Agency
UK
2
Dr. Helga Hogasen
NO
3
Dr. Tuula Honkanen-
Buzalski
National Veterinary and Food
Research Institute
FI
2
Mr. Rex Horgan European Commission
BE
4
48.
Annex 2 – Participants at the Colloquium
Name
Affiliation
Country
Dr. Jan Hultgren
Swedish University
of Agricultural Sciences
SE
2
Mr. Jean-Louis Jouve
FAO
FR
1
Dr. Ramon Juste
Basque Institute for Agricultural Research
and Development (NEIKER)
ES
1
Prof. Linda Keeling
Swedish University of Agricultural Sciences
SE
4
Dr. Matthias Kramer
Friedrich Loeffler Institute
DE
3
Dr. Hilde Kruse
NO
2
Dr. Pierre Le Neindre
Institut National de la Recherche Agronomique
(INRA)
FR
4
Dr. Maya MakaveevaMinistry of Agriculture and Forestry
BG
3
Mr. Robert McDowell
USDA-APHIS Risk
Analysis Systems
US
3
Dr. Koen Mintiens
Veterinary and BE
Agrochemical Research Centre
2
Prof. David Morton
University of Birmingham
UK
4
Dr. Endre Pál Nadai
Hungarian Food Safety Office
HU
1
Dr. João Niza Ribeiro Segalab/Agros/Confagri
PT
2
Dr. Jarlath O’Connor
Department of Agriculture
and Food
IE
4
Dr. Wim Ooms
Food and Consumer Product
Safety Authority
NL
4
49.
Name
Affiliation
Country
Dr. Ana Afonso Polyviou
Veterinary Services
CYP
1
Mr. David Pritchard
Department for Environment
Food & Rural Affairs
UK
4
Mr. Darius Remeika
State Food and Veterinary Service
LT
1
Mr. Francisco Reviriego Gordejo
European Commission
BE
1
Dr. Heidi Rosengren
National Veterinary and Food Research Institute
FI
2
Dr. Helmut Saatkamp
Wageningen University
NL
3
Mr. Jose SaezMinistry of Agriculture,
Llorente
Food and Fisheries
ES
2
Prof. Mo Salman
Colorado State University
US
2
Dr. Moez Sanaa
National Veterinary School of Alfort
FR
1
Dr. Savvas Savva
Veterinary Services
CYP
2
Dr. Joseph Schon
Laboratoires LMVE
LUX
2
Dr. Christoph Staubach
Friedrich Loeffler Institute
DE
2
Mr. Marius Taut
Sanitary Veterinary and Food Safety Directorate
RO
3
Mr. Joost Teeuw
RIKILT – Institute of Food Safety
NL
3
50.
Annex 2 – Participants at the Colloquium
Name
Affiliation
Country
Dr. Georgios Theodoropoulos
Agricultural University
of Athens
GR
1
Mr. Razvan Tiru
Sanitary Veterinary and Food Directorate
RO
2
Dr. Jordi Torren Edo
European Medicines Agency
UK
4
Mrs. Sandra Tuijtelaars
International Life Sciences
Institute (ILSI)
BE
2
Mrs. Pirkko Tuominen National Veterinary and Food Research Institute
FI
3
Dr. Ivar Vågsholm
SE
1
Mr. Luc-Henri Vanholme
Federal Agency for the Safety
of the Food Chain
BE
3
Dr. Philippe Vannier
(AFSSA)
French Food Safety Agency FR
3
Prof. Marina Verga
University of Milan
IT
4
Mr. Valentin Voicu
Sanitary Veterinary and Food
Safety Directorate
RO
3
Mr. David Vose
Vose Consulting
BE
1
Mrs. Nicole Werner-Keiss
Food and Veterinary Service
LV
3
Dr. Marion Wooldridge
Veterinary Laboratories
Agency
UK
3
51.
EFSA Staff
Dr. Jan Bloemendal
International and Institutional Affairs
Dr. Frank Boelaert
Team on Zoonoses
Dr. Sandra Correia Rodeia
Panel on Animal Health and Animal Welfare
Ms. Vanessa Descy
Administrative Support
Dr. Wolfang Gelbmann
Panel on Biological Hazards
Dr. Bart Goossens
Dr. Martha Hugas
Mr. Alun Jones
Team Communication
Dr. Juliane Kleiner
Scientific Experts Service
Dr. Pia Makela
Team on Zoonoses
Dr. Maria Pittman
Dr. Oriol Ribo
Ms. Valérie Rolland
Scientific Committee
Ms. Francesca Salvi
Administrative Support
Dr. Jordi Serratosa
Dr. Eirini Tsigarida
Dr. Didier Verloo
52.
Annex 3:
Presentations made at the Colloquium
Providing a
European scientifically sound
approach for food and feed
risk assessment
Herman B.W.M. Koëter
EFSA, Executive Director a.i.
53.
EFSA’s Mission and Tasks [Reg 178/2002]:
… provide scientific advice and scientific and technical support… [Art. 22. 2];
… shall provide scientific opinions… [Art. 22.6];
… collect and analyse data to allow the characterization and monitoring
of risks… [Art. 22.4];
… promote and co-ordinate the development of uniform risk assessment
methodologies [Art. 23(b)];
… commission scientific studies… [Art. 23(d)];
… undertake action to identify emerging risks… [Art. 23(f)].
EFSA’s working environment
 Management Board;
 Advisory Forum;
 Scientific Panels;
 Scientific Committee;
 Scientific Expert Services (SES);
 European Council;
 European Commission;
 European Parliament.
EFSA stands for
 Scientific excellence
 Openness and transparency
 Co-operation
 Independency
54.
Annex 3 – Presentation by Herman B.W.M. Koëter
Scientific activities (work themes):
 Providing scientific opinions, guidance and advice in response to questions;
 Assessing the risk of regulated substances and development of proposals for
risk-related factors;
 Monitoring of specific animal health risk factors and diseases;
 Development, promotion and application of new and harmonized scientific
approaches and methodologies for hazard and risk assessment of food
and feed.
Number of questions and opinions
 Number of questions: 237 (2003); 179 (2004); 238 (2005, until November);
 Number of self tasks : 6 (2003); 19 (2004); 13 (2005 until November);
 Total number of opinions on 31 December 2004: 186;
 Number of opinions on 1 November 2005: >285.
Scientific Meetings (2005)
 Number of scientific expert groups: >100;
 Number of scientific meetings: >500;
 Number of scientists participating in these meetings: > 5000.
Scientific activities (work themes):
 Providing scientific opinions, guidance and advice in response to questions;
 Assessing the risk of regulated substances and development of proposals for
risk-related factors;
 Monitoring of specific animal health risk factors and diseases;
 Development, promotion and application of new and harmonized scientific
approaches and methodologies for hazard and risk assessment of food
and feed.
55.
Distribution of resources
Investing in food science
Animal health & zoonoses
15%
32%
15%
38%
Formal questions
Authorisations
Investing in food science: approaches
 Science Colloquiums (3-4 per year);
 Development of networks and data bases;
 EFSA Self tasks by Panels / SC (external experts assisted
by EFSA staff);
 EFSA Self tasks by Scientific Expert Services-SES (EFSA staff).
EFSA’s Science Colloquiums:
 EFSA Scientific Colloquiums: open scientific meetings to discuss in-depth
topical and sensitive issues related to EFSA’s mission;
 Adequate follow-up by development of Guidance Documents and adoption of
work approaches.
EFSA Colloquium 1
“Methodologies and Principles for Setting Tolerable Intake Levels
for Dioxins, Furans and Dioxin-like PCBs”
18-29 June 2004, Brussels
EFSA Colloquium 2
“Microorganisms in Food and Feed Qualified Presumption
of Safety – QPS”
13-14 December 2004, Brussels
56.
Annex 3 – Presentation by Herman B.W.M. Koëter
EFSA Colloquium 3
“European Food Consumption Database
– Current and medium to long-term strategies”
28-29 April 2005, Brussels
EFSA Colloquium 4
“Principles of Risk Assessment of Food Producing Animals:
Current and future approaches”
1-2 December 2005, Parma
The Colloquium is:
 An interactive event rather than only a passive listening to lectures;
 A platform for scientists to have in-depth discussions on principles and
approaches for risk assessment of food producing animals, taking into
account animal welfare;
 How to apply science-based criteria for animal health and welfare;
 A way to build common views and understanding and for EFSA to pick
your brains.
The Colloquium is not:
 An attempt to agree on the details of a preferred strategy or approach, if any;
 An attempt to finalise a blue print for the work ahead of us;
 A “who is right and who is wrong” discussion.
57.
58.
RISK ASSESSMENT
of BIOHAZARDS in FOOD
Professor J - L. JOUVE
59.
The Approach
(the remit)
p.m. – The Risk Analysis Framework(s)
1 The Risk Assessment paradigm (MRA) for foods
= the “Primacy” of Codex
(Address the general process of MRA)
2 Achievements so far
(Where we are)
3 – Lessons learned – Challenges
(Lessons learned Challenges for the future)
4 – Other types of (Risk) Assessments
(What type of MRA can be performed
What are their applications)
p. m. - The Risk Analysis Framework
The EU General Food Law requires food legislation to be based on Risk Analysis
RISK ANALYSIS
“A process consisting of three components: risk assessment, risk management
and risk communication”
(Codex Procedural Manual, 2004 – 14th Ed.: Definitions of risk analysis terms related to food safety)
Definition is a formal representation (3 components) of a process for controlling
situations where populations could be exposed to a hazard
e.g. Food Safety
60.
Annex 3 – Presentation by J-L. Jouve
Risk Analysis [Codex] : a decision paradigm for Food Safety Governance
Preliminary
activities
Review
Monitoring
RISK
MANAGEMENT
=
The Policy
RISK
COMMUNICATION
=
The Exchange
RISK
ASSESSMENT
=
The Science
Implementation
Options
identification
Options
selection
Caveat - Other Frameworks
E.g. The OIE Framework [Risk Analysis for Animals and Animal Products]
RISK ASSESSMENT
HAZARD IDENTIFICATION
RISK MANAGEMENT
RISK COMMUNICATION
OIE Risk Assessment includes Release assessment
 Exposure assessment
 Consequence assessment
 Risk estimation
E.g. Framing Quality of Life Traits in the Risk Analysis process ( EU – SSC, 2003)
The Risk Profile
The Technology Assessment
Risk Assessment
Physical and
Somatic traits
Benefit Assessment
Psychological
and Social traits
Physical and
Somatic traits
Psychological
and Social traits
Recommendation Analysis
Risk Management measures
61.
1THE RISK ASSESSMENT PARADIGM as applied to MRA for foods
The “primacy” of CODEX
RISK ASSESSMENT
“A scientifically based process consisting of the following steps :
(i) hazard identification
(ii) hazard characterization
(iii) exposure assessment
(iv) risk characterization”
(Codex Alimentarius Commission, 1999 :
Principles and Guidelines for the Conduct of Microbiological Risk Assessment)
i.e. A formal representation of the process
The four steps paradigm
 Hazard identification
 the identification of… agents capable of causing adverse
health effects…
 Hazard characterization
 the qualitative and/or quantitative evaluation of the nature of the adverse
health effects… Includes dose-response assess.
 Exposure assessment
 the qualitative and/or quantitative evaluation of the likely intake...
 Risk characterization
 the qualitative and/or quantitative estimation, including attendant
uncertainties, of the probability of occurrence
and severity of… adverse health effects…
(Codex Procedural Manuel, 2004)
Risk assessment: Principles (WP, 2004 and “Statement of Principle” in CPM, 2004)
RA s hould be soundly based on science
should incorporate the four steps of the RA process
should be documented in a transparent manner
62.
Hazard Identification
Hazard Characterization
[Dose-Response]
Exposure Assessment
Risk Characterization
 Constraints, uncertainties and assumptions having an impact on the risk
assessment should be explicitly considered…and documented in a transparent
manner
 Expression of uncertainty or variability in risk estimates may be qualitative or
quantitative, but should be quantified to the extent that is scientifically
achievable
 RA should be based on realistic exposure scenarios, with consideration of
different situations being defined by risk assessment policy
Also : functional separation RAs/RM, but interactions - statement of scope – selection of experts – data
from developing countries - reporting
2
ACHIEVEMENTS SO FAR
SPS Agreement, art 5.1 : Members shall ensure that their
sanitary or phytosanitary measures are based on an assessment
….of the risk to human health, taking into account risk assessment
techniques developed by the relevant international organizations
Risk Assessment re. Codex – Achievements
 To-date practice
 Formal structure for RA (the 4 steps)
 Explicit procedures for quantitative, probabilistic approaches
(e.g. FAO/WHO guidelines)

A number of large [“food chain”] MRAs ( e.g. Salmonella in broilers and eggs)
 Listeria in ready to eat foods,
 E. coli in hamburgers,
 Vibrio in seafoods,
 Campylobacter in poultry.
63.
“Full” quantitative/probabilistic “food chain” MRAs



“Food chain” : Farm to consumption approach
“Full” : include the 4 steps
Include narratives :
 Hazard identification
(Hazard association with illness – Public health outcomes)
 Exposure assessment (Production to consumption pathways : review of
literature and data)
 Hazard characterization
(Organism, host and matrix characteristics – Disease characteristics Dose/response data - epidemiological and outbreak information
 Risk characterization
(Risk estimation – risk management options assessment)
 Mathematical modeling [Exp. Assess. – Dose/Resp.]
 Probabilistic treatment
 Time (1-3 years) - people (inter-sectors; large team) - budget
E.g. “Food Chain” Exposure Assessment (FAO/WHO)
Model structure
Pathogen Prevalence
Pf
FARM
Cf
Pp
Process
Pr
RETAIL
Cp
CONSUMPTION
HOME
Cr
Estimate of
EXPOSURE
Concentration (Numbers of Pathogen)
Probabilistic approach
 Variables described in terms of distributions
 Monte Carlo simulation
 Monte Carlo analysis allows to simulate variability and uncertainty
in the values
64.
A+B-C=D
+
Normal (3,1)
Normal (6,2)
=
Normal (5,1)
-5 -2
1
4
6
9 12
 R
ange of values for the outcome (“D”) and probability of
occurring can be determined.
 Generates distribution of exposure [range – likelihood]
0.12
Mean Estimate
Dose: Approx 36 CFU
0.10
0.08
0.06
0.04
Conservative Estimate
Dose: Approx 152,000 CFU
0.02
2E+10
2E+09
3E+08
3E+07
4E+06
5E+05
7E+04
Dose
8192
1024
128
16
2
0
0.00
65.
E.g. DR stochastic models [Salmonella (FAO/WHO)]
1,0
Can-norm
(mean)
0,9
0,8
Can-susc
(mean)
Probability
0,7
0,6
US-norm (mean)
0,5
0,4
US-susc (mean)
0,3
0,2
Naive_BP
(mean)
0,1
0,0
0
1
2
3
4
5
6
7
8
Log Dose
Canada : Weibull model – Bayesian techniques to combine feeding trial and epidemiological data
US : Beta Poisson model – Shigella dysenteriae as surrogate to reflect low dose infectivity
E.g. Risk characterization
Risk per serving distribution
 50% reduction
in the average
concentration
per carcass
0.5
Original
After intervention
0.4
0.4
Probability
0.3
Results:
 Annual
expected rate
of illnesses
per 100,000:
 Before: 43
 After: 14
0.3
0.2
0.2
0.1
0.1
0.0
-9.0
-8.0
-7.0
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
Log Risk per Serving
66.
0.0
3LESSONS LEARNED - CHALLENGES
3.1Re. value of a quantitative / probabilistic approach
[Technical dimension]
 A probabilistic approach presents a number of advantages
and offers a number of benefits
 It entails, however, a number of difficulties that need to be
appropriately overcome
PROBABILISTIC APPROACH
 Aims at range of plausible values
 Makes full use of available information
 Appropriate hazard specific information can be incorporated
 Facilitates integration of Microbiology/Toxicology and Epidemiology
 Appreciation of the overall degree of variability and uncertainty
 K
ey sources of uncertainties associated with all aspects of the risk
assessment and their impact
 Includes analysis of sensitivities
 Allows for appreciation of the confidence that can be placed on the analysis
and its findings
 A
ssumptions and their impact – Critical assumptions – Extent to which
plausible alternative assumptions could affect conclusions
Benefits
 Provides the decision maker with a rational framework for risk characterization,
together with a pragmatic evaluation of indeterminations and their impact
 Evaluation of public health impact of specific interventions
(Comparison of efficiency within a common framework)
 Facilitates benchmarking risks - risk / benefit analyses
 Assists in determining research priorities
67.
 Allows for iterative interaction between risk assessors and risk managers on
an ongoing basis
= Promote consistency – Improve – the decision making process
Difficulties
Exposure Assessment:
 Degree of subjectivity associated with model design
 Insufficient information on exposure pathways; (P) and (C) at various stages of
the food chain; consumption patterns
 Interdependency of variables
Dose-Response Assessment:

Model uncertainty
 Functional
form of DR relationship not resolved
 D
ifficulty to endorse one single DR model
 L
imited criteria for selection (goodness-of-fit; others)
1 1
11
0,9
0,9
0 10 1
0,8
0,8
Exponential
Exponential
Truncated
Logistic
Truncated
Logistic
Weibuli
Weibuli
Gamma
Gamma
Log
Logistic
Log
Logistic
Log
Probit
Log
Probit
Data
Data
Frequency
Frequency
0,7
0,7
0,6
0,6
0,5
0,5
Exponential
Exponential
Truncated
Logistic
Truncated
Logistic
Weibuli
Weibuli
Gamma
Gamma
Log
Logistic
Log
Logistic
Log
Probit
Log
Probit
Data
Data
0 01
0 01
0 001
0 001
0,4
0,4
0 0001
0 0001
0,3
0,3
0,2
0,2
0 00001
0 00001
0,1
0,1
00
0000
10.0
10.0
20.0
20.0
30.0
30.0
40.0
40.0
50.0
50.0
BaP
BaPDose
Dose(mg/kg/day)
(mg/kg/day)
60.0
60.0
0 000001
0 000001
1 OE-06
1 OE-06 1 OE-05
1 OE-05 1 OE-04
1 OE-04 1 OE-03
1 OE-03 1 OE-02
1 OE-02 1 OE-01
1 OE-01 1 OE+00
1 OE+00 1 OE+01
1 OE+01 1 OE+02
1 OE+02
BaP
BaPDose
Dose(mg/kg/day)
(mg/kg/day)
Listeria monocytogenes (FAO/WHO) - A comparison of available dose-response
models for describing infection, morbidity or mortality.
NOTE: Caution should be used in interpreting these curves since they are based on different endpoints,
types of data etc. In general, the predictions based on the models show a high degree of uncertainty
and variation.
68.
Log Probability response (infection. illness
or morbidity)
1.00E+00
xx
x
1.00E+02
x
1.00E+04
1.00E+06
x
x
+
x
x
+
x
x
x
1.00E+08 +
x
x
x
+
+
+
Mexican-Style Cheese
+
+ FDA-General
+
+
FDA-Neonates
+
x FDA-Elderly
x
2
3
Chocolate Milk
x Corn Salad
Butter
+
Notermans-IV, Normal
1.00E+12
1
+ Lindqvist and Westöö
x Farber et al.
+
x
x
+
1.00E+10
Buchanan et al.
+
+
x
x
x
+
4
5
6
7
8
9
10
11
12 13
log dose ingested
Haas-1041
Haas-F5817
 Increased complexity – Resource demanding
 More complicated to conduct quality assurance of the calculations
 Apparent reduction of transparency
! Models should represent a concept that is statistically and biologically valid
! Prove useful when sufficient quality data are available
And when the uncertainties are deemed tenable
Challenges
= WHAT NEEDS TO BE DONE
(Research)
 Further investigate opportunities for incorporating more probabilistic
approaches into the risk characterization process
 Improve the scientific basis for probabilistic risk
assessment methodologies
 Quantitative
support for distributions
 Expansion
of existing data bases
 Hazard
specific information
 Pathway
related data
 Dose
/ response data
 Epidemiological
information
69.
 Develop appropriate structures, frameworks, guidelines to allow enhanced
implementation of probabilistic risk assessment
 Validation
of assumptions
 Guidelines
for application and validation of models
 Separation
of variability and uncertainty
 Assessment
of critical endpoints
 Issue
of sensitive subpopulations

Increased use of “mechanistic” knowledge
 Increased
insight into the processes that lead to certain endpoints
 Biological
basis for extrapolation at different endpoints
 Biologically
based models
3.2Re. planning / RA – RM interaction
[Managerial dimension]
STAKEHOLDERS INVOLVEMENT
 “Large” or “major” RA require careful planning to make the best use of
resources vs. utility (Decision-based approach)
 Effective interaction between RA-RM is essential (challenge ?)

Importance of Problem Formulation
 Clarification
of purpose, scope, resources available
 Take
account of public concerns
 Identify
protection goals
 Identify
different RM options
 Importance of team work (coordination – communication)
(role of EFSA vs. national agencies – challenge ?)
 It is also important to involve other stakeholders
But how ? And when ? (challenge ?)
PLANNING OF “MAJOR” RA
(adapted from CFSAN,2002)
1 Potential risks to be evaluated
2 Problem Formulation
70.
3 Feasibility of RA
 A
vailability of data; resource/experience needed/available; technical merit;
other approaches possible)
4 Preparing RA
 P
articipants and team; type, scope, form, complexity of RA; charge, RM
problem, RM questions, key assumptions; resources; time frame;
communication needs
 C
oordination-Communication-Transparency
5 Performing RA
 C
larify scope; collect data, model inputs; refine assumptions for modeling;
data and model inputs verification; model development and audit;
run model; sensitivity analysis; review results; report
6
Follow-up
 P
eer review; stakeholder input;
 I ssuing;
 C
ommunication
 T
eam; audience-needs; strategy; dispatch of info.;
evaluation of communication
3.3SOPHISTICATION Vs. SIMPLIFICATION
[Dimensions of functionality, validity and utility]
 Quantitative probabilistic procedures increase the complexity of MRA
 Under which conditions other (simpler ?) approaches allow to form rational
arguments to support RM decisions, in a timely manner : specificity or risk
assessment ? Validity ? Utility ?
SPECIFICITY OF RISK ASSESSMENT
Q : At what point does an argument based on scientific evidence become a risk
assessment ?
=Scientific examination includes :
 Collection of scientific information (scientific evidence, facts and data,
scientific literature)
71.
 Evaluation of quality - transparency of information collected
 Processing of information
 Structuration – Combination of information
 Analysis
Detailed examination of something complex in order to understand its
nature or to determine its essential features
 Inference
Passing from one proposition or judgment considered as true to
another whose truth is believed to follow from that of the former
– Conclusions that logically follow from the consideration of facts
= Risk Assessment includes
 Collection of scientific information (scientific evidence, facts and data,
scientific literature)
 Evaluation of quality - transparency of information collected

Processing of information
 Structuration – Combination of info. [ Modeling ]
 Analysis
 Inference Assessment * * Analysis AND inference
“Risk” Assessment : Assessment of the “probability of an adverse health effect,
and the severity of that effect”
“Probability” : mathematical or statistical quantification of a phenomenon
– if no measurable data are available, estimates may be used
(Definitions according to Terminology Standardization and Harmonization, Vol. 2 (1999), N° 1-4)
 Risk Assessment = a specific form of scientific examinations
 Added value (functionality) = appropriate combination of
 formal representation of the system under study (modeling)
 rules of inferring probability of adverse outcome of interest
 Appropriate characterization of the system for the purpose of inferring probability
will generally require representing and incorporating variability in various
72.
phenomena, as this is known to be critical in estimating the probability of adverse
outcomes (Paoli, G., ILSI-IAFP Workshop on Using MRA, Prague, Oct. 2005)
=Importance of functionality – validity – utility
VALIDITY
Inputs
 Availability of information – data
 Quality / transparency of evidence
Model(s) Assumptions
 Quality / transparency of analysis
Interpretation
 Quality / transparency of inference
 Basis (vs. rules) for inference of probabilities
= Need to identify which dimensions carry the most weight
= Main challenges :
 data availability
 [prevalence – concentration; pathways; Dose /Response]
 data quality control (criteria – combination)
 verification – validation of models
= Quality assurance is crucial for enhancing the validity of RA
= Need for the development of “Good Evaluation Practice”
 Quality Assurance - main aspects to consider
 Data quality assurance- Data collection
- Data evaluation
- Sorting – selecting data sources
 Weight of evidence
 Model verification – Model validation
 Sensitivity Analysis
 Documentation
 Peer review
73.
Elements for “Good Evaluation Practice” (GEP)
 Clearly defined objectives
 Well specified scenarios
 Identification, collection, categorization of data – verification of compliance
with established criteria
 Appropriately selected models, verified and validated, supported by
quality data
 Level of detail of analysis appropriate to the level of the assessment
 Proper integration of Exposure Ass. and Hazard Char. in Risk Char.
 Presentation of calculations
 Explanation of assumptions – of choice of data
 Expression of uncertainties e.g. in scenarios, in models, in parameters
 Quantification / evaluation of randomness, variability, uncertainty in
model predictions
 Identification of key opportunities for risk mitigation
 Identification of key opportunities for reducing uncertainties
Cf. OECD, 2000: Guidelines for the testing of chemicals
Malmfors et al., 2001: GEP, guidelines proposal (www.toxicology.org/iutox/GEP)
EU-SSC, 2002: The future or risk assessment
FAO/WHO : Draft guidelines on risk characterization of micro. hazards in food
USEFULNESS
 Cf. validity
= Decision / context dependant
 Fitness for purpose
 Address – answer risk managers (the public) questions, concerns
 Different types of risk assessments to meet different needs
 Timeliness – Responsiveness
74.
 Balance resources / added value
 [ Enhanced consistency in decision making - transparency Understanding
– prioritization – decision – communication]
! Meta dimensions
 International position (Codex, WTO…)
 Inclusiveness (Analytic – deliberative process)
Perception at European Level : Prospective Project on QMRA
EFSA, Advisory Forum, April 2005
(www.efsa.eu.int/advisory_forum/adv_meetings/876_en.html)
 QMRA : promising development and necessary
 basis for common and more objective science-based criteria
 strengthen EU position re. Codex, WTO
 evaluate different mitigation options vs. common targets
 increased transparency – improved communication – building trust
 more transparent, systematic, efficient risk management process





account of regional differences
time and resource intensive - many MS cannot yet contribute
duplication of other international work (FAO/WHO, USA, Canada…)
insufficient data availability, quality, data quality control
p
resent difficulty for RM to become aware of benefits of QMRA, to ask the
right questions, to utilize results of QMRA
= Proactive strategy – Funding and support (Challenge)
75.
4Types or Risk Assessments
In addition to “full” MRAs (Codex type), other structured (risk) assessments
may be used to support food safety decisions
Types of Risk Assessments
{ vs. traditional scientific advice }
E.g.
 “Full” quantitative/probabilistic “food chain” MRAs
 Comparative or “Risk Ranking” MRAs
 Stepwise, tiered approach
 Specific product and process MRAs
 Simple models to answer specific questions
 Risk profile
 Qualitative risk assessments
 Epidemiological studies and tools
“Full” quantitative/probabilistic “food chain” MRAs
 Useful tool (FS problem re. entire food chain – optimize interventions – set
performance criteria vs. level of protection)
 Methodology requires further developments :
 “most likely” risk model (exposure pathways – D/R modeling)
 verification – validation
 sensitivity – uncertainty analysis
 Large data requirements :
 more quantitative data
 weaker segments : primary production – consumption – D/R
 value of information analysis
76.
 Large projects :
 time – people – budget – careful project planning
= Undeniable in Codex / WTO context
Comparative or “Risk Ranking” MRAs
E.g. Single agent – multiple food classes
25
20
15
10
5
Food Category
IC
F
AC
V
PC
GV
S
D
RS
M
DF
PM
UM
FF
SC
PF
DS
CR
FS
DM
0
P
SS
Median predicted Cases per 100 million Servings
Predicted Relative Risks Associated with Food Categories - Total Population based
on the Median Predicted Cases of listeriosis per 100 million Servings.
P = Pátê and Meat Spreads
SS = Smoked Seafood;
FS = Fresh Soft Cheese
DM = Deli Meat
PF = Preserved Fish
DS = Deli Salads
SC = Soft Mold-Ripened Cheese
PM = Pasteurized Fluid Milk
UM = Unpasteurized Fluid Milk;
MD = Miscellaneous Dairy Products
RS = Raw Seafood;
PC = Heat-Treated Natural Cheese
V = Vegetable
F = Fruits
AC = Aged Cheese
IC = Ice Cream
 Useful – Basis for priority setting (e.g. research needs; interventions)
 Ability to quantify risks
 Consideration of severity
 (multiple end-points; QALY-DALY)
 Modeling sub-populations
 Common metric
 (public health consequences; economic impact)
 Hazards vs. different risk perceptions
 Consideration of potential for control
 (inherent risk vs. loss of control)
77.
Stepwise, tiered approach
Tier 1 (K, I, D)
Tier 2 (+K, +I)
Tier 3 (+D)
1 – Rough analysis (risk profile – simple model – worst case)
2 – Decompose most relevant steps
 compare different modelling approaches : risk contribution
 identify major risk contributing factors
3 – Describe most risk contributing steps in (full) detail
 introduce probabilistic modelling
 variability and uncertainty analysis
 address data-gaps – generate new data
Specific product and process MRAs
 Targeted to industrial needs






specific product (formulation – process – site)
assess process changes (impact on shelf life; “safety”)
optimize process conditions
evaluate radical product / process innovations
benchmarking
pre-market design
 Actually, an exposure assessment with a threshold level of concern
 Probabilistic approach – Importance of separating variability (distributions)
from uncertainty (novel approaches e.g. second order modeling; optimization
procedures around MC simulation)
78.
(exposure assessment with a threshold level of concern)
Initial contamination distribution
0.2
1.0
0.18
Cumulative probability
0.16
Pilog (Nr)
0.14
Fit raw micro
0.12
0.1
0.08
0.06
0.04
0.02
>0
7-7-5
7-5-8
6-5-7
6-6-5
5-5-6
5-5-5
4-5-5
4-4-5
3-5-4
3-3-5
1-1-5
2-1-5
1-5-2
2-5-3
0-5-1
<0
0-0-5
0
data as
0.8
0.6
@Risk samples
from distribution
0.5
0.2
0.0
0
5
10
distribution
Log (Nr)
15
20
25
30
35
Initial bacterial count (cells / g)
Ambient Temperature(°c)
0>15.6
0>15.6
9
Value x 10°-2
8
7
6
5
Parameter(s)
4
3
2
Predictive
Model
1
0
0
5
10
15
20
25
30
35
values sampled
Repeated each step
Time Spani in transport by Consumer (minutes)
0>15.6
0>15.6
3
Value x 10°-2
2.5
2
1.5
1
0.45
0.5
0.4
0
-10
0
10
20
30
40
50
60
70
80
90
100
0.35
Final distribution
0.3
0.25
0.2
0.15
0.1
0.05
0
-1
0
1
2
3
4
5
6
7
8
Simple models to answer specific questions
E.g.
 Risk assessment of antimicrobial resistance that matches fractional changes in
the prevalence of contaminated poultry carcasses to fractional changes in the
number of campylobacteriosis cases (USA)
 Apportionment of salmonellosis cases to various food sources by matching
strain and phage type proportions (DK)
 Analyses of case-control studies to apportion disease risk to food sources and
behavior patterns
79.
Microbiological Risk profile
Preliminary activities
Review
Monitoring
MRM
- Identification of a MFS issue
- Microbiological risk profile
- Risk assessment policy
- Commissioning MRA
MRA
Risk Communication
- Verifying compliance
Implementation
of MRM options
Identification of available
MRM options
- Giving effect
Selection of MRM options
Risk Management Framework [ Codex ]
 Often an early step in Risk Analysis
 (RM framework: preliminary activities)
 Means to lay out the major aspects of risk management concern
 Means to collect and evaluate prior knowledge on hazard, exposure, adverse
health effects, other aspects of concern
 Provides an intermediate level of details
 (preliminary, more simple form than qualitative MRA)
=Used to identify/assess current options, need for further in depth
assessment, data gaps
= Can be viewed as a preliminary qualitative MRA combined with control
options assessment
=May be used when a “full” MRA is not feasible due to time or data
insufficiency
80.
Suggestion for a microbiological Risk Profile ( Codex)
1 Pathogen-commodity
of concern
2Description of the public
health problem
 description of the pathogen
 description of the disease
(susceptible populations;
annual incidence; outcome of
exposure; severity of clinical
manifestations, long term
complications; treatment
available; % food borne)
 characteristics of food
borne transmission
(epidemiology; etiology; food
char., use and handling; other
foods; frequency and char. of food
borne outbreaks, of sporadic
cases; epidemio. data from
outbreaks; regional, seasonal,
ethnic differences in incidence;
economic impact or burden)
3Food production, processing,
distribution and consumption
(char. of commodity;
description of farm to table
continuum, incl. impacting
factors; what is known about
the risk, how it arises and who
is affected; summary, extent,
effectiveness of current RM
practices; additional RM
strategies)
4Other elements
(internat. trade; agreements;
economic consequences; public
perception of problem and risk)
5 Available information and gaps
(existing RA; scientific
info; source; expertise;
guidance doc.)
6RA need and questions
Qualitative risk assessments
 An assessment continuum :
 Narrative – Narrative with “risk” terminology
 Structured qualitative – Semi-quantitative
 Quantitative-probabilistic with extensive uncertainty analysis
81.
 Factors affecting validity





Availability and quality of information and data
Availability and extent of expertise
Background and biases
Quality and transparency of inference
Agreement on science and control options
 Factors affecting usefulness
 Complexity of question(s) posed
 Time constraints
 Resource constraints
Epidemiological studies and tools
 Epidemiologic data provide RM with information of direct relevance
 Epidemiologic data can be especially valuable e.g. to set priorities, in evaluation
of risk reduction policies, in risk/benefit analysis
 Vigorous effort
 to answer new questions within the framework of epidemiological studies
 to promote the use of epidemiologic studies in regular RA process
 to conduct epidemiological studies for the purpose of RA
CONCLUSION
Critical needs:
 Data [Primary production; Dose-response; Hazard specific]
 Criteria for data quality control
 Methodological research
 Robustness of inference
 Rapid risk assessment approach
 Limits to simplification
 Decision-based management of the RA process
82.
Geographical BSE Risk (GBR)
Mo Salman
Professor and Director
Animal Population Health Institute
Colorado State University
Colorado, USA
[email protected]
83.
What is GBR?
 Qualitative indicator of the likelihood of the presence of one or more cattle
being infected with BSE, at a given point in time, in a country.
History/Background
 A system analysis initiated for EU members in order to avoid the introduction
of the agent form “infected” or “suspected” countries.
 Expansion of the assessment to include all countries participated in exportation
of animals and/or animal products to EU members.
 Standardized form/questionnaire was used to obtain the required data/
information.
 Application by the country but it is on volunteer basis.
 Countries are encouraged to apply if they want to maintain the trade with EU
members.
 Scientific Steering Committee (SCC) of EU initiated the system.
 Comments were received in February 1999 from EU-Member States and in
reaction to the publication of the pre-opinion on the Internet .
 The procedure was first applied in March 1999 to 11 Member States of the
European Union.
 The procedure was refined in:
 July 2000,
 January and November 2002
 EFSA then took the reasonability in applying the method and submission
of written recommendations to country and the commission of risk
management.
Components of GBR system
 External Challenges – Importation of cattle and MBM.
 Stability of the system in the country – Propagation and amplification
of the agent.
84.
Annex 3 – Presentation by Mo Salman
Initial sources
of BSE
20 years
Import of MBM
Import of cattle
challenge
Recycling &
amplification of BSE
Feeding & feed
controls
stability
BSEinfected
cattle
Surveillance
Cattle
exposed
to BSE
Control Crosscontamination
Contamination
of domestic
MBM
Rendering processes
BSE-cattle
proceessed
BSEinfectivity
rendered
SRM use
Risk Assessment for BSE
 World Organization for Animal Health (OIE) recommendation:
 For determination of the BSE status of a country, risk assessment has to be
performed:
 Qualitative RA
- Scientific Steering Committee (EU): Geographical BSE risk („GBR“)
 Quantitative RA
- USA: Harvard Risk assessment
- Canada
- Japan
85.
Country categories (GBR)
GBR I:
Highly unlikely
Argentina, Australia (I), Brazil, Chile,
Iceland, New Caledonia, New Zealand,
Panama, Paraguay, Singapore, Uruguay,
Vanuatu
GBR II:
Unlikely but not excluded
Botswana (I), Colombia, Costa Rica (II),
El Salvador (I), India, Kenya, Mauritius,
Namibia (I), Nicaragua (I), Nigeria, Norway
(I), Pakistan, Sweden (II). Swaziland (I)
GBR III:
Likely but not confirmed or
confirmed at a lower level
Albania, Andorra, Austria, Belarus,
Belgium, Bulgaria, Croatia, Denmark,
Canada (II), Cyprus, Czech Republic,
Estonia, Finland, Former Yugoslav Republic
of Macedonia, France, Germany, Greece,
Hungary, Ireland, Israel, Italy, Latvia,
Lithuania, Luxembourg, Malta, Mexico,
Poland, The Netherlands, Romania, San
Marino, Slovak Republic, Slovenia, South
Africa, Spain, Switzerland, Turkey, USA (II)
GBR IV:
Confirmed at a higher level
United Kingdom, Portugal
Year 2001
86.
Year 2003
Year 2005
GBR and BSE Surveillance
 The system is mainly designed to prevent the introduction of the BSE agent
from high risk countries.
 Little emphasis is on the BSE surveillance in the assessment.
 Current methods and procedures are under revision for future use.

OIE – Ad hoc groups
 BSE surveillance chapter
 BSE country assessment
 Surveillance methods
87.
BSE status
As per the OIE Chapter
“Previous” OIE-chapter
 BSE free country or zone
 BSE provisionally free country or zone
 Country or zone with a minimal BSE risk
 Country or zone with a moderate BSE risk
 Country or zone with a high BSE risk
Provisionally free:
Argentina, Iceland, Singapore and Uruguay
Revised OIE-chapter
 Negligible BSE risk
 Risk assessment: demonstration of appropriate general measures to
manage all risks identified
 Surveillance Type B
 MBM not fed to ruminants for 8 years
 Controlled BSE risk
 Risk assessment: No demonstration of appropriate general measures to
manage all risks identified
 Surveillance Type A
 MBM not fed to ruminants for 8 years
 Undetermined BSE risk
 Does not meet requirements of other categories
Surveillance
 Negligible risk – Type B surveillance
 Detection of BSE prevalence of at least one case per 50,000 adult cattle, CI 95%
 Controlled risk – Type A surveillance
 if no identified BSE cases: detection of BSE prevalence of at least one case
per 100,000 adult cattle, CI 95%
 If BSE detected: “more intensive surveillance method“
88.
Conclusion
 Absence of cases does not mean absence of risk - presence of cases does not
mean everything represents a risk
 Probable that more countries will detect BSE
 Aims and transparent risk assessment are useful
 Implementation of measures and surveillance adjusted to risk
 Trade conditions according to the risk
Acknowledgement
 All SCC members and Ad-hoc scientific working groups since 2000
 Dr. Joachim Kreysa
 Dr. Dagmar Heim
89.
90.
Annex 3 – Presentation by Matthias Greiner
Risk assessment for animal diseases:
import risk assessment
and endemic diseases
Matthias Greiner
Danish Institute for Food and Veterinary Research International
EpiLab, OIE Collaborating Centre for Research and Training in
Population Animal Health Diagnosis and Surveillance Systems
91.
Credit to members of the work package on risk research of the Coordination
Action FMD CSF
Amie Adkin, Anders Stockmarr, Clazien de Vos, Emilya Ivanova, Eric Breidenbach,
Hans Thulke, Helmut Saatkamp, Herman A. van Langen, Jarkko Niemi, Jeroen
Dewulf, Koen Mintiens, Larry Paisley, Lis Alban, Tadeusz Wijazka
Outline
Framework and assumptions of risk assessments (RA)
Quality assurance for RA
RA for transboundary and endemic diseases
TransboundaryEndemic
 ‘risk’ gradient between exporting/
importing country
 ‘risk’ factors within country
 supports international animal
health and food safety
 supports veterinary/public health
decisions
 public health hazards, OIE former
list A diseases
 any condition relevant to animal
health, public health, ecology,
economy, welfare
 foreign data sources
 own data sources
OIE guidelines (animal health and zoonoses)
Codex Alimentarius guidelines (food safety)
Assumptions of the risk analysis process
The given risk problem and (if applicable) management options
can be formulated explicitly
 this requires a consensus among stakeholders on the known or
perceived risk problem
92.
The acceptable level of risk is known
 this requires that risk managers can apply an objective decision criterion or a
criterion reached by consensus
It is possible to evaluate the risk under different management
options objectively
 this requires adequate analytical concepts, data, information and
analysis methods
The appropriate management option can be chosen
 this requires an understanding of the results and assumptions
of a risk assessment
OIE scheme for import risk analysis
Release
assessment
Exposure
assessment
Consequence
assessment
Hazard
identification
Risk
assessment
Risk
management
RISK COMMUNICATION
International rules for import risk assessment
General points
 overall goal is a fair trade and a minimum of restrictions
 restrictions only justified to the extent necessary and only if risks can be
substantiated scientifically
 risk assessment not required where OIE sanitary standards exist and are
adopted by trading partners
93.
Risk assessment is required
 to justify sanitary measures that exceed OIE standards
 when OIE sanitary standards are lacking
More general risk analysis scheme
Canadian Food Inspection Agency Risk Analysis Process
Problem Identification
Risk Assessment
Evaluation of Results
Risk Management Options
RISK COMMUNICATION
Process Initiation
Option Selection
Implementation and Evaluation
http://www.inspection.gc.ca/english/fssa/ris/fracade.shtml
Quality aspects
Ensuring regular conduct of a RA
 agree on risk question and required precision (in writing)
 make sure that scope is clear and realistic with the resources available
General scientific procedures
 both qualitative and quantitative assessments must be consistent with current
scientific knowledge
 assess quality of data and select methods accordingly
 use the data and analysis methods correctly and state assumptions underlying
their use
94.
Reporting and communication
 allow involvement of stakeholders
 report uncertainty of the results
 address the risk problem in the presentation of results
 state resources used to complete the work
Peer-review
 agree on quality indicators for data and information sources for quantitative
and qualitative approaches
 agree on peer-review system, which includes reports not published in peerreviewed journals
(import RA)
Geographical RA for release assessment
 pre-processing and evaluation of data sources such as OIE records and Promed
could be coordinated among institutions concerned with import RA
 document all data used (including unofficial data)
 use consistent methods to estimate the probability of disease and prevalence
in the exporting country
Conclusions
 RAs for transboundary and endemic diseases are principally not different
 The use of data of and information about the exporting country requires
mutual acceptance of all methods involved
 There is demand for quality indicators for both qualitative and
quantitative RAs
 It would be useful to have a peer-review system for RA reports
Credit to members of the work package on risk research of the Coordination Action FMD CSF
Amie Adkin, Anders Stockmarr, Clazien de Vos, Emilya Ivanova, Eric Breidenbach, Hans Thulke,
Helmut Saatkamp, Herman A. van Langen, Jarkko Niemi, Jeroen Dewulf, Koen Mintiens, Larry Paisley,
Lis Alban, Tadeusz Wijazka
95.
96.
Annex 3 – Presentation by Linda Keeling
Issues related to animal welfare risk
assessment
Linda Keeling
Department of Animal Environment and Health,
Swedish University of Agricultural Sciences
97.
Do we need risk assessment?
Housing
Management
Risk
Welfare
Animal
 Legislation is based on risk
 Allocation of resources, good management etc
 Increasing knowledge of how to assess welfare
 Why assess risk (probability) when we can assess the actual welfare state of
the animal?
Welfare risk assessment is necessary for…
 Ethical reasons
 Prevention is better than cure (moral responsibility)
 Scientific reasons
 Identify causal factors and gaps in our knowledge so leading to improved
understanding of animal welfare
- Epidemiological studies
- On farm welfare assessment
 Clarification reasons
 Welfare is a complex concept. Risk assessment is a methodology that could
help get agreement on key issues and set priorities
- Getting consensus
Epidemiological studies
 Health related welfare problems
 E.g. lameness (dairy cattle and broiler chickens)
- Large data bases, identifying relative risk for effects of housing,
management etc. (Manske, 2002, Berg, 1998)
98.
 Behavioural problems that are infrequent and ethically questionable to ‘trigger’
under experimental conditions
 E.g. Vent pecking in laying hens
- Effects of rearing conditions on prevalence of cloacal cannibalism
(Gunnarsson, 2002)
 E.g. Feather pecking in hens
- Questionnaire study (Nicol et al., 2003)
 E.g. Tail biting in pigs
- Decision support system with semantic modelling (Bracke et al. 2002)
On farm welfare assessment
 Focus on quantifying animal-based measures of welfare as they reflect the
‘output’ from the many on-farm ‘inputs’
 Disease, lameness, plumage damage, tail damage, body condition score,
injuries, stereotyped behaviour, slipping, getting up and lying down
behaviour, fearfulness in general, fear of humans, play etc
 Over 50 animal based measures identified in the EU project
‘Welfare Quality’
 When to take the measure? Most efficient to visit farm when risk
is greatest (critical points).
Summary of ‘scientific’ reasons
Housing
Management
Risk
Animal
Lameness
Injury
Fearfulness
Frustration
…
…
Animal
welfare
 Quantifying specific animal-based welfare measures
 How lame? How injured? How fearful? How frustrated when not able to
show nesting behaviour?
 Using different epidemiological techniques to assess risk for many more
welfare relevant measures
 Integrating different measures in a meaningful way
99.
Welfare is a characteristic of the animal
 An animal’s welfare is its state as
regards to its attempts to cope with its
environment (Broom 1986)
 Welfare depends on how the animal
feels. (Duncan 1993)
The animal is the same wherever, but the view of
what matters in terms of animal welfare varies
according to many factors, not only where you live…
Animal welfare means different things to different people
 Related to the normal and satisfactory functioning of the animal
 Emphasis on coping with the situation.
 E.g. Broom definition (vets, animal scientist)
 Depends on subjective experiences
 Emphasis on feelings and emotions
 E.g. Duncan definition (ethologists, consumers)
 Based on the ’nature’ of the species
 Emphasis on natural behaviour
 E.g. Ecological (consumers and politicians)
All animal-based concerns
Risk assessment
Risk management
 What is more important health or behaviour?
 Can’t answer - both are important
 Which is the bigger welfare problem lameness or fearfulness?
 Can’t answer - both are important
100.
 Which is the greater welfare problem – a 30% chance to be lame or a 60%
chance of living in a state of fear?
 Depends how lame or how fearful?
 30% chance to be mildly lame compared to a 60%
chance to be very fearful?
 Depends for how long?
Risk
assessment
risk management
dialog
 30% chance of being mildly lame during 90% of life or
60% chance to be very fearful during 10% of life?
Risk
management
I would probably put the priority on reducing lameness
 30% chance of being mildly lame during 10% of life or
60% chance to be very fearful during 90% of life?
I would probably put the priority on reducing fearfulness
Summary of ‘clarification’ reasons
 Keeping it animal centred, using animal-based measures, keeps the focus
on welfare
 Putting figures on welfare helps get consensus between different people and
different disciplines so risk assessment can go to risk management
RA/RM dialog
Housing
Management
?
Animal
Put figures on the
relative risks
?
Risk
?
??
?
?
??
Improve knowledge
how to integrate
Lameness
Injury
Fearfulness
Frustration
…
…
Animal
welfare
Extend, improve and get consensus
on list of animal-based measures
101.
A personal view of how to proceed
 Greater emphasis on identifying and developing animal-based measures of
welfare problems
 All types of welfare problems; health, behaviour and psychological
 Must have an animal centred approach
 Expand epidemiological-type work to quantify risk factors for these
welfare problems
 Find a common currency to integrate problems (animal equivalent of ‘disability
adjusted life years’)
 Modelling using e.g. ‘Delphi’ or ‘Conjoint’ methodologies to get a balanced
expert opinion
 Developing techniques to investigate how the animal itself weighs different
aspects e.g. operant techniques
 Start research on the positive side of life. How are bad things mediated by
good things in life.
 More research on animal cognition
102.
Methodologies and challenges
in animal diseases and foodborne
biological risks
David Vose
Vose Consulting
www.risk-modelling.com
103.
Perspective of an independent risk analyst
Vose Consulting provides risk analysis training and consulting in animal health
and food safety
 As well as insurance, finance, engineering, mining, banking, business,
forecasting...
 So we compare with other fields and use their techniques
David Vose is:
 Author/editor of OIE antimicrobial risk guidelines
 OIE animal health Murray guidelines based on Vose (2000) and courses
 Editor/author of WHO/OIE risk characterisation guidelines
 Co-author of Danish salmonella risk attribution
 Risk analyst for FDA resistant-Campylobacter
 Been involved in several F2F risk assessments
Foundation for a risk assessment
A risk assessment is:
 A tool to help the decision-maker differentiate between possible options
 A rational, probability based discussion of the effects of those options
 Uses the best available information
 Even if its very poor or incomplete
The choice of approach is constrained by many factors…
104.
Annex 3 – Presentation by David Vose
Constraints of a risk assessment
Ranked decision questions
Available data
Timeframe
Quality of assumptions
Guidelines?
Risk assessment design
Qualitative to quantitative
Answer questions
Guidelines: guides not rules or recipes
OIE animal health code now very good:
 Use to be too prescriptive
 Now very general, but supported by Noel Murray guidelines
Codex Alimentarius microbial risk guidelines
 Document just basic principles, but good
 Has been over-interpreted, e.g. separation of assessment and management
 Drew inspiration from toxicological risk assessment (EPA) which has important
differences with microbial
 Over-emphasis has been placed on components of risk assessment, rather
than its usefulness
WHO/FAO guidelines and example analyses
 Example assessments
decision support
described
modelling
techniques
rather
than
 Perpetuated exposure/ dose-response necessity for a risk assessment
 Nonetheless showed the difficulties involved
 Draft risk characterisation guideline encourages greater freedom to problem solve
105.
Animal import risk assessment
Risk = f[Probability of introduction & size of impact(s)]
Well developed techniques available
to calculate probability of exotic
pathogen introduction
But they may be quite wrong!
Exporting country
Prevalence estimate very burdensome
for low prevalence countries.
E.g. €2M/positive BSE case in France
Need new techniques
Importing
country
Extent of disease spread very difficult
to model in a naïve population
May need to accept more qualitative approach
Estimating a low prevalence
Standard method
 Random sample: s infected out of n tested. Can extend to herd sampling
 Prevalence estimate = s/n with uncertainty and corrections for test performance
 Need a lot of samples:
 e.g. for 50,000 negative tests with perfect test, we are 95% confident
prevalence is below 1/17,000 to 1/21,700 (depending on statistical method)
BSurvE: ‘freedom’ from BSE or prevalence from Massey University, approved by OIE
 Offers a weighted approach based on clinical presentation and age
 Clinical suspects, fallen stock, casualty slaughters, healthy slaughter stock
 Weighting given to average of population
 Specify “design prevalence”, for example, want to be 95% confident that
prevalence is less than 0.1 per 10,000 animals in standing population
 Calculate a “points score” required to achieve this goal
 Use past accumulated surveillance results plus current activity to calculate
points already scored
From Wilesmith et al presentation: “National BSE Surveillance – A Method for Analysing and
Interpreting the Data”
106.
 When reach required score, have achieved objective
 Then points can “expire” at (say) 5% per year, to account for population turnover
 Ends up conservative because of method of combining cohorts
Danish EpiLab method: use of non-survey data to demonstrate ‘freedom’
 Based on comparing zero and ‘design’ prevalence
 Incorporates all available evidence but requires considerable subjective input
 Has a neat way of incorporating previous years’ observations
Probability of pathogen entry
Standard method OIE
 Widely recognised epidemiological problem, but not addressed
 Animals in a consignment assumed independent
 Diagnostic test results assumed independent
 May greatly over-estimate security of an import control
Example (from Murray OIE guide)
 n animals in a consignment randomly drawn from a population with prevalence p
 Test applied with sensitivity Se (ignoring specificity)
1–p
 Probability an animal tests negative given its not infected =
1 – pSe
1–p
 Probability all animal test do same =
n
1 – pSe
 Probability something else happens =
1–
1–p
n
1 – pSe
But this assumes that each infected animal gives another chance to detect
 If animals share same infection onset they may all have very high, or low,
probability of detection
107.
 Diagnostic test performance needs to be more population based, emphasis on
performance in a group
 Need research
Estimation of pathogen spread and impacts
Standard method OIE
 Consider a number of possible scenarios and estimate the impact
 Guestimate the probability of each scenario
 Makes a lot of sense, flexible enough to consider various impacts
— Economic, social, environmental, etc
Almost never publicly done
 Regulatory agencies stop at estimating probability of agent introduction
 Half of the equation, not transparent
Biosecurity Australia draft guidelines
 Incorporates scenarios in OIE style
 Uses a semi-quantitative method to evaluate and rank outbreak scenarios
 Reasonably transparent
Microbial risk assessments
In past have proven of very limited value
 Farm to fork
 Take a long time to produce (example)
 Require unrealistic amounts of data
 Fail to account for data quality at beginning (e.g. dose-response uncertainty)
 Ill-defined purpose (decision questions)
108.
Moving towards more focused assessments
 FDA risk assessment (figure)
 Danish salmonella attribution (figure)
 Need more of this approach
Could be valuable to expand our horizons
 Include pathogen produced during life of food-producing animals, not just
transported on meat/dairy product
 Not one pathogen at a time (example)
 This would possibly allow us to see multiple points of benefit of certain actions
 Develop a Probability-Impact table to get an overview of food pathogen and
their management
Completion times of some farm-to-fork QRAs
Havard BSE
Final report
CVM Campy
Final report
FSIS E Coli
Draft report
FDA Listeria
Being revised
USDA Vibrio
Draft report
US FSIS SE
Final report
Jan-96
Jan-97
Jan-98
Jan-99
Jan-00
Jan-01
Jan-02
109.
Fluoroquinolone-resistant Campylobacter risk assessment
Section 1
Campylobacter culture
confirmed cases observable
in US population
Model:
Contaminated carcasses after slaughter
plant * probability = affected people
Section 2
Total number of Campylobacter
infections in year
in US population
Section 3
Number of those with
Fluoroquinolone-resistance from
chickens and administered
Fluoroquinolone
Section 4
Number of Fluoroquinolone
resistant Campylobacter
contaminated chicken carcasses
consumed in year
Section 5
Using the model to manage risk.
Measuring the level of risk.
Controlling the risk.
Danish Vet Service Salmonella attribution
“A Bayesian Approach to Quantify the Contribution of Animal-food Sources
to Human Salmonellosis” - Hald, Vose, Koupeev (2002)
Eggs
Travel
97.5% percentile
Outbreak
Mean
Pork
2.5% percentile
Imported poultry
Imported pork
Broilers
Imported beef
Turkeys
Ducks
Beef
Unknown source
0
200
400
600
800
1,000
1,200
1,400
Estimated number of cases of human salmonellosis in Denmark in 1999 according to source
Model ranks food sources by risk. Easily updateable with each year’s data. Bayesian update improves
estimate and checks validity of assumptions.
110.
Microbial risk assessments
Vet
Vet
Pharma
company
Mum-to-be
Vet
Vet
Risk analyst
111.
Dose-response for Salmonella
1.0
Average Probability of Response
0.9
x
0.8
x
0.7
0.6
x
For a 50% probability of
infection one needs a dose
between 30 and 50000 cfu
0.5
x
0.4
x
0.3
x
x
0.2
x
x
0.1
x
0.0
0.0
x
Naive-beta Poisson
USDA (normal)
USDA (susceptible)
Health Canada (normal)
Health Canada (susceptible)
Outbreak exponential
Outbreak beta-Poisson
x
1.0
2.0
6.0
Dose of 100 cfu has
between 1% and 65%
probability of causing
infection
7.0
8.0
Dose-response for Vibrio parahaemolyticus
Data from healthy adult males
Probability of Gastroenteritis
1
0.1
0.01
Sanyal et al 1974
Aiso 1963
Takikawa 1958
Beta-Poisson
0.001
0.0001
0.00001
1
112.
2
4
6
log10 dose of Vp
8
10
12
9.0
Dose-response for Listeria monocytogenes
From Jean-Louis Delforge’s presentation
Log Probability response (infection. illness
or morbidity)
1.00E+00
xx
x
1.00E+02
x
1.00E+04
1.00E+06
x
x
x
+
x
x
x
x
x
1.00E+08 +
x
x
x
x
+
+
1.00E+10
+
Buchanan et al.
+
+
x
x
x
+
+
+
Butter
+
Mexican-Style Cheese
+
+ FDA-General
+
+
FDA-Neonates
+
x FDA-Elderly
x
2
3
Chocolate Milk
x Corn Salad
x Farber et al.
+
Notermans-IV, Normal
1.00E+12
1
+ Lindqvist and Westöö
4
5
6
7
8
9
10
11
12 13
log dose ingested
Haas-1041
Haas-F5817
Transparency of microbial risk assessments
Transparency is accepted as essential

As a result we list:
 All assumptions
 All approximations, substitutions
 Data sources
 References, …
 Then we produce quantitative results, distribution and sensitivity plots, etc.
Problem remains for decision-maker to gauge how reliable the results are
 Can read a list of assumptions
 But the connection to the precision of the model is not evident
Possible solution: develop a scoring system
 Risk assessors, scientists review assumptions
 Score each assumption from “Strong” to “Weak”
113.
 Metric aggregates these scores to provide an overall reliability score
 PhD project, student with VAR/CODA in Belgium
Possible future of risk modelling
Move away from spreadsheets with basic Monte Carlo
 Two-dimensionality limiting
 Difficult to follow model structure
 Vose Consulting in last stages of developing a C++ food safety ToolPak
More use of Bayesian Belief Networks
 Analytica makes model structure more apparent

WinBUGS could be developed into a commercial product
 Allows multi-dimensional arrays
 Advantage of allowing incorporation of disparate data
 Allows automatic incorporation of uncertainty and randomness
 Can make modules
 Currently software is difficult to operate
114.
Instructions for Discussion Groups
Hubert Deluyker
EFSA, Scientific Expert Services
115.
Objectives
Identify aspects to be considered in the conduct of risk assessments (RA) on
micro-organisms and animal welfare
 methodology
 data needs, collection, analysis
 expertise and other resources
 communication of results
Organisational Details
1st DAY
12.00-13.00
14.00-16.00
Discussion Groups (DG) Session
16:30-18:10
Plenary Session
with Four parallel groups
DG reports (15’) & discussion (10’)
18:20-18:40
2
nd
Eur. Commission DG Research
Day
DG Session
9:00-11:00
Discuss outcome plenary session
10:00-11:00
Prepare conclusion & recommendations
Final Plenary Session
11:30-12:30
DG report back (15´)
12:30-13:30
Discussion, conclusions, recommendations
Meeting package
 Hand-outs of presentations
 Discussion group briefs and participant list
116.
Annex 3 – Presentation by Hubert Deluyker
 Additional background information
 Draft Discussion Paper on The Decision Process regarding the Conduct of
Risk Assessments involving Micro-organisms in Feed, Animals, and Food
derived from Animals
DG 1 + 2
Risk Assessment for Endemic Diseases

Methodology
 General principles, specific elements
 Qualitative versus quantitative
 Rationale for ‘farm-to-fork’ approach
 Use of previous risk assessments
 Data
 Quality requirements: existing data
 How to deal with missing data
 Expertise and other resources
 Fitness for purpose
DG 3
Disease Import Risk Assessment
 Methodology
 Experience with OIE guideline
 See also DG 1 & 2:
- use of previous risk assessments
- qualitative versus quantitative
 Data
 Quality requirements: new data collection systems
117.
 Fitness for purpose DG 4
Animal Welfare Risk Assessment
 Methodology
 Discuss needs and options
 Determine hazards

Data
 Parameters
 Data sources
 Other aspects: quality, missing information
Discussion groups 2nd day

Identify similarities and determine differences
 Methodology
 Data
 Expertise
 Conclusions and recommendations
After the Colloquium
 Draft summary report of colloquium to be prepared by rapporteurs (01/06)
 1st review by DG chairs and rapporteurs (02/06)
 Review of revised draft by all participants (04/06)
 Publication of summary report and power point presentations on EFSA website
(05/06) and in EFSA Science Colloquium Report Series (06/06)
EFSA Guidance Document
118.
Current activities of DG Research
in the area of animal health
and welfare
Dr Jean-Charles Cavitte
European Commission DG RTD-E3
119.
Presentation outline
 FP5- FP6
 FP6 Work Programme
 Thematic Priority 5 Food Quality and Safety
 Scientific Support for Policies
 FP7 general outline
 Food, Agriculture and Biotechnology
 Technology Platforms
 ETP Global Animal Health
Community Research Programmes

Objectives (Article 163 Amsterdam Treaty)
 to strengthen the European research potential
 to promote the competitiveness of European industry
 to support Community policies
 From FP 5 (1998-2002)
Society’s Needs
to ERA and FP 6 (2003-2006)
Integrating European Research
Priority Thematic Areas
Anticipating S/T Needs
Structuring the ERA
Strengthening the
Foundations of ERA
Key Actions
RDT
Generic Activities
Support for Infrastructure
Problem-solving Approach
120.
Towards a European Research Policy
Annex 3 – Presentation by Jean-Charles Cavitte
Animal Health
5th FP (1998-2002)
KA2
control of infectious diseases
humans + animals
45 projects:
e.g. CSF, FMD, ASF, BT, AI
KA5
sustainable agriculture
animal health & welfare
(non infectious diseases)
6th FP (2003-2006)
TP 5 Food quality & safety
concentrate on fewer topics
MED-VET-NET Network on zoonoses
EPIZONE Network on epizootic diseases
EADGENE Network on animal genomics
Scientific Support to Policies
underpin formulation/
implementation
e.g. CSF/FMD/AI
INCO
KA2 – Control of infectious diseases – livestock
Problem solving approach
Objectives and priorities in infectious diseases of livestock
 provide scientific and technical basis in support of Community rules and
policies - adaptation of tools to the latest scientific developments
 diseases of major economic importance
 zoonosis
Main topics
 Vaccines, vaccine strategies, marker vaccines
 Pathogenesis
 Epidemiology
 Risk assessment methods
 Antibiotic resistance
121.
KA2 – Infectious diseases
Livestock & Aquaculture
45 projects
 FMD
 Brucellosis
 CSF
 TB and ParaTB
 ASF
 Clostridia
 BT
 E coli
 AI
 Salmonella
 BVDV
 Nematodes
 TGEV
 …
Key Action 5 Agriculture
42 projects
 Welfare : transport, lameness, …
KA2 – Infectious diseases
Livestock & Aquaculture
Examples
 Bluetongue and other Culicoides-borne diseases threatening the EU:
Identification of vulnerable areas by surveillance and GIS modelling to aid risk
assessment (QLK2-CT-2000-00611)
 Development of a safe, efficacious bluetongue virus vaccination strategy in
Europe (QLK2-CT-2001-01722)
 Appraisial of the zoo-sanitary risks associated with trade and transfer of fish
eggs and sperm (QLK-CT-2002-01546)
 …
122.
FP6 (2003-2006)
Scientific support
to policies
Citizens & governance
Sustainable development ...
Food quality and safety
Aeronautics and space
Nanotechnologies ...
Information society technol.
Life Sciences, genomics &
biotechnology for health
Structuring the ERA
Research &
Innovation
Training &
Mobility
Research
Infrastructures
Science &
Society
New and emerging
S/T needs
SME activities
International cooperation
JRC
Strengthening the
foundations of ERA
Food Quality and Safety
Fork-to-Farm
Food
intake
Production
Processing
Safe,
high-quality
foods
Health and
well-being of
Consumer
Environmental
factors
685 M€ over the four years (EU15)
123.
Networks of Excellence
 New instruments under FP6
 Networking; strategic scientific integration; spreading excellence (training,
communication)

3 NoEs of interest:
 MED-VET-NET: zoonoses
 EPIZONE: epizootic diseases
 EADGENE: genomics
MED-VET-NET: zoonoses
 To develop a network of excellence for the integration of veterinary, medical
and food sciences, in the field of food safety, in order to improve research on
the prevention and control of zoonoses including food borne diseases
 16 partners (incl. 1 SME) from 10 MSs
 From November 2004; 5 years
 Virtual Institute
 Four workpackages: Epidemiology, Host-Microbe Interactions, Detection/
Control and Risk Assessment.
EPIZONE: epizootic diseases
 Major infectious diseases in livestock
 Durable integration of scientists in health and production of animals to
improve research on preparedness, prevention, detection and control of
epizootic diseases
 4 themes:
 diagnostics,
 intervention strategies,
 risk assessment (e.g. WP on Campylobacter; preharvest modules;
integration of socioeconomics in RA)
124.
 Develop and implement strategic integration activities: establishment of
priorities in scientific activities, strategic review and planning
 15 partners, China, Turkey and 1 SME
 Negociation phase
EPIZONE: risk assessment theme
 4 intended Work Packages:
 Standardisation of import risk assessment
 European online database on epizootic diseases as an early warning system
 Decision support system for CSF
 Impact of environmental effects on the risk of the occurrence of epizootic
diseases in Europe
EADGENE: genomics
 European Animal Disease Genomics Network of Excellence for Animal Health
and Food Safety
 Genomics of host-pathogen interaction in animals (ruminants, pigs, poultry
and farmed fish), leading to new opportunities for diagnosis, intervention and
selective breeding
 Potential application of research as model for human diseases
 13 partners in 10 countries
125.
FP6 (2003-2006)
Scientific support
to policies
Citizens & governance
Sustainable development ...
Food quality and safety
Aeronautics and space
Nanotechnologies ...
Information society technol.
Life Sciences, genomics &
biotechnology for health
Structuring the ERA
Research &
Innovation
Training &
Mobility
Research
Infrastructures
Science &
Society
New and emerging
S/T needs
SME activities
International cooperation
JRC
Strengthening the
foundations of ERA
Scientific Support to Policies
 1st call:
 FMD, CSF, TB and ParaTB, fish diseases
 3rd call:
 Diagnosis List A diseases, networks FMD and CSF, Iridoviruses in fish, Viral
diseases in bees
 4th call:
 Avian influenza, Swine influenza, ETP GAH
 5th call:
 A: bluetongue
 B: dedicated call on Avian / Pandemic Influenza
126.
SSP1
 CSF Vaccine & WILDBOAR: Epidemiology and control of classical swine fever
(CSF) in wild boar and potential use of a newly developed live marker vaccine
 FMD Impro_Con: Improvement of foot and mouth disease control by ethically
acceptable methods based on scientifically validated assays and new
knowledge on FMD vaccines, including the impact of vaccination
 VENOMYC: Veterinary network of laboratories researching into improved
diagnosis and epidemiology of mycobacterial diseases
 PANDA: Permanent network to strengthen expertise on infectious diseases of
aquaculture species and scientific advice to EU policy
Influenza (animals)-1
 5th FP:
 Aviflu (shared cost project; 10.2002-9.2006): dev/validation of detection
methods; development of quantitative transmission model; improvement
AI vaccines; study of pathogenesis
 ESNIP (CA; 12.2000-2.2004): European surveillance network for Influenza in
pigs; diagnostic/epidemiology
 6 th FP SSP3:
 Healthy poultry (11.2004-10.2007): i.e. dev of new strategies for prevention,
control and monitoring of epizootic diseases of poultry particularly AI and
analyse them; identification and quantification of risk factors for introduction
and spread of AI at regional and farm level; GIS toolboxes for risk
assessment
 LAB-ON-SITE (11.2004-01.2008): improved laboratory and on-site detection
of OIE list A viruses in animals and animal products
Influenza (animals)-2
 6 th FP SSP4:
 FLUAID: support to management of AI crisis in poultry; vaccines and
companion tests; antigenic drift; virus transmission and persistence
 ESNIP2
 Technology Platform for Global Animal Health: diagnostics and medicines
127.
 6 th FP P5-call 4:
 EPIZONE (NoE)
 6 th FP SSP5-B: dedicated call AI/Pandemic Influenza due to be published in
December; short period to apply
SSP5-B: dedicated call on Influenza (animals)
 Tasks to fill urgent gaps, expected to provide crucial deliverables in relatively
short perspective, to serve as foundation for new and longer term research
investments in the years to come
 AI vaccine development (conventional/new generation;
large scale administration)
 Improved diagnosis and early warning systems
 Ecology and pathogenesis
 Migratory birds (multidisciplinary network; ornithology/testing/data
processing/risk evaluation)
 AI virus survival (likelihood of persistence/restocking or other
control measures)
 Reinforcement of CRL and NRLs network
 Technology transfer and training particularly for INCO target countries
FRAMEWORK PROGRAMME 7
General Outline
2007-2013
Commission Proposal
What’s new ?
Main new elements compared to FP6:
 Management:
 ERC (Basic Research)
 Logistical and administrative tasks transferred to external structures
 Simplification of procedures
128.
 Annual budget doubled
 Total: EUR 5 billion ►10 billion per year
 Basic research (~ EUR 1.5 billion per year)
 Joint Technology Initiatives, Research Infrastructures
FP7 Specific Programmes (2007-2013)
Cooperation – Collaborative research
Ideas – Frontier Research
People – Human Potential
Capacities – Research Capacity
+
JRC (non-nuclear)
JRC (nuclear)
Euratom
Cooperation – Collaborative research
9 themes
1. Health
2.Food, Agriculture and Biotechnology
3. Information and Communication Technologies
4. Nanosciences, Nanotechnologies, Materials and new Production
Technologies
5. Energy
6. Environment (including Climate Change)
7. Transport (including Aeronautics)
8. Socio-Economic Sciences and the Humanities
9. Security and Space
129.
FP7 budget (€ billion, current prices)
Cooperation 4.734
JRC 1.817
Capacities 7.486
Cooperation 44.432
People 7.129
Ideas 11.862
FP7 2007-2013 ‘Cooperation’ budget
I. Cooperation
Budget (€ million, current prices)
1. Health
8317
2. Biotechnology, food and agriculture
2455
3. Information society
12670
4. Nanotechnologies, materials and production
4832
5. Energy
2931
6. Environment
2535
7. Transport
5940
8. Socio-economic research
9. Security and space
Total
* Not including non nuclear activities of the Joint Research Centre: €1 817 million
130.
792
3960
44432*
Theme 2:
“Food, Agriculture and Biotechnology”: 3 pillars
 Sustainable production and management of biological resources from land,
forest, and aquatic environments
 ‘Fork to farm’: Food, health and well being
 Life sciences and biotechnology for sustainable non-food products and
processes
Food, agriculture and biotechnology research Objectives
 Build a European Knowledge-Based Bio-Economy (KBBE)
 Respond to social and economic challenges:
 sustainable food production
 food-related disorders
 infectious animal diseases
 agriculture/fishery production and climate change
 high quality food, animal welfare and the rural context
 Support CAP, CAHP and CFP
 Involve all stakeholders (incl. industry) in research
 Respond quickly to emerging research needs
More information
 EU research: http://ec.europa.eu/research
 Seventh Framework Programme:
http://ec.europa.eu/research/future/index_en.cfm
 Information on research programmes
and projects: http://www.cordis.lu
131.
Technology Platforms:
Policy Rationale
 Contribute to Competitiveness (Lisbon goal)
 Boost research performance
 ERA, 3% target
 Positive impact on other Community policies
 Concentrate efforts and address fragmentation
Central Concept
Unite relevant stakeholders around:
 A common “VISION” for the technology concerned.
 Industry, public authorities, research community, regulators, civil society
and consumers
 Mobilisation of a CRITICAL MASS of private and public research and
innovation effort.
 Definition of a STRATEGIC RESEARCH AGENDA including education, training,
communication and dissemination.
3 Main Stages
 Stage 1: Stakeholders get together to agree the need for a TP,
prepare the vision paper
 Stage 2: Stakeholders define a Strategic Research Agenda
 Stage 3: Stakeholders implement the Strategic Research Agenda
Global Animal Health
 Promote the development and distribution of effective tools to control animal
diseases of major importance to Europe and the rest of the world.
 Improve animal health and welfare, food safety, human health, market access
and contributing to achieving the Millennium Development Goals
132.
 Stakeholders: IFAH (animal health industry); Copa-Cogeca (farmers);
EuropaBio; FVE (Veterinarians); research organisations; universities; OIE;
FAO; ILRI; consumers; EFSA, CVOs; EMEA; HMA; IABs
 Vision paper “ETP Global Animal Health” (December 2005)
 Strategic research agenda: in preparation
 Website: http://www.europa.eu.int/comm/research/biosociety/index_en.html
 Commission contact: [email protected]
Conclusions
 Past and on-going research projects contributing to advances in disciplines
supporting “assessment” of “risks” of animal “diseases” and their “control”
(ecology; epidemiology; diagnostics; therapeutics…)
 FP7: forthcoming opportunities
 Networking/integration/involvement of stakeholders: key words
133.
134.
Annex 4:
Slides of Discussion Groups
Discussion Group 1
Endemic DiseaseS
Day-2-results
135.
DG 1 + 2
Risk Assessment for Endemic Diseases
 Methodology
 General principles, specific elements
 Qualitative versus quantitative
 Rationale for ‘farm-to-fork’ approach
 Use of previous risk assessments
 Data
 Quality requirements: existing data
Definition/clarification
 Control strategies if any
 Sets the scenario and context in which QRA will be done
 Endemic defined in space and time
 Space defined as region with meaningful biological/political boundaries
 Incidence over time
 Diseases – interpreted in broadest sense
 Clinical disease, contagious, infective, food borne disease and zoonosis
 Non-infectious metabolic and genetic
Methodology
 RA outcome - the answer risk manager receives defines the process
 Dialogue between RM and RA about outcome as it might change during
the process
 General principles following from the dialogue
 Need a clear case definition – is it the disease or infection that is the point
of interest ?
 Purpose to enable to separate risk management options and to
assess them
136.
Annex 4 – Slides of Discussion Group 1
 Linked to cost vs risk benefit analysis, cost effectiveness analysis
– In food law economics is excluded
– OIE outline consequence assessment included
 Qualitative versus quantitative – continuum
Case study - BSE
 Risk profile
 Available facts on hazard identification, characterisation, exposure assessment
 Outcome – risk characterisation (generic morbidity, mortality, letality)
 Qualitative vs quantiative
 Ideally both but always qual.
 Farm to fork
 Linear approach – end and beginning – global HACCP
 Quality and availability of data
 An idea of prevalence - consumer risk and incidence for the course of
the epidemic
 Strategy uncertainty
 Worst case assumption ? is this RM
 Research priorities
 Sometimes RA not feasible
 Expertise and resources
 Bayesian approach for backcalculation, prevalence and incidence estimation
 Multidisciplinary teamwork
 Communication RA and RM responsibility of both
137.
Case study - salmonella
 Elements
 Risk profile
– clarify the impact of salmonella shedding (clinical disease, public health)
– Options for risk reduction
 Salmonella complex epidemiology – problem to define dose response
– Qual models more useful,
• quantitative requires large amounts of data
• carefully monitored interventions
 Farm to fork
– Black boxes/ marginal models useful
 Use of previous risk assessments
 Critical use helpful – avoid reinventing the wheel
 Data both prevalence and bacterial load
 Studies of risk factors
– Case control studies
– Outbreak investigations
• Indicate dose –response
 Missing links – data gaps
– No missing data only wrong models
• No consensus on this
 Expertise - multidisciplinary
Case study - brucella
 Elements to consider
 Outcome – best strategy for eradication – time, cost, public health
– Test slaughter with/without vaccination
 Qualitative RA to be complemented with quantitative RA
 Farm to fork – possible to produce cheese from unpast milk, occupational
health
 Critical use of previous RA - helpful
138.
 Data
 Structure of industry – epidemiology and history of disease
 Surveillance and available diagnostic tests
 Expertise - epidemiology and dz control
 Continuous dialogue between RA and RM during the process
 Black box
 Smoking and cancer – epidemiology of a black box process
139.
140.
Discussion Group 2
Endemic DIseasES
Day-2-results
141.
Risk managers and risk assessors
 The
��
primary tasks should come from risk managers.
��
A dialogue should be established between risk managers and risk assessors
to construct the questions and issues to be addressed.
��
Risk managers should be the decision-makers in terms of taking action from
options provided by the risk assessors.
��
Periodic re-evaluation of risk management decisions requires surveillance data.
Black Box
 The
��
intention is appreciated but the use of this term can mislead – it is not
helpful term.
 The
��
model should be explained in simple terms and it is the responsibility of
risk assessors to communicate the model to users and stakeholders.
Wildlife interface
��
A risk-based approach will require epidemiological data, but data collection is
an issue separate from the risk assessment process.
��
Data gaps should be considered, but these gaps should be accepted as part of
the uncertainty in decision-making process that follows the risk assessment.
General
��
Confidence in data and data sources should be considered.
��
Some quality scale of the RA model/analysis can be given depending on data
availability.
��
Data availability and quality are the responsibility of the risk managers.
142.
Conclusion
 The
��
concept of RA for endemic diseases has several dimensions that need to
be discussed and evaluated.
��
Classification of endemic diseases and the level of threshold should
be considered.
 This
��
meeting is a beginning and not the end of such discussion and evaluation.
 The
��
RA process needs to be refined to make it appropriate for endemic
disease issues.
143.
144.
Discussion Group 3
Import Risk Assessment
Day-2-results
145.
OIE Guidelines
 Generally good
 Principles fine
 Details not always necessary in a specific RA
 Clarification/HI/Release/borders/quarantine
 Border gradients
– If present in importing country
– Hazard or not?
– Prevalence differences
• biological consequences?
• Definition of hazard identification
 If no knowledge of pathogen in importing country
– Legal issue re hazard?
Fit for Purpose
 ‘Fit for Purpose’ concept
 2 (or more) types of RA
– For specific immediate regulatory descisions
– For longer term investigation and ‘research’
 Depends on risk question
 And aims of RM
 Identify all relevant risk managers
– Member States and Commission (FVO)
 Identify additional tools needed
 Quality issues
 Score RA to inform RM of eg data uncertainty etc
Surveillance and data
 Assessment of competent authorities (third countries)?
– FVO/Vet services
 Detection of emerging situations
– Before clinical signs etc
146.
 Subclinical and sero-surveillance
 Freedom from disease
– Low prevalence/many tests/expensive
 Region/country/zone
 International RA – is it possible?
 Collate policy making & RA by regions
 Networks for info:
 world?
 RM responsible for large international data bases
 International fact finding
Harmonisation/Standardisation
 Legal equivalence of surveillance
 Principle
 Defining risk profile by country?
 Region/epi area
 ‘Intelligent analyses’ to detect anomalies in reported data
 Correct anomalies if possible
 Probability of freedom
 Based on a range of evidences
 Wildlife
 But illegal activities
 Greatest risk?
147.
Qualitative descriptions
 Definitions?
 Is this possible?
 Absolute risks
– ‘grades’?
– Prob not
– Alternatives
– Blocks of text to describe
– Matrices often reduce transparency
 Relative risks
 Prob more use/higher/lower etc
Units (per animal; per annual import quantity etc.)
Quality and validity of RA
 Data
 Lack
 Expert opinion/evidence
– How, who, what?
 Assumptions and uncertainties
 Transparent
 Id key uncertainties which affect decisions
 Sensitivity analysis
 Experimental/surveillance
measures working)
etc
to
validate
results
 Statistical methods to validate results
 Quant
Risk manager/Risk assessor
 Interaction
 Starts with Risk question
 Dialogue/iteration
 Fit for Purpose?
148.
(&
can
check
RM
 Feedback loops re consequences
 Use of surveillance data
 RM – when do they act?
 Often only after problem!
 ALOP = RM
Risk Profiles
 Guide pre-RA
 clarifies
 RM expectations
 Risk questions
 Data availability?
 Increase understanding
 Decision process & management options
Recommendations
 Close RA/RM interaction throughout
 Specifying risk question e.g. strain specification?
 Id stakeholders
 Use ‘Risk profiles’ to outline problem
 OIE guidelines – clarification on:
 Interface between HI and Risk release
 Border gradient
 Data capture
 Sensitive data, how to get and use
 Outputs should id data gaps & future data needs
 Verification/challenge
– (data to verify or not?)
149.
Consideration of other groups’ findings
 Import – rapid response to new and emerging diseases
 Understanding the import process as part of the food chain
 Consider food chain globally and impact of farming conditions and capture of
wild animals on animal welfare, health and food safety
 For consistency welfare hazard needs to be “slippery floor”
 Use RA principles (science based, transparent etc) for welfare
 Welfare issues connected with disease control
150.
Discussion Group 4
Animal Welfare
Day-2-results
151.
Multidimensional Risk Assessment of Animal Welfare
Health Animal / Public
+
low
moderate
high
Production/ +
Economics
--
+ Behaviour
+
Physiology
of Hens in Barren cages
+
Production/ +
Economics
--
low
moderate
high
+ Behaviour
+
Physiology
152.
Multidimensional Risk Assessment of Animal Welfare of Hens in
Furnished cages
+
Production/ +
Economics
--
low
moderate
high
+ Behaviour
+
Physiology
of Hens in Free Range
+
low
moderate
high
Production/ +
Economics
--
+ Behaviour
+
Physiology
153.
Model
 ”Hazard” (we may find an alternative term)
 is a misleading term because we may consider factors with positive effects
on welfare
 identification
 characterisation
 Exposure
 prevalence, duration
Welfare consequences are a function of exposure x Exposure = welfare
(consequences that can be +ve and/or -ve)
Keep it simple at start: single welfare problem (lameness) or hazard
(e.g. elements of a farming system)
How do we define unwanted outcome
 Poor welfare
 Recognise essential requirements to live?
 What does an animal want?
 How do we define a good life?
 What data do we need?
Animal based measures
 Behavioural measures (e.g., stereotypes)
 Physiological measures (e.g., immuno-competence, homeostasis,
roduction indices)
 Health measures
154.
GENERIC SCHEME FOR ASSESSMENT OF A PERCEIVED ANIMAL
WELFARE PROBLEM
 Recognition of the welfare problem and state case definition (e.g. skin
lesions, lameness, stereotypes, indicators of animal needs not met)
 Formulate the risk question – focussed
 Formulate the animal based measure (key welfare indicators)
 Collect data and measure scale of the problem (e.g. incidence, prevalence,
intensity, duration)
 Using the data, identify the causes of the welfare problem and other risk
factors (e.g. enabling factors such as managerial, environmental factors),
make a risk pathway
 Identify the cases of the risk factors e.g. predisposing factors:
sex, genetics, age, breed, state, critical control points, extrinsic
factors (management factors, hygiene condition)
 Summary assessment of the occurrence, intensity and duration of the
welfare condition
 Conclusions based on the summary measure
155.
Data requirements
 Data quality (fit-for-purpose)
Need particular type of data for the recognition
of poor welfare and on its cause. And...
How and when?
1.
2.
3.
4.
Quantitative dose response best (continuum)?
If not, use qualitative data (binary, banding)
If not then expert with evidence
If not then expert opinion (methods that aim at objective summary of
expert opinion; convergence rather than conclusion)
 Practicality of collection, (snapshots??), and costs
 Key data collection point
 Are sub-groups relevant?
 Is data reliable, accurate (or biased), complete (identify missing data),
applicable (representative) to the farming situation?
 Is it valid - does it actually reflect welfare, pinpoint the cause accurately?
(See above)
156.
TM-AD-06-001-EN-C
European Food Safety Authority (EFSA)
Official seat:
Palazzo Ducale
Parco Ducale 3
I-43100 Parma
Italy
Operational and
postal address:
Largo N. Palli 5/A
I-43100 Parma
Italy
Tel: +39 0521 036 111
Fax: +39 0521 036 110
E-mail: [email protected]
www.efsa.europa.eu

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