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|authors=M.J. Tijhuis, N. de Jong, [[User:Mikko|M.V. Pohjola]], H. Gunnlaugsdóttir, M. Hendriksen, J. Hoekstra, F. Holm, N. Kalogeras, O. Leino, F.X.R. van Leeuwen, J.M. Luteijn, S.H. Magnússon, G. Odekerken, C. Rompelberg, [[User:Jouni|Jouni T. Tuomisto]], Ø. Ueland, B.C. White, H. Verhagen
|reference=  M.J. Tijhuis, N. de Jong, M.V. Pohjola, H. Gunnlaugsdóttir, M. Hendriksen, J. Hoekstra, F. Holm, N. Kalogeras, O. Leino, F.X.R. van Leeuwen, J.M. Luteijn, S.H. Magnússon, G. Odekerken, C. Rompelberg, J.T. Tuomisto, Ø. Ueland, B.C. White, H. Verhagen: State of the art in benefit–risk analysis: Food and nutrition. Food and Chemical Toxicology 50 (2012) 5–25 {{doi|10.1016/j.fct.2011.06.010}}
|urn=  
|year=2012
|ethics=  
|journal=
|moderator= Essi Vuorinen
|stub=}}

This page (including the files available for download at the bottom of this page) contains a draft version of a manuscript, whose '''final version is published and is available in the [http://www.sciencedirect.com/science/article/pii/S0278691511002547 Food and Chemical Toxicology 50 (2012) 5–25]'''. If referring to this text in scientific or other official papers, please refer to the published final version as: '''M.J. Tijhuis, N. de Jong, M.V. Pohjola, H. Gunnlaugsdóttir, M. Hendriksen, J. Hoekstra, F. Holm, N. Kalogeras, O. Leino, F.X.R. van Leeuwen, J.M. Luteijn, S.H. Magnússon, G. Odekerken, C. Rompelberg, J.T. Tuomisto, Ø. Ueland, B.C. White, H. Verhagen: State of the art in benefit–risk analysis: Food and nutrition. Food and Chemical Toxicology 50 (2012) 5–25 {{doi|10.1016/j.fct.2011.06.010}}'''.

==Title==

Editing State of the art in benefit–risk analysis: Food and nutrition

==Authors and contact information==

:M.J. Tijhuis, correspondence author
:(mariken.tijhuis@rivm.nl)
:(National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands)
:(Maastricht University, School of Business and Economics, Maastricht, The Netherlands)

:N. de Jong
:(National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands)

:M.V. Pohjola
:(National Institute for Health and Welfare (THL), Kuopio, Finland)

:H. Gunnlaugsdóttir
:(Matís, Icelandic Food and Biotech R&D, Reykjavík, Iceland)

: M. Hendriksen
:(National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands)

:J. Hoekstra
:(National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands)

:F. Holm
:FoodGroup Denmark & Nordic NutriScience, Ebeltoft, Denmark

:N. Kalogeras
:(Maastricht University, School of Business and Economics, Maastricht, The Netherlands)

:O. Leino
:(National Institute for Health and Welfare (THL), Kuopio, Finland)

:F.X.R. van Leeuwen
:(National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands)

:J.M. Luteijn
:University of Ulster, School of Nursing, Northern Ireland, United Kingdom

:S.H. Magnússon
:(Matís, Icelandic Food and Biotech R&D, Reykjavík, Iceland)

:G. Odekerken
:(Maastricht University, School of Business and Economics, Maastricht, The Netherlands)

:C. Rompelberg
:(National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands)

:J.T. Tuomisto
::(National Institute for Health and Welfare (THL), Kuopio, Finland)

:Ø. Ueland
:(Nofima, Ås, Norway)

:B.C. White
:(University of Ulster, Dept. of Pharmacy & Pharmaceutical Sciences, School of Biomedical Sciences, Northern Ireland, United Kingdom)

:H. Verhagen i,j
:(National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands)
:(Maastricht University, NUTRIM School for Nutrition, Toxicology and Metabolism, Maastricht, The Netherlands)
:(University of Ulster, Northern Ireland Centre for Food and Health (NICHE), Northern Ireland, United Kingdom)

==Article info==

Article history:
Available online 12 June 2011

==Abstract==

Benefit–risk assessment in food and nutrition is relatively new. It weighs the beneficial and adverse
effects that a food (component) may have, in order to facilitate more informed management decisions
regarding public health issues. It is rooted in the recognition that good food and nutrition can improve
health and that some risk may be acceptable if benefit is expected to outweigh it. This paper presents
an overview of current concepts and practices in benefit–risk analysis for food and nutrition. It aims to
facilitate scientists and policy makers in performing, interpreting and evaluating benefit–risk assessments.

Historically, the assessments of risks and benefits have been separate processes. Risk assessment is
mainly addressed by toxicology, as demanded by regulation. It traditionally assumes that a maximum
safe dose can be determined from experimental studies (usually in animals) and that applying appropriate
uncertainty factors then defines the ‘safe’ intake for human populations. There is a minor role for
other research traditions in risk assessment, such as epidemiology, which quantifies associations
between determinants and health effects in humans. These effects can be both adverse and beneficial.
Benefit assessment is newly developing in regulatory terms, but has been the subject of research for a long
time within nutrition and epidemiology. The exact scope is yet to be defined. Reductions in risk can be
termed benefits, but also states rising above ‘the average health’ are explored as benefits. In nutrition,
current interest is in ‘optimal’ intake; from a population perspective, but also from a more individualised
perspective.

In current approaches to combine benefit and risk assessment, benefit assessment mirrors the traditional
risk assessment paradigm of hazard identification, hazard characterization, exposure assessment
and risk characterization. Benefit–risk comparison can be qualitative and quantitative. In a quantitative
comparison, benefits and risks are expressed in a common currency, for which the input may be deterministic
or (increasingly more) probabilistic. A tiered approach is advocated, as this allows for transparency, an early stop in the analysis and interim interaction with the decision-maker. A general problem in
the disciplines underlying benefit–risk assessment is that good dose–response data, i.e. at relevant intake
levels and suitable for the target population, are scarce.

It is concluded that, provided it is clearly explained, benefit–risk assessment is a valuable approach to
systematically show current knowledge and its gaps and to transparently provide the best possible science-
based answer to complicated questions with a large potential impact on public health.

===Abbreviations===

ADI, acceptable daily intake; AICR, Association for International Cancer Research; ALARA, as low as reasonably achievable; AR, Average Requirement; ATBC,
alpha-tocopherol, beta carotene cancer prevention (trial); BCRR, Benefit Cancer Risk Ratio; BENERIS, benefit–risk assessment for food: an iterative value-of-information
approach; BMD, benchmark dose; BMDL, lower one-sided confidence limit on the BMD; BNRR, Benefit Noncancer Risk Ratio; BRA, Benefit–risk analysis; BRAFO, benefit risk
analysis of foods; CARET, beta-carotene and retinol efficacy trial; DALY, disability adjusted life years; EAR, Estimated Average Requirement; EFSA, European Food Safety
Authority; FAO, [United Nations] Food and Agriculture Organization; FOSIE, food safety in Europe: risk assessment of chemicals in the food and diet; FUFOSE, Functional Food
Science in Europe; ILSI, International Life Sciences Institute; JECFA, Joint FAO/WHO Expert Committee on Food Additives; LLAB, lower level of additional benefit; LOAEL,
Lowest Observed Adverse Effect Level; MOE, margin of exposure; NOAEL, No Observed Adverse Effect Level; OECD, Organisation for Economic Co-operation and
Development; PAR, population attributable risk; PASSCLAIM, process for the assessment of scientific support for claims on foods; POD, point of departure; PRI, population
reference intake; RDA, recommended dietary allowance; RfD, Reference Dose; RIVM, [Dutch] National Institute for Public Health and the Environment; RR, relative risk; SCF,
Scientific Committee on Food; (P)TDI/WI/MI, (provisional) tolerable daily/weekly/monthly intake; UL, tolerable upper intake level; ULAB, upper level of additional benefit;
WHO, World Health Organization; QALIBRA, quality of life – integrated benefit and risk analysis; QALY, quality adjusted life years; WCRF, World Cancer Research Fund.

==Keywords==

Keywords:
Benefit–risk,
Benefit,
Risk,
Food,
Nutrition

==Introduction to the scope of this paper==

Food and nutrition are in essence necessary and beneficial to,
but may also have adverse effects on, human health. Public health
professionals have realized, while acknowledging the success of
the current food safety system, that the health loss due to unhealthy
food and nutrition is many times greater than that attributable
to unsafe food; and that the health gains to be made
through the consumption of certain foods are many times greater
than the health risks involved (van Kreijl et al., 2006). The beneficial
and adverse effects may occur concurrently through a single
food item (for example fish or whole grain products) or a single
food component (for example folic acid or phytosterols), within
the same population range of dietary intake. This means that any
policy action directed at the adverse effects also affects the degree
of beneficial effects. The challenge in the relatively new field of
benefit–risk assessment in food and nutrition is to scientifically
measure and weigh these two sides.

Historically, the assessments of benefits and risks have been
separate processes, where by far most attention has been directed
at the assessment of risks, as required by regulation (EU, 2000,
2002b). Focus has been on food safety, with precaution as a risk management option (EC, 2000a) surrounded by much debate as to
when and how it should be applied. Worded differently, it is a
political decision whether or not to include benefits. From a public
health perspective, however, a decision-making process which
strives for the lowest risk does not by definition lead to the optimal
population health outcome. If the benefits are high enough, some
risk may be acceptable. As such, there is a broader picture to be
assessed.

The benefit–risk assessment approach entails a paradigm shift
from traditional risk analysis, as known from toxicology, to benefit–
risk analysis. Essentially, it brings together different scientific
disciplines, which have their own history, perspective, tools and
uncertainties:

* Toxicology, the discipline that traditionally investigates the risk
of ‘too much’ (acute or chronic adverse effects), as required by
the regulatory process.

* Nutrition, a younger discipline that investigates the risk of ‘too
little’ (nutritional deficiencies), the risk of ‘too much’ (nutritional
intoxications or affluence-associated problems) and the
benefits of optimal nutrient intakes.

In benefit–risk assessment, there is an important role for epidemiology,
the methodology-oriented discipline that investigates associations
between (real-life human) exposures and health
outcomes, adverse or beneficial.

In order to weigh the risks and benefits of foods or their
components, they must be evaluated and expressed in a comparable
way. Methodologies have been developed and applied to
actual cases. New grounds have been explored, yet consensus
on the general principles or approaches for conducting benefit–
risk analysis for foods and food components still needs to be
reached.

This paper presents an overview of current approaches to an
integrated weighing of benefits and risks, a state of the art in benefit–
risk analysis, in the field of food and nutrition. In this, most
attention is directed at the assessment phase, but benefit–risk management
and benefit–risk communication are also recognized as an
integral part of the benefit–risk paradigm. The perspective is broad,
rather than technical. The aim is to facilitate scientists and policy
makers in carrying out and judging benefit–risk assessments, to
eventually come to better informed decisions about food-related
health issues.

We start with an overview of key terms in the field. We then
describe risk assessment and benefit assessment separately to
build up to a description of current approaches to combine the
two. This is illustrated by an overview of benefit–risk case studies
and followed by a brief description of benefit–risk management
and communication. We end with conclusions and
recommendations.

==Key terms in benefit–risk assessment of food and nutrition==

As benefit–risk assessment of food and nutrition involves different
disciplines using different scientific jargon, we first define the
key terms as they are used in this review: food, nutrition, risk, benefit
and benefit–risk assessment.

In this review, ''food'' is defined as any substance or product,
whether processed, partially processed or unprocessed, intended
to be, or reasonably expected to be ingested by humans (EU,
2002a). Foods are made up of many different components affecting
body functions. ‘‘Food’’ and ‘‘food component’’ are viewed as a
broad concept: macronutrients, micronutrients, bioactive nonnutrients,
additives, contaminants, residues, phyto/phyco/mycotoxins,
micro-organisms, allergens, supplements, whole foods and
novel foods. The total of these components that are ingested via
food, naturally occurring or added, can be seen as nutrition. Notably,
‘nutritious’ has the connotation of ‘healthy’. As a science,
‘‘nutrition’’ studies the total of processes by which food or its components
are ingested, digested, absorbed, metabolized, utilized,
needed and excreted, and the interactions between these components
and between these processes, and their effect on human
health and disease.

In the food and nutrition context, we see risk as the probability
and severity of an adverse health effect occurring to humans following
exposure (or lack of exposure) to a food or food component.
In risk assessment, a distinction is made between ‘‘risk’’
and ‘‘hazard’’. In this context, the intrinsic potential of a food
(component) to result in adverse health effects is called a hazard.
A hazard, then, becomes a risk if there is sufficient exposure. Risk
can result from both action and lack of action and both can be
analyzed (Wilson and Crouch, 2001). An ‘‘adverse health effect’’
is defined by the WHO as ‘a change in morphology, physiology,
growth, development or life span of an organism, which results
in impairment of functional capacity or impairment of capacity
to compensate for additional stress or increase in susceptibility
to the harmful effects of environmental influences’ (WHO, 1994).
A ‘‘beneficial health effect’’ can analogously be seen as ‘an
improvement of functional capacity or improvement of capacity to
deal with stress or decrease in susceptibility to the harmful effects
of environmental influences’ in the above definition (Palou et al.,
2009). On the benefit side, no parallel words exist for ‘‘hazard’’
and ‘‘risk’’. In order to make the parallel distinction on the benefit
side but also have similarity in nomenclature, we will use
‘‘adverse health effects’’ and ‘‘beneficial health effects’’ instead of
hazards and benefits. In this review we use benefit analogously
to ‘‘risk’’, as the probability and degree of a beneficial health effect
occurring to humans following exposure (or lack of exposure) to a
food (component). Beneficial health effects may postpone the
onset of disease and thus benefit can also be measured as a reduction
of risk.

''Benefit–risk assessment'' is seen here as a science-based process
intended to estimate the benefits and risks for humans following
exposure (or lack of exposure) to a particular food or food
component and to integrate them in comparable measures, thus
facilitating better informed decisions by decision-makers.

==Risk assessment==

Risk assessment is an established field, that is mainly addressed
by toxicology (Faustman and Omenn, 2008). The traditional risk
assessment paradigm (EC, 2000b) consists of

* hazard identification (what effect?),
* hazard characterization (at what dose? how?),
* exposure assessment (how much is taken in?) and
* risk characterization (what is the probability and severity of the effect?).

A risk assessment framework specifically for food has been
developed in the context of the FOSIE project (Food Safety in Europe,
www.ilsi.org/Europe) (Smith, 2002). The basic concepts and
approaches to risk assessment in toxicology will be addressed in
Section 3.1. A separate paragraph is dedicated to micronutrients,
which is placed under toxicology for practical reasons. There is
an additional (not yet clearly defined) role for epidemiology, which
will be addressed in Section 3.2. Focus will be on those characteristics
where the most pronounced differences exist between toxicology,
epidemiology and later benefit assessment, and which
need to be considered when integrating the different disciplines.
Table 1 presents a summary of some of the characteristics of toxicological
and epidemiologic risk assessment.

{|{{prettytable}}
|+'''Table 1.''' Characteristics of toxicological and epidemiological risk assessment<sup>a</sup>, that need to be integrated, adapted from van den Brandt et al., 2002.
|-
!Characteristic
!Tox
!Epi
|-----
|Initiation
|Mainly regulatory
|Mainly academic
|-----
|Starting point
|Usually suspicious substance
|Both substance and endpoint
|-----
|Study design
|Mainly experimental
|Mainly observational
|-----
|Formal procedures defined
|Yes
|No
|-----
|Study population
|Selected, genetically homogeneous animals
|Mainly free-living, genetically heterogeneous humans
|-----
|Exposure under study
|Controlled (administered), mostly one agent, supraphysiological doses
|Uncontrolled<sup>b</sup> (measured), multiple agents likely, physiological doses
|-----
|Extrapolation
|To lower doses (‘actual exposure’) using default value of 10 or chemical specific adjustment value
|No, actual exposure
|-----
|Presentation of exposure and risk
|Usually absolute
|Usually relative (ranking of study subjects)
|-----
|Quantification of effect (with chronic exposure)
|Incidence (proportion of animals with adverse effect over duration of study; usually lifetime risk;) or continuous measure
|Incidence rate (proportion with adverse effect per person-year, standardized to a particular age distribution; lifetime risk rare) or continuous measure
|-----
|Comparison
|Difference
|Usually ratio<sup>c</sup>
|-----
|Evaluation of evidence
|Selection of high-quality study with the most sensitive effect in the most sensitive subgroup (worst-case scenario or positive evidence approach)
|All high-quality studies (weight of evidence approach, calculating overall effect)
|-----
|Some sources of uncertainty
|
* Threshold assumption, low-dose extrapolation
* Interspecies differences
* Human interindividual variation
* Imprecision
|
* Exposure measurement error
* Residual confounding
* Selection bias
* Human interindividual variation
* Imprecision
|}
:<sup>a</sup> With focus on chronic exposures.
:<sup>b</sup> Unless randomized clinical trial (RCT).
:<sup>c</sup> Necessary for case-control study; for cohort studies calculation of risk differences is also possible.


===Food toxicology===

Classical food toxicology focuses on the absence of risks, viz. on
safety (EU, 2000, 2002b). Risk assessment may be initiated for a
variety of purposes, most commonly it is triggered by a legal
requirement (EC, 2000b).

====Basic principles====

The basic tenet of quantitative risk assessment is that data on
health effects detected in small populations of animals exposed
to relatively high concentrations of an agent can be used to predict
health effects in large human populations exposed to lower concentrations
of the same agent. Generally, risk is measured as the
fraction of the population exceeding a defined upper intake or
‘guidance’ level. This level is established by working down from
the most sensitive hazardous effect (the so-called critical effect)
in animals and applying uncertainty factors for extrapolation to
the most sensitive human subgroup. The toxicological approach
differs between compounds that are assumed to show a threshold
effect and those that are not, between compounds that are avoidable
in the diet and those that are not, and depends on the amount
of scientific information available and the policy of decision-makers
towards acceptance of risk. First we briefly address the identification
of adverse health effects, then we address their
characterization and translation into risk.

====Identification of adverse effects====

The better toxicity studies have an appropriate number of dose
levels, sufficient sample sizes and focus on relevant endpoints in a
relevant species (WHO-Harmonization-Project, 2005–2003). These
are mostly animal toxicity tests, with an experimental design following
OECD guidelines for the testing of chemicals (OECD). Tests
of acute toxicity, i.e. the toxicity that manifests itself immediately
or within 14 days after exposure to a single administration of a
chemical by ingestion, inhalation or dermal application, are often
not useful for hazard identification and risk assessment in relation
to foods and food chemicals (Barlow et al., 2002), because human
exposure is generally of chronic nature and is usually much lower
than the dose that is identified in these tests. Tests of subacute or
subchronic toxicity, i.e. repeated dose toxicity studies mostly performed
in rodents for a period of 28 or 90 days, should reveal the
major toxic effects. The effects may include changes in body and
organ weight, organ histopathology, hematological parameters,
serum and urine clinical chemistry and sometimes extensions to
screen for neurotoxicity or immunotoxicity. Tests of chronic toxicity
cover a larger part of the animal’s life span, e.g. 12 or 24 months
in rodents. There are specific tests and procedures for establishing
reproductive and developmental toxicity, for neurotoxicity, for
genotoxicity, for carcinogenicity, for immunitoxicity and for food
allergies (Barlow et al., 2002).

====Characterization of adverse effects and risk====

[[Image:Fn1a.JPG|thumb|450px|'''Fig. 1a.''' Theoretical description of adverse health effects of a nutrient or foreign
non-genotoxic compound as a function of excessive intake. Note: The NOAEL is a
point observation of the most critical effect. Uncertainty factors (UF’s) are applied to
the NOAEL to derive the guidance level (UL, ADI, TDI). The default UF is 10 to allow
for species differences and 10 to allow for human variability. Increased UF’s may be
used if the study does not detect a NOAEL and the guidance level has to be based on
the LOAEL. Lower UF’s may be used with higher quality data or extremely mild and
reversible effects. Source: Renwick et al., 2004. Reprinted by permission of the
publisher (Elsevier).]][[Image:Fn1b.JPG|thumb|450px|'''Fig. 1b.''' Theoretical description of beneficial health effects of a nutrient as a
function of level of intake. Note: The solid line shows the risk of inadequacy for the
effect used to establish the recommended dietary allowance (RDA); as intake
decreases below the RDA there is an increase in the risk of adverse effects due to
inadequate intake. At an intake equal to the EAR (also known as AR), 50% of the
population will have intakes that are adequate, but for 50% of the population this
intake will be inadequate for the prevention of adverse effects or maintenance of
benefit. The dashed line represents the risk of inadequacy of nutrient intake for a
health benefit (e.g. disease risk reduction) not included in the derivation of the RDA.
Source: Renwick et al., 2004. Reprinted by permission of the publisher (Elsevier).]]


The standard approach to characterize a dose–response relationship,
for substances assumed to show non-carcinogenic
threshold effects, is the derivation of the No Observed Adverse Effect
Level (NOAEL). This is the largest amount of a substance that
the most sensitive animal model can consume without adverse
health effects (see Fig. 1a). Usually, the most robust datasets are
chosen with adverse effects occurring at the lowest levels of exposure
from studies using the most relevant exposure routes (Faustman
and Omenn, 2008). If there are no adequate data
demonstrating a NOAEL, then a LOAEL (the lowest amount at
which adverse effect are observed) may be used. Based on the
NOAEL (or LOAEL), health-based guidance levels are established,
under the rationale that these will ensure protection against all
other adverse health effects which may be caused by the compound
considered. This means that although different endpoints
may yield different NOAEL’s, one NOAEL is chosen as a point of
departure from the experimental dose–response range for setting
acceptable exposure levels and this is the one for the most relevant
sensitive endpoint in the most sensitive species; Based on this
NOAEL, a health based guidance value is established by means of uncertainty factors. By default (i.e. unless specified data are available
to do otherwise) the NOAEL is divided by 10 to take into account
differences between animals and humans (interspecies),
and again by 10 to take into account differences in sensitivity
between humans (interindividual) (Dorne and Renwick, 2005;
Renwick, 1993).

The guidance level for nutrients (see also Section 3.1.4) is the
tolerable upper intake level (UL), for additives is termed the ADI
(acceptable daily intake) and for contaminants in food the terminology
in Europe is tolerable daily (or weekly/monthly) intake
(TDI/TWI/TMI). In the USA, Reference Dose (RfD) is preferred as
a less value-laden term for the latter. All these terms refer to
an estimate of the intake of a substance over a lifetime that is
considered to be ‘without appreciable health risk’ (WHO, 1994).
They are meant to protect 98% of the population. To most toxicologists,
guidance levels are ‘‘soft’’ estimates and not a matter of
marking acceptability from non-acceptability (EPA, 1993). Exposures
somewhat higher than the guidance level are associated
with increased probability of adverse effects, but that probability
is not a certainty. Similarly, the absence of a risk to all people
cannot be assured at the level below the guidance level (EPA,
1993). As a consequence, more and more dose–response modeling
is being advocated as a more science-based way to go. Different
mathematical and physiological approaches are being
explored. Edler et al. (2002) describe a number of statistical and
mechanistic modeling approaches with differing data requirements,
degree of complexity, applicability and type and quality
of resulting risk estimates, including: structure–activity relationships
and the threshold of toxicological concern; the benchmark
dose (BMD); probabilistic risk assessment; and physiologicallybased
pharmacokinetic modeling. The latter fits in the development
towards a ‘systems toxicology’; in vivo dose–response
curves could be predicted by combining in vitro toxicity data
and in silico kinetic modeling (Louisse et al., 2010; Punt et al.,
2008). Other initiatives exist, e.g. the Key Events Dose–Response
Framework (KEDRF) incorporates information on the substance’s
mode of action and examines critical events that occur between
the initial dose of a bioactive agent and the effect of concern
(Julien et al., 2009).

By default, the threshold approach is not applied to genotoxic
carcinogens, because theoretically (although the organism is
equipped with some degree of cytoprotection) one molecule can
initiate the cancer process. The Joint FAO/WHO Expert Committee
on Food Additives (JECFA) and in Europe the SCF (the predecessor
of EFSA, the European Food Safety Authority) advised that the presence
of carcinogenic contaminants in the diet should be reduced to
‘irreducible levels’ by current technological standards (Edler et al.,
2002), which is complicated by the fact that detection limits are
increasingly more sensitive. This approach is captured in the ALARA
(as low as reasonably achievable) management option to
regulate avoidable genotoxic carcinogens. These committees considered
that there was no adequate science basis for low-dose
extrapolation to quantify a ‘‘virtual safe dose’’, an acceptable additional
cancer risk for lifetime exposure to the compound of interest
which can be set at, for example, one additional cancer case in a
population of one million people. Current methods that do quantify
a virtual safe dose are based on linear extrapolation from a
point of departure on the dose–response curve (Boobis et al.,
2009). It is argued that for genotoxic carcinogens, both the threshold
concept and the low-dose linearity concept are in need of
refinement (Boobis et al., 2009; Rietjens and Alink, 2006; Waddell,
2006). This will require better mechanistic insight and risk–benefit
analysis (Boobis et al., 2009; Rietjens and Alink, 2006; Waddell,
2006). It can help overcome the critique with regard to the precautionary
principle that it does not provide a transparent way to
weigh and account for different risks, or in other words, that it does
not help regulators to decide which risks to regulate (EFSA, 2005;
Post, 2006).

EFSA recommends using the margin of exposure (MOE) approach
as a harmonised methodology for assessing the risk of substances
in food and feed, which are genotoxic and carcinogenic
(EFSA, 2005). The MOE approach uses a reference point, that is often
taken from an animal study and that corresponds to a dose that
causes a low but measurable response in animals. This reference
point is then compared with various dietary intake estimates in
humans, taking into account differences in consumption patterns
in the population (EFSA, 2005). The MOE essentially shows
whether the human levels of exposure are close to effect levels
and enables the comparison of the risks posed by different genotoxic
and carcinogenic compounds. It is for the risk manager to decide
if the magnitude of the MOE is acceptable, and if further action
is needed taking into consideration additional aspects, such as the
feasibility of removing the substance from the food supply. In general
a MOE of 10,000 or higher, if it is based on a bench mark dose
lower confidence limit for a 10% change in effect (BMDL10, see below)
from an animal study, might be considered of low concern
from a public health point of view and as a low priority for risk
management actions. However, this number is arbitrary and the
judgment is ultimately a matter for decision-makers. Moreover
an MOE of that magnitude in itself should not preclude the application
of risk management measures to reduce human exposure.
Recently, an outline framework for calculating an MOE was proposed
in order to help to ensure transparency in the results (Benford
et al., 2010).

The MOE approach could also be applied for non-carcinogenic
substances. EFSA recommends the use of the benchmark dose
(BMD) to estimate the MOE, for instance where the human exposure
is close to the ADI (EFSA, 2005, 2009b). The BMD approach
estimates the dose that causes a low but measurable critical effect,
e.g. a 5% increase in the incidence of kidney toxicity or a 10% percent
change in the level of liver weight. By calculating the lower
confidence limit of the estimated dose (BMDL), the uncertainty
and variability in the data is taken into account (EFSA, 2009b). In
this BMD approach more information is used than in the NOAEL
approach.

====Micronutrients====

For micronutrients, a somewhat different approach needs to be
followed, as risk can come from intakes that are too high, but also
from intakes that are too low. Micronutrients, home in the field of nutrition, are addressed here because toxicology-related measures
are used in their risk assessment. The higher end guidance level,
the UL (see Fig. 1a), differs in two principal aspects from the ADI
and TDI. First, micronutrients, as opposed to additives or contaminants,
are subject to homeostatic control. This means that the
body may adapt its functioning to intake and deal with some level
of excess or shortage. Second, UL’s may be set for different life
stage groups.

The other often used guidance level, the recommended dietary
allowance (RDA, see Fig. 1b) (also known as population reference
intake, PRI) (EFSA, 2010a), is by default set for different life stage
groups and by sex and represents the nutritional needs of most
healthy individuals (thereby exceeding the minimal requirements
for almost all). It is calculated from the Estimated Average Requirement
(EAR, see Fig. 1b) or Average Requirement (AR) (EFSA, 2010a),
which is the daily intake value that is estimated to meet the
requirement, as defined by a specified indicator of adequacy, in
50% of the individuals in a life stage or sex group (Renwick et al.,
2004). Often, a normal distribution of requirements and a 10–
15% coefficient of variation (CV) for the EAR are assumed. The
RDA must be considered when deriving the UL, as it is possible that
default uncertainty factors would yield a UL below the RDA. The intake
range between deficiency and toxicity varies greatly between
nutrients. For example, it is large for vitamin C and narrow for zinc.
In addition, it may overlap between different populations. Related
to this given, there is critique on current methods of establishment
of the upper level for micronutrients and maximum permitted levels
of vitamins and minerals in food supplements working from
precaution, and a call for using ‘decision science’ is made (Verkerk,
2010). Here, the point is that dosages that induce risks in a few
might actually at the same time induce benefits in many (Verkerk,
2010).

===Nutritional epidemiology===

In the traditional risk assessment of foods, the use of epidemiologic
data has been limited. There is no mandatory role (and there
are no clear criteria) for epidemiologic data in risk assessment for
regulatory purposes, despite the fact that (nutritional) epidemiologists
are conceptually and practically already used to describe effects
at physiological intake levels and despite the limitations of
animal studies due to the requirement of both interspecies and
high-to-low dose extrapolations.

====Basic principles====

Epidemiology is about identifying and quantifying associations
between (real-life) human exposures and health outcomes, adverse
or beneficial. The exposure and the health outcome, usually
a disease or marker for a disease, are most often a priori specified
and hypothesis-driven. If the association between exposure and
outcome is found to be inverse, i.e. higher exposure leads to lower
disease occurrence, this signifies a reduction in risk. In other words,
in epidemiology, an ‘exposure’ is a factor or condition that may increase
or decrease the risk of disease (WCRF/AICR, 2007). This work
often serves the quest for knowledge and understanding more than
a practical regulatory goal, but it is also possible to quantify the effect
on (public) health to directly aid in decision making. Reflecting
the above, three types of measures can broadly be distinguished in
epidemiology: measures of disease occurrence, measures of association
and measures of effect (Rothman et al., 2008). Disease occurrence
can be expressed as cases that are prevalent (current) or
incident (new). The measures of association are most often expressed
as relative differences (or ‘ratio’ measures), for example:
the relative risk (RR) to acquire disease X among those consuming
food (component) Y in the 5th quintile of the intake distribution is
1.5 compared to those in the 1st quintile (the reference, set to 1);
but often absolute difference measures, such as the ‘excess risk’
that consuming food (component) Y contributes to acquiring disease
X, can also be calculated. The measures of effect have impact
on those exposed or on the population. An example of the latter is
the population attributable risk (PAR), which can be seen as the
reduction in occurrence of disease X that would be achieved if
the population had been entirely unexposed to food (component)
Y, compared with its current exposure pattern (Rothman et al.,
2008). In the risk assessment context, epidemiologic studies are
mainly observational in design.

====Study design and dose–response characterization in epidemiology====

The great advantage of epidemiology is the fact that it actually
studies the species it wants to make inferences about. Yet this is
also its hurdle, as humans are free-living and subject to many influences.
Both exposure and outcome are difficult to measure accurately
and subject to systemic bias and measurement error
(Kroes et al., 2002; Rothman et al., 2008; Willett, 1998). These
are not insurmountable problems, however. The possibility of bias
occurring can be reduced through good study design. Study design
can be experimental or observational. Experimental design in humans,
encompassing randomized controlled trials (RCT’s), is not
ethical or feasible in case of (severe) risks or long latency (van
den Brandt et al., 2002). In some cases, unintended risks turn up
in trials, such as in the ATBC and CARET trials (ATBC-Study-Group,
1994; Omenn et al., 1996) and in the Norwegian Vitamin Trial and
Western Norway B Vitamin Intervention Trial for folate (Ebbing
et al., 2009), where an increased percentage of cancer cases was
seen where a decrease was expected. These trials were, however,
limited to one dose, and thus dose–response relationships could
not be characterized. Observational studies include cohort studies
(starting with exposure assessment and following up on disease
development) and case-control studies (starting with diseased
and non-diseased and assessing past exposure). They allow for long
latencies between exposure and health outcome of interest. These
studies do not provide direct evidence for causal relationships, but
a number of methodological considerations have been proposed to
judge whether an observed association is a causal relationship and
to help in making decisions based on epidemiologic evidence (Hill,
1965; Phillips and Goodman, 2004; Rothman et al., 2008). All
factors suspected to be related to the factor of interest need to be
measured accurately and in unbiased samples of the target population.
Statistical modeling techniques can then control for their influence on the outcome (confounding) or detect associations that
are different in subgroups (effect modification or interaction). In
studies that do not show an effect, it is often not clear whether
the effect is null or simply smaller than can be detected by the
studies (insufficient power). Combining data from different studies
will increase the power and estimate dose–response functions
with more precision. However, gathering the appropriate dose–response
data will not always be possible. An example of successful
use of epidemiologic studies is the case of aflatoxins and liver cancer
(van den Brandt et al., 2002). Animal models had been found to
be inappropriate, showing a large degree of variability in the carcinogenic
potential of aflatoxins across species. From human studies
looking into Hepatitis B and liver cancer, which included
aflatoxin intake, the aflatoxin potency to induce liver cancer (cancers/
year/100.000 people/ng aflatoxin/kg body weight/day) could
be calculated. Another example, in which dose–response relations
have been estimated from human observational studies, is the relation
between methyl mercury in fish and cognitive development,
as recently summarized by the FAO/WHO expert consultation
(FAO/WHO, 2010).

====Proposed frameworks for use of epidemiologic data in risk assessment====

The need for a more structured use and presentation of epidemiologic
data is risk assessment is long recognized (Hertz-Picciotto,
1995). Hertz-Picciotto (1995) has proposed a classification
framework for the use of individual epidemiologic studies in dietary
quantitative risk assessment, with special focus on dose–
response assessment, and this has been modified by van den
Brandt et al. (2002). They identified three classification categories
for epidemiologic studies: (1) the study can be used to derive a
dose–response relationship; (2) the study can be used to check
on plausibility of an animal-based risk assessment; (3) the study
can contribute to the weight-of-evidence determination of
whether the agent is a health hazard. For the classification of a
study into the three categories a number of criteria are used
addressing its validity and utility. Recently, a three tier framework
has been proposed through which human observational studies
can be selected for use in quantitative risk assessment, with specific
focus on exposure assessment (Vlaanderen et al., 2008). General
recommendations for reporting of observational studies have
been provided by STROBE, which stands for Strengthening the
Reporting of Observational Studies in Epidemiology (http://
www.strobe-statement.org/ (Vandenbroucke et al., 2007)).

====Scope of risk assessment====

In contrast to benefit assessment, the scope of risk assessment
is fairly well established (see Section 4.4). It deals with the assessment
of adverse health effects caused by physical or chemical
agents. These can be occurring naturally in foods, resulting from
food preparation or manufacturing processes or environmental
contaminants. At the moment there is further development towards
a better understanding of the nature and mechanisms of
toxic effects caused by these agents, the inclusion of data other
than the traditional toxicological data (Eisenbrand et al., 2002;
van den Brandt et al., 2002), and consideration of the shape of
the dose–response relationships (linear, threshold, J-shape, Ushape)
(Calabrese et al., 2007; Zapponi and Marcello, 2006).

====Benefit assessment====

Benefit assessment is not an established field. Although beneficial
effects conferred by food have been the topic of investigation
for a long time (for example within nutritional epidemiology,
see Section 3.2), the structured characterization of benefits
has only recently begun to develop. Benefit assessment has
recently gained more attention because of the developments in
the form of health claims on foods. In December 2006, the European
Union published its Regulation 1924/2006 on nutrition and
health claims made on foods, which, several decades after risk
assessment, places a legal perspective also on benefit assessment.
Below we will address some concepts and approaches in benefit
assessment.

====Basic principles and concepts====

In the past decades, the goal of ‘‘adequate’’ nutrition, as defined
by the absence of nutritional deficiencies, has shifted to the ambitious
goal of ‘‘optimal nutrition’’. This change goes beyond the concept
of ‘risk reduction’ and does not only apply to traditional foods
and food components, but has also led to the concept of ‘‘functional
foods’’. Food is seen as a possibility to improve health. Taking beneficial
effects into account, may also uncover a discrepancy. The
RDA (see Section 3.1.4 micronutrients) allows a small percentage
of the population to be at inadequate intake for the specified indicator
of adequacy; however, if the intake needed to gain a theoretical
benefit (as indicated by the dashed line in Fig. 1b) is higher
than this level, then a substantial part of the population may not
have the advantage of this theoretical benefit. In the benefit assessment
context, epidemiologic studies can be both observational and
experimental, but experimental (‘intervention’) studies are
preferred.

====Study design and strength of evidence====

As described in Section 3.2 both observational and experimental
methodologies can be used to assess risks and benefits. From
observational studies, there is ample experience in describing associations
between human exposure and health outcomes. The level
of evidence they provide is most commonly assessed by using

* the WCRF/AICR criteria, resulting in the judgment ‘‘convincing’’,
‘‘probable’’, ‘‘limited-suggestive’’, ‘‘limited-no conclusion’’ or
‘‘substantial effect on risk unlikely’’ (WCRF/AICR, 2007); or

* the WHO criteria, resulting in the judgment ‘‘convincing’’,
‘‘probable’’, ‘‘possible’’ or ‘‘insufficient’’ (WHO, 2003).

For ethical reasons, human experiments (or ‘‘intervention
studies’’) are generally pre-eminently carried out to study beneficial
and not adverse health effects, once observational data have
shown that the benefit is reasonably probable and/or toxicological
screening has given reasonable proof of safety. However, it
is not uncommon that these trials are short-term and limited to
only one or two doses, and these doses are generally rather high
to yield the best chance of ‘success’ (van den Brandt et al., 2002).
This hampers the construction of dose–response curves that are
relevant for long-term human exposure. Short-term trials may
not always be appropriate to characterize effects of food and
nutrition, because after long-term exposure response mechanisms
may adapt to a different state (tolerance) or wear out (threshold).
Guidelines for the design and reporting of human intervention
studies and for a standardized approach to prove the efficacy of
foods and food constituents are being developed (ILSI, 2010).
The created knowledge and methodology can be applied in a
broader context, and this includes answering a benefit–risk
question.

====Proposed framework====

There is agreement in the field to consider benefits according to
standardized assessment procedures and specific definitions (Palou et al., 2009). Benefits should be scientifically proven. It is
advocated to structure benefit assessment in the same framework
as risk assessment; For example, the EU advisory working group on
harmonization of risk assessment procedures identifies a 4-stage
approach consisting of

* ‘value identification’,
* ‘value characterization’,
* ‘use assessment’, and
* ‘benefit characterization’(EC, 2000b).

EFSA uses the terminology

* ‘identification of positive / reduced adverse health effect’,
* ‘characterization of positive / reduced adverse health effect’,
* ‘exposure assessment’, and
* ‘benefit characterization’ (EFSA, 2010b).

Examples of initiatives specifically focusing on benefit assessment,
in the form of claims made by industry, are the FUFOSE
and PASSCLAIM projects, coordinated by ILSI Europe (www.
ilsi.org/Europe). One of the goals for the FUFOSE project was to
assess critically the science base required to provide evidence
that specific nutrients and food components beneficially affect target
functions in the body (Bellisle and et al., 1998; Diplock et al.,
1999). Its follow-up, the PASSCLAIM project, aimed to define a
set of generally applicable criteria for the scientific substantiation
of claims on foods (Aggett et al., 2005; Asp and Bryngelsson,
2008). The PASSCLAIM criteria cover: (1) the characterization of
the food or food ingredients for the health claim, (2) the necessity
of human data for the substantiation of a health claim (by
preference data coming from human intervention studies but
also accepting human observational data), (3) the use of valid
endpoints or biomarkers as well as statistically significant and
biological relevant changes therein, and (4) the requirement for
the totality of the data and weighing of the evidence before
making a judgment whether a health claim is or is not substantiated
(Aggett et al., 2005; Asp and Bryngelsson, 2008). Addressed
in the claims regulation 1924/2006 is the particular
beneficial nutritional property of a food (in so-called nutrition
claims) or the relationship between a food or food category
and health (in so-called health claims) (Verhagen et al., 2010).
Examples of the latter are the relationship between calcium
and development of bones and teeth, the relationship between
plant sterols and coronary heart disease through reduced blood
cholesterol and the relationship between iron and cognition in
children. A state of the art in nutrition and health claims in Europe
is given by Verhagen et al. (2010).

===Scope of benefit assessment===

Claims are a driver of, but not the only, development in benefit
assessment. There are other initiatives to increase benefit from
food, such as voluntary food reformulation by industry (for example
reducing the amount of salt, sugar or trans fatty acids) (van
Raaij et al., 2009). With respect to traditional foods there has been
a development in research to look at whole foods or whole diets
(dietary patterns), for example the Mediterranean diet.

Benefits are often measured on a disease scale, either directly as
a reduction in disease risk or indirectly as a change in disease marker.
Currently, markers of optimal health are also being explored
(Elliott et al., 2007). This is a fundamentally different process, as
subjects are healthy individuals and effects are expected to typically
be small. Two bottlenecks have been identified in the development
of the newly explored biomarkers to quantify health
optimization (van Ommen et al., 2009): the robustness of homeostasis
and inter-individual differences in what appear to be normal
values. Many markers are maintained within a limited range and
effects are masked unless homeostasis is challenged. Nutritional
challenge tests (e.g., a high-fat diet) are being developed in this
context. These will bring imbalance to normal physiology. Quicker
recovery then signifies more optimal health. A combination of perturbation
of homeostasis and systems biology and the -omics technologies
have been proposed as a means to quantify the system’s
robustness and to measure all the relevant components that describe
the system and discriminate between individuals (Elliott
et al., 2007; Hesketh et al., 2006; Keijer et al., 2010; van Ommen
et al., 2009). This development also signifies a shift from a population
orientation to a more individual based orientation of benefit–
risk analysis.

So far, benefits are based in the biological health domain. Decision-
makers are bound by a limited amount of (public) money and
(thus) require management options based on physiological mechanisms,
not on for example individual ‘preferences’. However, certain
perceptions and behaviors may well influence the quality of
life in such a way that they are relevant for benefit–risk weighing
(for a detailed description of consumer perception, see Ueland
et al., 2011). Also, long-term benefits, in the form of ecological considerations,
could be relevant. Thus, it might be reasonable to accept
a broad scope of what entails a benefit.

==Benefit–risk assessment==

From a legal perspective, benefit assessment is about the permission
to carry a claim on a product and risk assessment is about
defining a maximum intake dose below which the population intake
is safe (without appreciable adverse effects). From a public
health perspective, benefit assessment and risk assessment are
not yes/no or safe/unsafe decisions, but describe a range and continuum
of doses and likelihoods of effect. Benefit–risk assessment
is about evaluating the whole scope of human health and disease.
It involves more than adding an assessment of benefits to an
assessment of risks; the scientific processes to be integrated are
not symmetric. Below, we will first briefly address the integration
of the science. Next, we will discuss a number of frameworks for
performing a benefit–risk analysis, the integration of outcome
measures, uncertainty in the assessment and other (problematic)
issues.

===Integration of disciplines===

Until recently, benefit assessment and risk assessment were approached
as separate processes and the domain of different underlying
scientific disciplines. In benefit–risk analysis, all inevitably
comes together. There are differences in the process and in the output
which need to be integrated. Table 2 presents some of these
differences. The integration requires adaptations from all disciplines
involved.

There is increasing interest to know which effects occur on the
entire range of human exposure to be able to characterize benefits
and risks. Toxicologists have suggested that toxicology should redirect
its focus from looking at adverse effects at high levels of experimental
exposure, to characterizing the complex biological effects,
both adverse and beneficial, at low levels of exposure, the so-called
‘low-dose toxicology’ (Rietjens and Alink, 2006). This will help risk
quantification of realistic intakes and is also interesting from the
idea that some low (physiologic) doses actually turn out to be beneficial
(Son et al., 2008). Epidemiologists and nutritionists have
urged all who are generating human data to improve the quality
of their data presentation and expand it, by presenting more detailed
information focusing on dose–response necessities, and/or
by sharing primary data (de Jong et al., submitted for publication).
Some also stress the important role for early, quantifiable biomarkers of exposure and of effect as well as for the -omics technologies
(Palou et al., 2009). This is expected to improve exposure
assessments and provide early response profiles.

Ideally the (old and new) disciplines could complement each
other within the benefit–risk assessment process, with their roles
differing depending on e.g. the type of food (component) and its
history of use, but always from the aim to end up with data most
relevant to humans.

{|{{prettytable}}
|+'''Table 2.''' Characteristics of (regulatory) benefit and risk assessment<sup>a</sup>, that need to be integrated.
|-
!Characteristic
!Benefit assessment
!Risk assessment
|-----
|Study design
|Experimental (observational may also be accepted)
|Experimental (observational may also be accepted)
|-----
|Study population
|Human
|Animal (Human is possible)
|-----
|Widely accepted methodology
|In development
|Yes
|-----
|Widely accepted endpoints
|In development; also measured as reduction in adverse endpoint
|Yes
|-----
|Exposure
|Usually few (supraphysiological) doses in experimental study; physiological doses/observational design may also be accepted
|Usually few (supraphysiological) doses in experimental study
|-----
|Study duration
|Usually short (days/weeks) in experimental design, years/decades can be covered in observational design
|Usually lifetime of animal until adverse endpoint occurs, years/decades can be covered in observational design
|-----
|Evidence required
|Grading ‘convincing’ (sometimes ‘probable’ admitted) in weight of evidence approach
|Strongest effect/steepest slope in positive evidence approach
|-----
|Desired outcome in benefit–risk assessment setting
|Benefit characterization, including low dose–response information
|Risk characterization, including low dose–response information
|-----
|Desired outcome in regulatory setting
|Benefit/no benefit
|Safe/unsafe or acceptable/non-acceptable
|}
:<sup>a</sup>With focus on chronic exposures.

===Benefit–risk assessment approaches===

A number of research efforts have been dedicated specifically to
the development of benefit–risk methodology in the field of food
and nutrition. There have been three recent Europe-based projects,
each with its own focus: BRAFO (2007–2010), QALIBRA (2006–
2009) and BENERIS (2006–2009); in addition, the topic has been
subject of investigation by the European Food Safety Authority
(EFSA, 2007, 2010b), by national food and health related institutes
(Fransen et al., 2010; van Kreijl et al., 2006) and by individual
groups.

====Generalities in approaches====

[[Image:Fn2.JPG|thumb|550px|'''Fig. 2.''' The benefit–risk assessment paradigm. Note: A benefit assessment arm and a
risk assessment arm run parallel until comparison. Wording changes slightly in the
literature, see Section 2.]]

Conceptually, those exploring the field of benefit–risk assessment
in food and nutrition agree that much health gain can come
from investing in a better diet, more than from further improving
food safety (see Table 3). Practically, there is consensus about
the importance of a well formulated and well described benefit–
risk question. This prior narrative needs address the exposure,
target population, health effects and scenarios. As there is more
experience with risk assessment than with benefit assessment, it
is generally suggested that the steps taken in benefit–risk assessment
should mirror the traditional risk assessment paradigm of
hazard identification, hazard characterization, exposure assessment
and risk characterization, with a risk arm and a benefit arm
running parallel until comparison (see Fig. 2). A fully quantitative
benefit–risk analysis requires a large amount of data (see Fig. 3)
and time. Assumptions about missing or incomplete data are inevitable.
A tiered (stepwise) approach is advocated, allowing transparency
and an early stop in the analysis in case the question is
answered or in case there is substantial lack of data. In most approaches
there is not only room for in-between scientific evaluation,
but also for interim consultation with the decision-maker.
This means that if a risk is found which is acceptable to the decision-
maker, then the benefit–risk assessment may also come to a
stop (i.e., a management judgement). In fact, policy makers are
most often the intended user and initiator of the benefit–risk
assessment. In general, a distinction can be made between a qualitative
comparison and a quantitative comparison of benefits and
risks. In a qualitative comparison the benefits and risks are compared
in their own currency. This is sufficient when there is clear
dominance of either the benefits or the risks, e.g. low gastritis incidence
versus high cancer incidence, or when comparison with
guidance values indicates that either the benefits or the risks are
negligible and there is no true benefit–risk question left. In cases
where benefits or risks do not clearly dominate, quantitative comparison
may answer the benefit–risk question. Quantitative comparison
of benefits and risks is seen as an (optional) end stage
and can be achieved by expressing the effect of each endpoint in the same metric (e.g. quality or disability adjusted life years). In
calculating these measures, the nature of the estimate may be
deterministic, i.e. using point estimates, or probabilistic, i.e. using
distributions. In general, some kind of screening is incorporated
in the assessment to evaluate whether the benefits clearly prevail
over risks or vice versa, and can remain qualitative. This may entail
that exposure assessment is performed before the dose–response
characterization (see Fig. 2). For transparency and best insight in
who receives the benefits and risks, all separate health effects need
to be communicated apart from the net health effect. In the process
of developing the methodology, case studies have been performed
to test and refine the approaches.

{|{{prettytable}}
|+'''Table 3.''' Comparing estimated health loss and potential health gain by healthy diet and unsafe food in the Netherlands (van Kreijl et al., 2006; see Section 6).
|-
!Factor
!Number of deaths/year
!DALYs<sup>a</sup>/year
!Health gain through improvement of food safety versus improvement of diet (composition, amount)
|-----
|Diet composition<sup>b</sup>
|13,000
|245,000
|rowspan=6|[[Image:Foodtable1.JPG|thumb|center|300px]]
|-----
|Bodyweight
|7000
|215,000
|-----
|''Diet in total<sup>c</sup>''
|
|''>350.000''
|-----
|Foodborne infections by known pathogens
|20–200
|1000–4000
|-----
|Chemical constituents
|100–200
|1500–2000
|-----
|''Food safety in total''
|
|''2500–6000''
|}
:<sup>a</sup> DALYs = disability adjusted life years; important here is the message, not the exact numbers: annual health gain to be achieved through improvements to the diet is many times greater than that possible through further improving food safety.
:<sup>b</sup> Diet composition including five factors (fruits, vegetables, fish, saturated fatty acids and trans fatty acids).
:<sup>c</sup> Counting diet composition and part of bodyweight linked to diet.


[[Image:Fn3.JPG|thumb|center|600px|'''Fig. 3.''' Quantitative benefit–risk assessment, according to QALIBRA methodology. Note: A fully quantitative benefit–risk assessment is very data intensive. For each individual,
information is needed on age, sex, body weight, etc. Furthermore, for each beneficial and adverse effect and for each scenario, information is needed on intake, dose–response
functions, age of disease onset, recovery probabilities, mortality probabilities, severity/disease weights, disease durations. Printed by courtesy of the QALIBRA project.]]

====Specific approaches====

Below we describe specific initiatives to approach the benefit–
risk assessment field.

The ILSI Europe project BRAFO (www.brafo.org) follows up the
projects FOSIE (see under ‘risk assessment’), FUFOSE and PASSCLAIM
(see under ‘benefit assessment’). The primary aim of BRAFO
has been to develop a framework that allows for the quantitative
comparison of human health risks and benefits in relation to a
wide range of foods and food compounds. The BRAFO approach
consists of 4 tiers, preceded by a pre-assessment and problem formulation
(Hoekstra et al., 2010). The problem is worded as a comparison
in net health effect between a reference scenario and an
alternative scenario. In tier 1, the individual risks and benefits
are assessed. Should only risks or only benefits be identified, the
assessment stops. When both risks and benefits are identified,
the assessment proceeds to tier 2, which entails a qualitative integration
of risks and benefits. In this, four main dimensions are considered:
the incidence, the severity/magnitude of the health
effects, their duration and the additional (reduction in) mortality
caused by the effect and consequent (reduction in) years of life lost.
Should either the risks or the benefits clearly dominate, then the
assessment stops. Special table formats are suggested to present
the data and information relevant in tier 1 and 2, for clarity and
transparency. In tier 3, the benefits and risks are weighed quantitatively.
Tier 3 is closely related to a 4th tier, which also entails a
quantitative assessment. These tiers are actually a continuum,
starting fully deterministically, using fixed estimates, and ending
more and more probabilistically, using probability distributions.
The approach has been tested and further developed with several
case studies.

The tiered approach developed by the National Institute for
Public Health and the Environment (RIVM) of the Netherlands also
distinguishes steps separated by ‘‘stop’’-moments to allow for a
timely evaluation to conclude whether the gathered information
is sufficient to answer the initial benefit–risk question (Fransen
et al., 2010). As compared with the BRAFO-approach, the exposure
assessment has been moved upward and the dose–response modeling
as part of hazard and benefit characterization is moved to a
later stage. The characterization step includes an extensive literature
search and modeling to estimate dose–response functions
for the selected effects. The advantage of giving priority to the
exposure assessment is that one gets early information of the population
distribution of the exposure. In case of no or very limited
exposure in risk groups, one can terminate the risk–benefit assessment
at an early stage, before going to a benefit–risk characterization.
The decision tree has been tested with case studies.

QALIBRA (www.qalibra.eu) has developed methods for quantitative
assessment that integrate the risks and benefits of dietary
change into a single measure of net health impact (See Fig. 3),
and that allow quantification of associated uncertainties. It is a typical
example of the 3-rd and 4-th tier in BRAFO (Hoekstra et al.,
2010). QALIBRA also follows a stepwise approach, the consecutive
steps consisting of: (1) Problem formulation including specification
of the dietary scenarios and the population to be considered. (2) Identification of the adverse and beneficial health effects to be assessed,
including influential factors and affected population. (3)
Estimation of the intakes or exposures that cause those health effects.
(4) Modeling of the dose–response relationship for each effect,
including the probability of onset at the current age and, if
the effect is continuous, the magnitude of the effect. (5) Estimation
of the probabilities of recovery and mortality for affected individuals.
(6) Selection of a common currency (e.g. DALY or QALY, see
Section 5.3). (7) Specification of the severity and duration of the effect.
(8) Calculation of the net health impact. (9) Evaluation of
(quantifiable and unquantifiable) uncertainties and variabilities.

These methods are implemented in the QALIBRA tool, which is
available to registered users on the QALIBRA website. The user is
responsible for supplying the data such as intake scenarios,
dose–response functions and if needed uncertainty intervals. The
tool can be used in BRAFO’s quantitative tiers 3 and 4. QALIBRA
recommends that practitioners follow the BRAFO tiered approach,
resolving benefit–risk questions without quantitative integration
where possible, and reserve the QALIBRA framework for those
cases where assessment at tiers 3 and 4 is necessary. The tool
works according to the annual directly attributable health impact
method (van Kreijl et al., 2006). This approach considers a single
year and only health effects that have their onset during that year.
Their direct potential impact is considered, without taking into account
interactions with other health effects. It is a less datademanding,
but less realistic, alternative for the ‘simulation approach’,
which takes into account a longer period, in which a proportion
of the population will change class with respect to age, risk
factor and disease status. QALIBRA advises to start with the simpler
approach (addressing in a narrative how the overall impact might
be affected by the way individual effects combine) and reserve the
simulation of health over whole lifetimes as a higher tier option
should the direct attributable health impacts method not answer
the benefit–risk question sufficiently. The RIVM Chronic Disease
Model is an example of a simulation approach; it has been used
in (a part of) a large Dutch case study (Hoogenveen et al., 2010;
van Kreijl et al., 2006).

The BENERIS (http://en.opasnet.org/w/Beneris) project has
made a significant contribution to the methodological foundation
of ‘open assessment’, which considers assessments as open collaborative
processes of creating shared knowledge and understanding
(see also Pohjola et al., this issue). The open process brings scientific
experts, decision-makers, and any other stakeholders to the
same collaborative process of tackling public health problems. Collaboration
is facilitated by the Opasnet web-workspace (http://
en.opasnet.org), which is the major output of the project and consists
of a wiki-interface, a modeling environment, and a database.
In the BENERIS approach, open criticism has a crucial role. The
objective in each tier is to convince a critical outsider (‘an impartial
spectator’) about the current conclusions. Thus, the assessment
work is directed towards the aim to gain acceptability in the eyes
of a critical outsider. Openness is a critical feature of an assessment
to make it possible in practice for outsiders to join and criticize the
current content of the assessment. Another characteristic of BENERIS
is the use of ‘value-of-information’, a scientific method for estimating
the expected benefit that occurs when an uncertainty is
resolved, to give guidance about the critical uncertainties that
should be focussed on in the next tier. The main tools are probability
distributions and Bayesian nets.

EFSA has organized a colloquium on ‘risk–benefit analysis of
foods: methods and approaches’ in 2006 (EFSA, 2007) and has recently
published a guidance document with respect to methodology,
approaches, tools and potential pitfalls in the risk–benefit
assessment (EFSA, 2010b). It proposes a three step approach, after
the problem formulation: (1) initial assessment, addressing the
question whether the health risks far outweigh the health benefits
or vice versa, (2) refined assessment, aiming at providing semiquantitative
or quantitative estimates of risks and benefits at relevant
exposure by using common metrics, and (3) comparison of
risks and benefits using a composite metric such as DALYs or
QALYs (see Section 5.3) to express the outcome of the risk benefit
assessment as a single net health impact value (EFSA, 2010b).

[[Image:Fn4.JPG|thumb|500px|'''Fig. 4.''' Schematic representation of the window of benefit assessment model. Note:
An increase in the intake of a food or food component will result in the following
subsequent physiological conditions: deficient, adequate, beneficial, adequate, and
toxic. The window of benefit integrates nutritional optimization and safety
considerations. Its optimal adequacy will vary between individuals and within an
individual during his/her lifetime, depending on age, sex, lifestyle and genetic
constitution. It holds on to threshold values, but introduces two new values: the
lower level of additional benefit (LLAB) and the upper level of additional benefit
(ULAB). No further detectable benefit can be seen from intakes below or above these
thresholds, respectively. Source: Palou, A., Pico, C., Keijer, J., 2009. Integration of risk
and benefit analysis – the window of benefit as a new tool? Crit. Rev. Food Sci. Nutr.
49, 670–680. Reprinted by permission of the publisher (Taylor and Francis Group,
http://www.informaworld.com).]]

Along a slightly different line, the ‘‘window of benefit assessment’’
has been proposed as a concept to integrate nutritional optimization
and safety considerations, see Fig. 4 (Palou et al., 2009). In
this concept, the threshold model and a score model are combined
as a framework for weighing risks and benefits. The traditional
threshold model works from the guidance values for each (non-)
nutrient that must not be exceeded or that must be reached, for
example the UL and RDA in the case of nutrients (see Section 3.1.4
nutrients). Two new thresholds are introduced: the upper level of
additional benefit (ULAB) and the lower level of additional benefit
(LLAB). These are defined as the upper and lower levels of daily
nutrient intake that can provide detectable additional benefits,
apart from benefits derived from avoiding excessive intake or
deficiency. Intake, then, should be within UL and RDA (absence
of excess or shortage), but preferably within ULAB and LLAB (improved
health). LLAB and ULAB will not be necessarily within the
window defined by the RDA and UL; it is possible, for example, that
the UL is based on a small and mildly adverse effect but that a larger
beneficial effect occurs at a higher dose. Thus, type and severity
of effect are relevant. It is postulated that, based on the robustness
of homeostasis, LLAB and ULAB will delimit the optimal intake, that
is needed when the biological system is challenged, while RDA and
UL indicate levels of intake that are not permissible on a population-
wide scale (Palou et al., 2009). It is proposed to score
nutritionally beneficial and adverse characteristics of foods and
combine them into a global score (Palou et al., 2009). The use of
a score model allows the weighing of risks and benefits. A tool like
this is still under development, for example by the PASSCLAIM project
(see Section 4). The window of benefit is targeted to the needs
of specific population subgroups (Palou et al., 2009). The same intake
of a nutrient leads to an individually different windows of
benefit depending on age, sex, physiological or pathological conditions, individual genetic constitution and lifestyle history
(Hesketh et al., 2006; Palou et al., 2009).

Similarly, Verkerk describes a conceptual model which shows
how beneficial effects may arise above the UL and that benefit–risk
evaluation is required across a ‘zone of overlap’ (Verkerk, 2010;
Verkerk and Hickey, 2010). Significant benefits occurring at intake
levels above the UL are postulated to be the norm rather than the
exception (Verkerk, 2010). It is argued that current management
by regulatory prohibition and using a single most sensitive risk factor
in the most sensitive population denies benefits to large parts
of the population (Verkerk, 2010; Verkerk and Hickey, 2010). Also,
it is argued that different management strategies are required for
different molecular forms of the micronutrients, for example in
the case of folates, carotenoids and niacin, as adverse effects differ
between them (Verkerk, 2010; Verkerk and Hickey, 2010).

Renwick et al. (2004) have been one of the first to recognize that
the difference in approach between establishing an RDA and a UL
means that any benefit–risk analysis is based on different underlying
criteria and approaches. They have provided a basis of how different
aspects of the intake–incidence data for either the benefit or
toxicity can be taken into account by suitable modeling, and how
the output of the model can be used to provide usable advice for
risk managers.

===Integrated measures===

One important question in weighing benefits and risks is which
unit of measurement to use. A distinction can be made between
single outcome health metrics and ‘‘composite’’ or ‘‘integrated’’
health measures. Single outcome health measures reflect a single
measurement dimension and unit, for example mortality, disease
incidence or functioning (quality of life). Composite health measures
capture a combination of single outcome health metrics into
one currency using a valuation function to weight the different
health states or outcomes. It then becomes possible to unite mortality
and morbidity and also to weigh different health states or
different outcomes such as a reduced incidence of angina against
an increased breast cancer incidence. Currently, the most used
integrated health measures in food-related benefit–risk analysis
are the DALY (disability adjusted life years) and the QALY (quality
adjusted life years), measures which use the value, or preference,
that people have for health outcomes, along a continuum between
0 and 1 (Gold et al., 2002).

[[Image:Fn5a.JPG|thumb|500px|'''Fig. 5a.''' Schematic representation of the QALY concept. Note: Simple situation for
one individual accounting for one episode of disease.]]

[[Image:Fn5b.JPG|thumb|500px|'''Fig. 5b.''' Schematic representation of the DALY concept. Note: Simple situation for
one individual accounting for one episode of disease.]]

In the QALY concept, 1 represents full health and 0 represents
death, see Fig. 5a. To generate QALY values, (often theoretical)
health states reflecting physical, social and emotional well-being
(not associated with a particular disease or particular condition)
are valued by members of the general public. The values (also
termed ‘‘utilities’’) are derived either directly, using methods such
as standard gamble, time trade-off and visual analog scales (Gold
et al., 2002); or indirectly, using generic quality of life/health status
questionnaires, in which some health states can be used to estimate
the value of (a large number of) other health states, varying
in sensitivity, administrative burden, cost, bias, etc. QALY’s are traditionally
mostly used to measure health gains at micro scale, for
example to compare two interventions. QALY’s of all individuals
are summed, and the scenario with the highest number of QALY’s
represents the highest health maximization. Per individual, QALY’s
are calculated by multiplying the duration of the disease (for
example the last 17 years of one’s life) by a quality weight (for
example quality is reduced from 1 to 0.7) and adding this to the
age of onset of disease (for example age 45), in other words: the life
years reached at age of death (for example age 62 years) or at an
earlier reference point are adjusted for their lack of quality and result
generally in a number which is lower than the true age of
death (for example age 57 years) or age at an earlier reference
point, see Fig. 5a. When comparing two treatment groups or alternative
scenarios, the average areas under the curve are compared
and this difference is the number of QALY’s that the ‘‘better’’ intervention
or scenario may gain over the ‘‘worse’’ intervention or
scenario.

In the DALY context, 0 represents no disability and 1 represents
death (Gold et al., 2002), see Fig. 5b. DALYs tend to be based on a
universal set of standard weights covering specific diseases, based
on expert judgments using ‘trade-off’ methods, the most wellknown
being those defined by Murray (Murray, 1994). For many
diseases, disability weights, which are currently being revised in
the Global Burden of Disease 2010 study, are available at the
WHO website (WHO, 2004). For example, the disability weight
for uncomplicated diabetes mellitus is 0.015, for angina pectoris
0.124, for cancer in the metastatic phase 0.75 and first ever stroke
0.92. Some countries have established national weighing factors
(Schwarzinger et al., 2003). DALY’s are based on disease years,
but for short-term discomforts, such as (minor) food poisoning of
influenza, there is also a solution (though the weight itself can be
hard to find); the condition can either be weighed as a full year
and then corrected to the actual duration or weighing can be
adapted to represent a year which includes an episode of disease
(the ‘year profile approach’) (van Kreijl et al., 2006). DALYs were
introduced to primarily communicate a population-aggregate measure
of (loss of) health – the Burden of Disease used by WHO.
DALY’s of all individuals are summed; the scenario with the lowest
number of DALY’s represents the highest health maximization or
lowest health loss. One DALY represents the loss of the equivalent
of one year of full health. It counts down from a standard ‘‘ideal’’
age of life expectancy at birth (for all population subgroups, but
separate for men and women). From this reference point (for
example 82 years for women, see Fig. 5b), years spent with a disease
(for example 17) are multiplied by a disability weight (for example 0.3, where 0 is healthy) and added to the years that death
(for example at age 62) prevented one from reaching full life
expectancy. In other words, the years of life lost (YLL, in this example
20 years) and the years lived with a disease (YLD, which count
more with higher disease burden) are added and thus result in a
number which is higher than the true years lost (in this example
25 years). When comparing two intervention groups or alternative
scenarios, the average areas above the curve are compared and this
difference is the number of DALY’s that the ‘‘worse’’ intervention or
scenario loses over the ‘‘better’’ intervention or scenario.

Often, the choice for DALY or QALY is a pragmatic one, based on
data availability or experience of use rather than on a fundamental
choice. Ethical and equity issues are not accounted for in QALY’s
and DALY’s. For example, when calculating QALY’s, the elderly
can be at a disadvantage, because they by definition have fewer life
years left and thus have less opportunity to increase quality adjusted
life years. Also, benefits and risks incorporated in one
integrated measure may occur in different population subgroups.
Folic acid is a good example, where prevention of neural tube defects
and masking of B12 deficiency or possibly increased cancer
risk apply to different subgroups. Decision-makers need to be
aware of this and thus it is important to always accompany the
DALY or QALY with the distribution of separate risks and benefits
in subgroups.

In the area of benefits and risks of fish consumption some less
‘intensive’ integrating measures have been explored. Gochfeld
and Burger (2005), have developed a composite benefit and risk
by dose curve to identify a zone of benefit, above the benefit
threshold and below the harm threshold. Separate dose–response
curves for benefit and harm are combined into net benefit–harm
composites. Foran et al. (2005) have calculated the Benefit Cancer
Risk Ratio (BCRR) and the Benefit Noncancer Risk Ratio (BNRR).
These ratios represent the rates of consumption of n-3 fatty acids
(g/d) while controlling for the cumulative level of acceptable carcinogenic
or noncarcinogenic risk of contaminants in fish. The risks
are assessed via the increased probability of death from cancer
(compared to a target risk of 1 in 10�5 and using a substance-specific
cancer slope factor) and the RfD (compared to a target noncancer
risk of 1), respectively. Their population values represent the
long run trade-off of benefits and risks over repeated consumption.
If the distribution of the estimates is not expected to be normal,
upper and lower 95% CI’s can be constructed by bootstrap methods.
Foran et al. have calculated the BCRR and BNRR to compare wholesale
farmed, retail farmed and wild salmon. Along a somewhat
similar line, Ginsberg and Toal (2009) have developed a risk–benefit
equation, calculated separately for each endpoint, using dose–
response functions and exposure information per fish meal. The increased
risk (for example on coronary heart disease, through methylmercury)
is then subtracted from the decreased risk/benefit (on
coronary heart disease, through n-3 fatty acids). Different fish species
can be compared, values >1 indicate a net benefit and values
<1 indicate an increased risk.

===Dealing with uncertainties===

In general, those involved in the benefit–risk process stress the
need to (at least) document and (if possible) assess uncertainties,
in every tier or stage of the analysis. A generally applicable approach
of evaluating uncertainties is a ‘Monte Carlo simulation’.
In this approach each uncertain variable is represented by a probability
distribution. Then multiple (plausible) values are generated
by repeated sampling values from the probability distributions for
each uncertain variable, and these values are used for calculating
the parameter of interest. The variation in the resulting outcomes
then represents the overall uncertainty for the parameter of interest.
In situations where uncertainty can not or hardly be described
in a probability function, for instance when no data are available
and experts find it difficult to make an estimate, the uncertainty
will be explored with sensitivity analyses ‘‘what-if’’ scenarios).
Here, the uncertain parameter is simply varied within a range of
most likely values. The models will then be run using different values
of the uncertain parameter. The baseline benefit or risk model
with default values of the parameters will then be compared to the
alternative models with different parameter values for the uncertain
variables. Furthermore, a sensitivity analysis allows one to
investigate which uncertainties and assumption are important
for the final benefit–risk result and which uncertainties can be ignored
(Saltelli and Annoni, 2010). It should be noted that the concept
of uncertainty needs to be discerned from the concept of
variability (van der Voet and Slob, 2007) and from the concept of
error (Wilson and Crouch, 2001).

===Issues yet to be solved===

Some issues in benefit–risk assessment still need to solved and
below some of them will be addressed.

====Evaluation of evidence====

[[Image:Fn6.JPG|thumb|550px|'''Fig. 6.''' Evaluating the presence of beneficial effects and the absence of adverse
effects. Note: Asymmetry in the evaluation of the evidence for claims and safety
assessments. The WHO classification (see Section 4.2), though meant for human
data, is used here as an example.]]

The evaluation of evidence for the identification of beneficial
and adverse health effects is asymmetrical: the presence of benefits
needs to be guaranteed and the evidence for this is reached differently
than for risks, for which the absence needs to be guaranteed
(Fig. 6). Information sources may differ, as some of the information,
mostly on the risk side, may be found in the so-called ‘‘gray literature’’
(for example government reports), which likely presents
good quality research but which is not as easily accessible as the
common scientific literature. The WCRF/WHO strength of evidence
grading (addressed under Section 4) is useful to identify large effects,
or small effects that have been investigated in many studies.
The same problem arises for adverse or beneficial effects for which
sufficient evidence exist but for which a decision needs to be taken
whether it is an adverse or beneficial effect of sufficient magnitude.
A well-known example is flushing with niacin excess. The grading
criteria also present some difficulties in case of new effects or in
cases where small effects are suspected but not sufficiently proven
on a broad range of outcomes (e.g. with consumption of sugar).
Subtle changes, especially for substances affecting human behaviour,
are difficult to identify. A large number of possible effects
may in reality have a more detrimental or beneficial effect on public
health than one convincing effect and ways need to be found to
take this into account to avoid a suboptimal decision. Also, they by
definition do not apply to animal data. In this context, animal studies
can go into category 3 of the classification categories for epidemiologic
studies mentioned under Section 4.3 (Hertz-Picciotto,
1995; van den Brandt et al., 2002), i.e. the study can contribute
to the weight-of-evidence determination of whether the agent
has an effect on health.

====Animal to human translation====

The cut-off values ADI, TDI, UL or RDA provide a safety
assessment. A benefit–risk assessment in which the 95th percentile
of the population distribution has an estimated intake of e.g.
6.5 mg/kg, where the ADI is 7, has to conclude that there is no
appreciable risk. Yet this is subject to different uncertainties
from when a human study on benefits concludes there is no
benefit. Guideline values can be used in a (first) qualitative
screening whether (substantial) risks are to be expected or not,
from where it can be decided whether a quantitative benefit–
risk assessment is needed. However, when the ADI is close or exceeded,
it is currently very difficult to quantify what the risks are
to humans. In some cases, for example for certain additives, the
management action at this point will be to lower the amount
that is allowed in food, thereby reducing the population intake
levels to below the ADI. In other, less avoidable cases, risk quantification
and integration into a common currency is desired, but
conversion of the measured pathological effect in an experimental
animal to a health state for which a quality of life weight exists
or to a clinical endpoint for which disease weights exist is
not straightforward; this is especially true when an endpoint is
measured that has no direct clinical endpoint, e.g. involuntary
weight loss. Another problem is that in animal experiments,
age of onset and age at death are seldom measured, whereas
in the directly attributable health loss calculation the probability
of onset per year for specified ages or age groups is required. It
will then be necessary to convert lifetime probabilities to annual
probabilities, which will often vary with age. But whether age of
onset has been measured or not, it would still be difficult to
translate this to the human situation. When the appropriate
treatment for age of onset is very uncertain, sensitivity analysis
should be performed to explore the impact of the different
assumptions.

====Integrated measures====

Apart from animal-related issues, not for all related health outcomes
(diseases affected) DALY weights are available or elicited
using similar methodology. Also, disability states in DALYs do not
take account of coexisting diseases. The weights are culture/country
specific (as are intake and disease occurrence). Because the
QALY is grounded in domains of health rather than descriptions
of specific diseases, it is at least theoretically possible to describe
and therefore value combinations of illness. When using QALYs,
health status profiles of those affected by the diseases under study
need to be accessible. QALY values vary with the actual questionnaire
used, often depend on framing of the questions, are not very
sensitive and differ between general practitioner, patient, family or
general public. Thus, the data requirements for calculation into the
common currency are high and there are many stumbling blocks.

====Exposure assessment====

Another issue is that food composition tables do not routinely
contain data on contaminants, additives or specific fractions such
as added sugars. To obtain this information, the target food should
be sampled and analytically analyzed or in some cases concentration
data are collected in special databases. The resulting values
should then be added to food consumption data manually, on a
one-on-one basis or by subgroups. In the interpretation of the results
it is important that there is also an idea about the background
exposure. But even for nutrients, for which much information is
available, estimation of intake is difficult (Kroes et al., 2002; Willett,
1998). Development of biomarkers may help here (Jenab
et al., 2009), but need to be applicable on a large scale to be really
useful.

====Lack of data====

Lack of data is a common reason for authors initially aiming to
perform benefit–risk analysis, to quit or restrict their analysis. For
example, in the UK, an expert group of nutritionists and toxicologists
was formed to review the benefits and risks associated with
fish consumption. They concluded that a ‘‘formal risk–benefit analysis
was not possible because the nutritional data were not sufficiently
quantitative’’ (Maycock and Benford, 2007). EFSA
expressed a problem with the data on one of the disease outcomes:
‘‘There is currently insufficient data to allow full quantitative risk
assessment of folic acid and cancer’’ (EFSA, 2009a). Recently it
was argued that ‘‘quantitative risk–benefit analyses of cardiovascular
effects of consuming specific fish species, based on joint contents
of fatty acids, methyl mercury, and selenium, cannot
currently be performed until the cardiovascular effects of methyl
mercury and selenium are established’’ (Park and Mozaffarian,
2010). The lack of data problem can sometimes be circumvented
by adjusting the initial problem definition (and accepting that data
availability influences the problem definition) or by using best
case/worst case scenarios. In general, lack of data introduces
uncertainties that depending on the problem formulation may result
in the inability to provide a useful assessment.

====Alternative actions and scenarios====

The scenarios to be compared are usually focused on the food
(component) of interest; the effects of substitution are usually not
taken into account. It is conceivable that for example the benefits
of eating fish will be different if not-eating fish in a western diet entails
eating more processed meat or if fish is replaced by e.g. soy and
walnuts. It is important to take other aspects of the diet into account,
even if only in a narrative, as there are many possible alternatives
which cannot all be quantified. Also, analyses and resulting
consumer recommendations can be refined. Fish, for example, can
be differentiated into types with general higher or lower contamination
levels (Ginsberg and Toal, 2009; Loring et al., 2010).

==Benefit–risk assessment in food and nutrition: case studies==

So far, several dozens of case studies have been performed to test
the methodological developments and expose their possible shortcomings
(‘‘learning by doing’’). These (completed and ongoing) case
studies, summarized in Table 4, deal with foods that may confer both
risks and benefits (fish, human milk, vegetables, soy, whole grain
cereals), with food components that may confer both risks and benefits
(folic acid, other micronutrients, phytosterols), with substitution
scenarios (added sugar/artificial sweeteners, SAFA/MUFA,
SAFA/carbohydrates) and with effects of alternative food processing
strategies (acrylamide, benzo(a)pyrene/HAAs). It can be seen from
Table 4 that the most investigated topic is fish consumption. Also,
many of the case studies following the newly developed tiered approaches
are yet to be completed. Microbiology-related studies
are addressed in Magnússon et al. (this issue). It is worth mentioning
that the concept of weighing the pros and cons of foods has also
been mentioned in relation to additional topics, such as coffee consumption
(Guenther et al., 2007) and dairy consumption in specific
subgroups such as vegetarians (Lanou, 2009; Weaver, 2009) or the
elderly (van Staveren et al., 2008). Recently, a plea was made for
benefit–risk analysis of the use of conjugated linoleic acid supplements
in cancer patients (Rastmanesh, 2010). The importance of
considering both risks and benefits is increasingly recognized.

In the Netherlands, the National Institute for Public Health and
the Environment (RIVM) has performed analyses of what can be
seen as an early and broad case study with regard to healthy diet
and food safety as a whole in the Dutch situation (van Kreijl
et al., 2006). It showed that the benefits of several foods outweigh their risk. Benefit–risk analyses were performed for fruits and vegetables,
wholegrain products, fish and human milk (breastfeeding)
see Table 4. The associated risks are mostly 100 times lower than,
and sometimes negligible compared to, the benefits. Taking this to
a broader picture, in terms of potential health gain, the report has
made clear that the benefits of food are not fully utilized. The annual
health gain to be achieved through improvements to the diet
is many times greater than that possible through further improving
food safety, see Table 3. The assessment includes a scenario comparison
between actual consumption and recommended intake
and takes into account the major chronic diseases diabetes, cardiovascular
diseases and cancer. This was placed in a food safety perspective.
Not including overweight as an outcome, the number of
healthy years lost (DALY’s) from the actual diet composition (taking
into account saturated fatty acids, trans fatty acids, fish, fruit
and vegetables) was 40–100 times greater than those lost for food
safety issues (van Kreijl et al., 2006). This reflects a maximum theoretical
health gain, because the whole population adhering to the
dietary recommendations is not realistic. Including the food contribution
to overweight in the comparison, the health loss expressed
in DALY’s was 60–140 times greater.

The obesity epidemic could also be seen as a case in the field of
food and nutrition. The benefits of calories are obvious, but so are
the risks of ‘too much’; an optimal intake range associated with
optimal health exists.


{|{{prettytable}}
|+'''Table 4.''' Case studies that started out with a benefit–risk question, performed quantitative benefit assessment and risk assessment, and intended to compare these qualitatively and/or quantitatively.
|-
!Topic and ''question type''
!Reference [framework/organization]
!Qualitative comparison, measures (toxicological and recommended reference values)
!Quantitative comparison, measure [reason for not reaching quantitative comparison]
!Remarks
|-----
|Acrylamide<sup>a</sup>
|Effect of alternative food processing strategy
|Seal et al. (2008)[ILSI Europe Expert group]
|YES, MOE approach NO [lack of knowledge regarding health impact at exposure levels experienced by the consumer]
|Mitigation scenarios. Benefit–risk exposure calculations, not health effect quantification.
|-----
|rowspan=2|Added sugar/intense sweeteners<sup>a</sup>
''Effect of substitution of food components with similar role in diet''
|Husoy et al. (2008) YES, Comparison with ADI and population nutrient goal
|NO [only benefits/ADI was not exceeded]
|Substitution scenarios. Exposure based on food consumption survey.
|For one sweetener the ADI was approached. For one additive associated to the use of sweeteners the ADI was exceeded, but no risk charachterization was performed.
|-----
|Hendriksen et al. (2010) [Dutch tiered approach/ RIVM<sup>e</sup>]
|YES, Comparison with ADI and population nutrient goal
|NO [only benefits/ADI was not exceeded]
|Substitution scenarios. Exposure based on food consumption survey and laboratory analysis of sweeteners
|-----
|Dyed food (Brown FK, fish)
''Effect of food component that confers both risk and benefit; effect of alternative food processing strategy''
|Combes (1987)
|YES, comparison with ADI
|NO [lack of data; on benefits: consumer requirements for dyed fish, and on risks: toxicity data for dye and for alternative food processing strategy]
|EFSA (2010a,b) re-evaluation states that the deficiencies in the available toxicity database does not allow a conclusion on the safety
|-----
|rowspan=17|Fish (Wild, farmed)<sup>a,b,c</sup>
''Effect of food that confers both risk and benefit''
|Ponce et al. (2000)
|
|YES
– QALY
|Taget-group based scenarios. Outcome :number of times individuals have to weight adverse effects more or less compared to beneficial effects to reach ambivalence.
|-----
|Tuomisto et al. (2004) [value-of-information approach]
|
|YES
– mortality (deaths from two
endpoints are combined)
|Response to paper performing risk assessment only; identification of points of improvement if accurate information would be available
|-----
|Cohen et al. (2005) [Harvard Center for Risk Analysis, Expert panel]
|
|YES
– QALY
|Hypothetical scenarios representing the response to two categories of risk management measures.Probabilistic uncertainty and sensitivity analysis.
|-----
|Foran et al. (2005)
|YES, comparison with guidance values (RDA and RfD)
|YES Comparison of wild versus farmed.
– BCRR/BNRR (based on cumulative exposure; see Section 5.3)
– number of lives saved per 100.000 individuals
|Scenario of exposure of 70 years.
|-----
|Gochfeld and Burger (2005) [Composite benefit–harm curve approach]
|YES, comparison with benefit thresholds (g/day/disease) and harm thresholds (RfD and MRL)
|YES
– Net benefit–harm composites (see
Section 5.3)
|Dose–response curves for benefits and harms are summed, where dose for harm is chosen as best case and worst case for MeHg content of fish.
|-----
|Hansen and Gilman (2005)
|YES, comparison with guidance values (PTWI, RfD, TDI via BMDL/ MOE approach)
|NO [qualitative comparison is regarded as reason for prompt political action to reduce the risks]
|High-risk population.
|-----
|van Kreijl et al. (2006) [OFOH<sup>f</sup>/RIVM<sup>e</sup>]
|YES, benefits were quantified by means of DALYs; comparison with TWI
|NO [it was not possible to calculate DALY’s for the health loss attributable to contaminants]
|Part of major study on healthy diet and safe foods in the Netherlands
|-----
|Mozaffarian and Rimm (2006)
|YES, based on strenght of evidence and potential magnitudes of effects
|NO [benefit–risk question answered on qualitative basis]
|Total adult and high-risk population
|-----
|VKM (2006) [Norwegian scientific committee for food safety]
|YES, comparison with RDA/UL and TWI
|NO [no widely accepted method; composition of total diet counts; food group is not homogeneous]
|Exposure based on food consumption survey.
|-----
|Becker et al. (2007) [National Food Administration, Sweden]
|YES, comparison with RDA and TDI/PTWI
|NO [no consistent dose–response data for exposure or health effects on humans]
|Exposure based on food consumption survey.
|-----
|van der Voet et al. (2007) [RIVM<sup>e</sup>]
|YES, comparison with AI and TDI NO [aim was to test a statistical model quantifying simultaneous and long-term exposure to two compounds representing benefits and risks]
|Benefit–risk exposure calculations, not health effect quantification.
|Exposure based on food consumption survey. Substitution scenarios. Monte Carlo simulation.
|-----
|Dewailly et al. (2008)
|YES, comparison with RDA and TWI
|NO [focus on analytical data]
|High-risk population. Laboratory analysis of MeHg, selenium and fatty acids.
|-----
|Guevel et al. (2008) [as Cohen et al. (2005)]
|YES, comparison with RDA and TWI
|YES Scenario of medium to high intake.
– QALY
|Adult population.
|-----
|FDA (2009a,b) [United States Food and Drug Administration]
|
|YES
– But endpoint-specific (incidence,
mortality)
|Not fish-specific
|-----
|Ginsberg and Toal (2009)
|
|YES
– But endpoint-specific risk–benefit
equation (see Section 5.3)
|Species-comparison approach, developed to guide advisories; resulted in 4 human consumption categories (unlimited; twice a week, once a week, do not eat)
|-----
|Loring et al. (2010) [as Ginsberg and Toal (2009)]
|
|YES
– But endpoint-specific
|High-risk population.
|-----
|FAO/WHO (2010) [Invited expert panel]
|
|YES
–DALY’s
|Scenarios. Report in preparation, executive summary available
|-----
|rowspan=3|Folate (fortification)<sup>a</sup>
''Effect of food component that confers both risk and benefit''
|Hoekstra et al. (2008) [RIVM<sup>e</sup>]
|
|YES
– DALYs
|Fortification scenarios; staple food and different doses; Sensitivity analysis performed.
|-----
|EFSA (2009) [expert meeting]
|YES, comparison with RDA and (uncertain) UL
|NO [insufficient data on risk for one of the endpoints]
|
|-----
|Fransen et al. (2010) [Dutch tiered approach/ RIVMe]
Verkerk (2010)[‘‘zones of overlap’’ model]
|YES, comparison with RDI and UL
YES, comparison of benefits and risks occurring above UL
|NO [no appreciable health risks]
NO Significant benefits of natural form at levels above the UL
|Data source is (Hoekstra et al., 2008), to test tiered approach. Focus on one fortification scenario
|-----
|rowspan=2|Human milk
''Effect of food that confers both risk and benefit''
|van Kreijl et al. (2006) OFOH<sup>f</sup>/RIVM<sup>e</sup>
|YES, benefits were quantified by means of DALYs
|NO [it was not possible to calculate DALY’s for the health loss attributable to contaminants]
|Part of major study on healthy diet and safe foods in the Netherlands
|-----
|Büchner et al. (2007) [RIVM<sup>e</sup>]
|YES, benefits were quantified by means of DALYs
|NO [enough evidence was found only for beneficial effects]
|Evaluation of breast feeding policies (comparing different durations of breast feeding)
|-----
|rowspan=3|Micronutrients
''Effect of food component that confers both risk and benefit (for folate see separate heading)''
|Verkerk (2010) [‘‘zones of overlap’’ model]
|YES, comparison of benefits and risks occurring above UL
|NO
|Niacin; significant benefits occur at intake levels above the UL
|-----
|Verkerk (2010) [‘‘zones of overlap’’ model]
|YES, comparison of benefits and risks occurring above UL
|NO
|Selenium; narrow margin between benefits and risks
|-----
|Verkerk (2010) [‘‘zones of overlap’’ model]
|YES, comparison of benefits and risks occurring above UL
|NO
|Fluoride; overlap of benefits and risks for significant propotion of the population
|-----
|rowspan=3|Plant foods/nitrate
''Effect of food that confers both risk and benefit''
|van Kreijl et al. (2006) [OFOHf/RIVMe]
|
|YES
– DALYs [risk estimates possible for 2 out of 4 identified hazardous plant food components, for the other 2 it is assumed the effects are not great.]
|Part of major study on healthy diet and safe foods in the Netherlands
|-----
|EFSA, 2008; Bottex et al. (2008) [EFSA<sup>d</sup> CONTAM<sup>g</sup> panel]
|YES, comparison with ADI
|NO [no appreciable health risks] Exposure scenarios based on RDA and type of vegetable
|Exposure scenarios based on RDA and type of vegetable
|-----
|Fransen et al. (2010) [Dutch tiered approach/RIVM<sup>e</sup>]
|YES, comparison with ADI NO [no appreciable health risks]
|NO [no appreciable health risks]
|Based on EFSA opinion (EFSA, 2008), to test decision tree
|-----
|Total diet
''Effect of following dietary guidelines versus current diet''
|van Kreijl et al. (2006) [OFOH<sup>f</sup>/RIVM<sup>e</sup>]
|
|YES
– DALYs
|Focus on fruits, vegetables, fish, saturated fatty acids, trans fatty acids. Part of major study on healthy diet and safe foods in the Netherlands
|-----
|Whole grain cereals
''Effect of food that confers both risk and benefit''
|van Kreijl et al. (2006) [OFOH<sup>f</sup>/RIVM<sup>e</sup>]
|YES, benefits were quantified by means of DALYs
|NO [risk difficult to quantify, but estimated to be negligible]
|Part of major study on healthy diet and safe foods in the Netherlands
|}
:<sup>a</sup> Topic also covered by BRAFO, in preparation (benefit risk analysis of foods; www.brafo.org). BRAFO is also preparing BRA’s on benzo(a)pyrene and heterocyclic aromatic
amines, on saturated fatty acid/mono-unsaturated fatty acid substitution, on saturated fatty acid/carbohydrate substitution and on soy.

:<sup>b</sup> topic also covered by QALIBRA, in preparation (quality of life – integrated benefit and risk analysis; www.qalibra.eu). QALIBRA is also preparing a BRA on functional foods
(phytosterols).

:<sup>c</sup> topic also covered by BENERIS, in preparion (benefit–risk assessment for food: an iterative value-of-information approach; http://en.opasnet.org/w/Beneris). BENERIS is
also preparing two BRA’s on fish consumption.

:<sup>d</sup> European Food Safety Authority.

:<sup>e</sup> [Dutch] National Institute for Public Health and the Environment.

:<sup>f</sup> Our Food Our Health.

:<sup>g</sup> Panel on Contaminants in the Food Chain.

==Benefit–risk management of food and nutrition==

Benefit–risk management entails the process of weighing policy
alternatives in light of the result of a benefit–risk assessment and
other relevant evaluation.

Currently, the interaction between risk assessment and risk
management on several occasions during the assessment process
is generally advocated and incorporated in the benefit–risk frameworks
(EFSA, 2010b; Fransen et al., 2010; Hoekstra et al., 2010). Active
interaction is necessary to ensure that the assessment will
meet the needs and answer the concerns of the risk manager,
and the analysis is seen as an iterative process (FAO/WHO, 1997).
As a principle, however, the appropriate functional separation,
i.e. different teams and preferably different agencies, is also advocated
(FAO/WHO, 1997; Palou et al., 2009). Separation is important
to ensure and show independency and objectivity of the scientific
process of risk assessment (FAO/WHO, 1997; Palou et al., 2009).
This is thought to enhance consumer trust in the system (Palou
et al., 2009). In the European Union, these ideas and a number of
food crises have resulted in the founding of EFSA (www.efsa.europa.
eu), the independent risk assessment body that provides scientific
opinions and advice as a foundation for European risk
management decisions taken by the European Commission, the
European Parliament and European Union Member States. Separation
is, however, thought not to be sufficient to create trust; transparency
of the scientific advice is thought to be vital or and a key
issue in many approaches (EU, 2000; Frewer and Salter, 2007; Jensen
and Sandoe, 2002).

Historically, the approach of regulatory agencies has been to
work from the food safety side and control the health risk of chemicals
to zero or negligible levels. There has been ample debate over
the degree to which this so-called precautionary principle should
be adhered to (Post, 2006). In more recent years, it has been advocated
that there are probably many other food-related health risks
that need to be preferentially addressed rather than keeping minor
toxicants to zero or negligible level. An ad libitum food intake, for example, has considerably more impact on health risk (Kasamatsu
and Kohda, 2006; van Kreijl et al., 2006). Also, it is increasingly recognized
that accepting some risk in order to achieve more benefits
is as much a risk management decision as is focusing on food safety
and disregarding benefits. The food safety system controlling contaminants
in food has been very successful in recent decades, but
needs to continue to adapt to new scientific insights and developments
in food supply and food consumption (van Kreijl et al.,
2006), as should management.

In the end, the decision-maker decides what degree of risk is
acceptable. The benefit–risk assessment is placed in a larger context;
it is weighed against issues such as cost-efficiency and equity
(Anand and Hanson, 1998).

The benefit–risk assessor must present his/her results in a form
which is useful to the decision- maker, who acts on behalf of the
public, so that some consideration of public perception is essential
(Jensen and Sandoe, 2002); if a (benefit-)risk analysis cannot be
understood, it cannot be used (Wilson and Crouch, 2001).

==Benefit–risk communication of food and nutrition==

Benefit–risk communication entails the interactive exchange of
information and science-based opinions concerning the comparison
of benefits and risks among assessors, managers, consumers
and other actual or potential stakeholders.

Communication on risks and benefits of food consumption is
not by default an integrated process. However, the importance to
provide consumers with information about both the risks and benefits
of food consumption in integrated messages is being more and
more advocated (FDA, 2009b). Yet, consumer responses to such
risk-benefit communications need to be better understood. Understanding
perception is important to promote better health and
avoid undesirable consequences (for a more detailed discussion
on consumer perception, see Ueland et al., 2011).

Many general psychological mechanisms should be taken into
account when presenting the assessment (interim-) results. Important
factors which influence the perception of the ‘objective’
assessment data are effect catastrophality, controllability, voluntariness,
reversibility, information framing, individual’s state of
mind and knowledge (understanding, familiarity) and trust (van
Kreijl et al., 2006). It has long been recognised that in general,
the acceptability of risk is much lower than that of benefits (Starr,
1969). This is related to theories which explain that people tend to
value their own property higher (‘endowment effect’) than that of
others and that they have higher value for what they lose than
what they may gain (‘loss aversion’). A consequence of this is the
preference for risk-reduction instead of (health) gain. Beneficial effects
as expressed in claims are required to be understood by the
average consumer. Early research, however, shows that consumers
have trouble differentiating between graded levels of evidence and
make little or no distinction between nutrition and health claims
(Verhagen et al., 2010). Furthermore, in people’s minds risk and
benefit are often inversely correlated. To what degree depends
not only on knowledge (the ‘analytic’ system) but also how they
feel affectively (the ‘experiental’ system) about those risks and
benefits (Alhakami and Slovic, 1994). This so-called ‘‘affect-heuristic’’
also influences the processing of numerical information and as
such also influences the interpretation of dose-response information
(Peters et al., 2007). Difficulty of working with probabilities,
especially dose-response insensitivity, occurs most when the outcomes
are highly emotional. Furthermore, there is widespread
underestimation of the role of the unconsciousness in decisionmaking
and behaviour (Custers and Aarts, 2010; Dijksterhuis
et al., 2005; Slovic et al., 2004).

So far, benefit–risk communication case studies have focussed
on fish consumption. In a study on consumption of sports-caught
fish in the US, respondents appeared to be influenced more
strongly by risk–risk comparisons (i.e., risks from other foods vs.
risks from fish) than by risk–benefit comparisons (i.e., risks from
fish vs. benefits from fish) (Knuth et al., 2003). The authors conclude
that risk assessors and risk communicators should improve
efforts to include risk–risk and risk–benefit comparisons in communication
efforts, and to clarify to whom the health risks and
benefits from fish consumption may apply (Knuth et al., 2003). In
a Belgian study, comparing risk-only, benefit-only, benefit–risk
and risk–benefit communication strategies with respect to fish
consumption, it was found that the latter two did not yield a significant
change in behavioural intention, though they did negatively
affect attribute perception (Verbeke et al., 2008). Within the QALIBRA
framework, consumer perceptions of the adequacy of the current
information provided about health risks and benefits
associated with food consumption have been explored, and consumer
perceptions and responses to several integrated health metrics
have been examined. Furthermore, preferences of assessors
and managers for the communication of benefit-risk assessment
outputs have been examined. Results from these studies are now
being published (van Dijk et al., 2011).

==Conclusions and recommendations==

The concept of benefit–risk analysis stems from the recognition
that food has both benefits and risks, that there are situations in
which the benefits outweigh the risks, and that focus on food
safety alone does not do justice to a well informed public health
system, where much health can be gained by prevention of disease
occurrence through better food and nutrition.

Both in concept and practice, benefit–risk assessment entails
more than adding a benefit assessment to a risk assessment and requires
adaptations in both benefit and risk assessment to provide a
weighed estimate. On the risk side, it is recognized that dose–response
relationships directly relevant to humans are needed. Thus,
attention needs to be redirected from the description of safe levels
to the description of dose–response functions covering also the
lower levels. This means adaptation of design and use of modeling
techniques in animal studies as well as adaptation of design and
better data presentation in human studies. With regard to the
establishment and scope of benefits, more experience is needed.
There is agreement, however, that the relation between a food
(component) and its benefit(s) needs to be scientifically characterized.
Along with the movement towards considering both benefits
and risks, there is also a movement away from assessing solely the
needs of the most susceptible individuals and applying the results
to the whole population, towards considering more the individual
differences.

In addition to integrating risk and benefit assessment approaches,
there also needs to be special attention for the way the
outcome of this process is expressed. The possibilities and limitations
of integrated measures need to be further examined and
brought under the attention of a wider scale of users. A benefit–
risk assessment conclusion should not merely state that ‘the benefits
outweigh the risks’, or vice versa, for average or susceptible
persons, but it should designate continued ways to maximize benefits
while minimizing risks. Public health (promotion) is not a status
quo but a dynamic process.

Even with developing benefit assessment, risk assessment and
benefit–risk assessment fields, a truly quantitative benefit–risk
assessment will often not be possible because of lack of data. If
quantification is possible, it should not yield a false sense of security
or insecurity and thus it is recommended that a benefit–risk
conclusion be always accompanied by a narrative of the major
uncertainties and assumptions in the assessment. Furthermore, the benefit–risk assessment should be contextualised by issues
outside the assessment (such as equity).

Overall, benefit–risk assessment, provided it is carefully explained
and interpreted, is a valuable approach to systematically
show the current knowledge and its gaps and to transparently provide
the best possible science-based answer to complicated questions
with large potential impact on public health.

==Conflict of Interest==

The authors declare that there are no conflicts of interest.

==Acknowledgements==

The preparation of this manuscript was funded through Safefoodera
project BEPRARIBEAN (project ID 08192) by the Dutch
Food and Consumer Product Safety Authority (VWA), the Research
Council of Norway (RCN) and the Nordic Council of Ministers
(NCM) and supported by Matís, The National Institute for Health
and Welfare (THL), the University of Ulster and the National Institute
for Public Health and the Environment (RIVM).

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