RISKY GENOMICS: THE ROLES OF RISK/BENEFIT ASSESSMENT AND RATIONAL DECISION FRAMEWORKS
Robert C. Lee
Background
Risk management is important with regard to new
or emerging technologies or programs that have
associated risks. Risk is typically defined as the probability
and severity of an adverse event1, as distinguished from
benefit, which can be defined as the probability and magnitude of
a positive event. The "precautionary principle" has
been advocated as policy or guidance in many risk
management scenarios. I focus here on risk management
involving genomic technologies (defined here as any application
of genomic science, i.e., genetic manipulation of
biota). Specifically, genetically modified organisms (GMOs)
have been the center of vigorous debate.
Definitions of the precautionary principle seem to be
as numerous as the stakeholders involved in these
decisions2, so for discussion purposes I will assume that, in a
broad sense, "precaution" implies managing a risk before
an adverse event occurs; e.g., imposing an intervention
that prevents or reduces the probability of the event.
Precaution could also imply having a mitigation plan in place
designed to manage the adverse consequences of an event once
the event occurs, although this usage is not as common.
Being precautionary is a reasonable and rational course of
action and indeed is plain old common sense, assuming one
has good knowledge regarding the risks. Risk
management becomes more difficult, however, when the probability
and/or the severity of an adverse event are uncertain, which
is the case with many worrisome low-probability,
high-consequence risks such as those associated with
environmental toxicants, human influenced climate change,
and now GMOs.
Risk management under a state of
uncertainty
In the face of uncertainty, decision-makers may make
good decisions, or they may make two classes of risk
management judgement errors. They may err in terms of,
say, waiting too long to impose controls on a risky
technology (thus potentially resulting in the adverse
consequences actually taking place). Conversely, they may err in terms
of imposing controls too soon or inappropriately
without proper evaluation of the risks, benefits, and costs of
the technology (thus potentially resulting in inefficient,
costly, useless, and/or risky controls). An associated
problem arises when a high degree of uncertainty exists (or
indeed when unanticipated risks exist), or when
psychosocial factors (such as dread of exotic "Frankenstein"
type technologies, inappropriate media coverage, etc.)
are important. In such cases the perception of risk (as
opposed to the "true" nature of risk in terms of probability
and severity) and risk attitudes become particularly
important3. Risk perception and risk attitudes, and more
generally, stakeholder values can be highly variable across
those stakeholders because of factors such as cultural
background, education, employment, and socio-economic
status. In cases of technologies that may potentially affect
a spectrum of stakeholders both positively and negatively, it
is obvious that such stakeholders' values should be
explicitly considered in terms of a decision-making process,
although in fact this is rarely done (unless prompted by legal
action). The consequence of not involving stakeholders is often
a decision based on fear and politics, as opposed to reason.
So, how does one go about being "precautious" when
one does not know exactly what event will happen, whom
the event will affect, and the nature of the
consequences? What happens when risks that are perceived to be
important by some stakeholders are perceived as trivial
by others? What are the tradeoffs involved and implications
of controlling some technologies and not others?
Typical precautionary principles are at the least simplistic
and potentially harmful, because they generally fail to
address these and similar considerations.
Risk assessment
Risk assessment informs decisions made under a state
of uncertainty and is defined as the science and process
of characterizing and quantifying risks. Proper risk
assessment (often termed "probabilistic" risk assessment)
also involves characterizing and quantifying uncertainties.
For many years scientists and decision-makers have
recognized the value of risk assessment in making risk
management decisions regarding industrial technologies such as
nuclear power, as well as unintended risks such as
environmental contamination4. An important distinction must be
made: Risk assessment is not risk
management; the former is a scientific process that informs the latter. A risk
management approach that does not explicitly incorporate
risk assessment and uncertainty assessment ignores
important information, and thus is inherently flawed.
Benefits assessment
Of course, important decisions regarding technologies
are not based on risk alone. Any particular technology
worth developing is not just risky, but presumably has
some benefits to its proponent/developer and others.
These benefits also may be highly uncertain. Essentially, the
same approaches and techniques used for risk assessment
can be used for "benefits assessment." Interestingly, even
from a business perspective the economic benefits of
new technologies are often unclear. For example,
biologically produced pharmaceuticals are a "hot item," but the
economic benefits of growing drugs in field plants given
the present and likely future regulatory climate are far
from clear. When the perspectives of multiple
stakeholders (such as consumers, growers, and regulators) are
introduced, as they should be, the benefits become much
less clear, making components of the genomic
technology industry reminiscent of the "dot.com bubble."
There appears to be a prevalent, highly naïve assumption that
any new genomic technology, especially those with health
care applications, will confer some net benefit to its
developers and consumers. Regardless, approval and
implementation of technologies are contingent on some sort of
balance between associated benefits and risks, whether
explicitly stated or not.
Tradeoffs
Consideration of both benefits and risks introduces
the additional concept of tradeoffs. Economic costs enter
the equation in many ways, but are often viewed as a
constraint on resources (e.g., a technology that costs too
much may not be worth developing, even though the
benefits may be great). Additionally, when a risk
management approach is taken, more tradeoffs are introduced.
Risk management always costs something. These costs may
be economic, either in terms of up-front costs or
opportunity losses (i.e., money spent on managing a particular
risk could be spent otherwise). Costs may also be
associated with foregone benefits or competing risks introduced by
the policy, e.g., the foregone health benefits associated with
a genomic therapy that is not allowed because of the
risks. Uncertainties also introduce tradeoffs. A tradeoff
exists between the potential for false negatives (i.e., something
is judged "safe" when it is really risky) and the potential
for false positives (i.e., something is judged "risky" when it
is indeed safe) in risk management decisions.
A risk management approach that does not
explicitly address tradeoffs is flawed and can result in inefficient
or even harmful decisions. For instance, delaying approval,
on the basis of uncertain risks, of a novel life-saving drug
that is a result of genomic technology could result in loss of
life of patients who may benefit.
Being precautionary
The science of genomic technology is advancing at
an extremely rapid rate. Unfortunately, this rate is far
outstripping the ability for decision-makers to evaluate the
benefits, risks, and costs under present regulatory and
advisory systems and risk assessment protocols. This may be
a reason why precautionary principles have been
proposed as ways to manage these rapidly advancing
technologies. To be clear, a precautionary principle is an
ex ante risk management strategy.
An example of a simple precautionary principle states
that a state of uncertainty does not preclude making a
decision. This is common sense. However, advocacy-based
versions state that risk management action should be taken in
the face of a risky technology, and indeed that certainty
of safety must be demonstrated by a proponent of a
technology2. This is simplistic and indeed irrational risk
management. Rather than engaging and informing
stakeholders, such precautionary risk management takes a
rather paternalistic approach, essentially imposing the values of
a few experts or advocacy groups on a large spectrum
of stakeholders. Such a precautionary principle
hamstrings true stakeholder involvement and proper risk
management by constraining elicitation of stakeholder values and
the scientific process of risk/benefit assessment with
pre-determined conclusions and the refusal to
acknowledge tradeoffs. In its most inappropriate implementation
the precautionary principle can indeed impede
scientific progress, much of which is targeted toward improvement
of the human condition. No drug, therapy, or surgical
procedure would ever be approved if it had to be risk-free.
Note that rigorous interpretation of precaution potentially
also results in a rather nonsensical scenario; i.e., if all
actions must be certified as "safe" before implementation,
then should not this apply to the precautionary principle itself?
Decision frameworks
An appropriate, rational risk management process
will integrate the science of risk/benefit assessment with
the policy of risk management, but to my knowledge this is
not occurring to date with GMO issues. It is unfortunate
that many of the same mistakes that have been made
historically in environmental toxicology and other fields are
now being made with regard to GMOs; history appears to
be repeating itself. The appropriate and acknowledged way
to evaluate highly uncertain risks is to employ risk
assessment as a scientific process within an explicit decision
framework that directly addresses stakeholder values,
tradeoffs, and the impact of uncertainty on decisions.
The term "requisite model" refers to a policy model
that contains everything essential for informing the issue
at hand5. Questions that we may ask to develop such a
model include:
• Who are the (legitimate) stakeholders?
• Who bears the consequences of the decision?
• Who is responsible for making/implementing/enforcing
the decision?
• What do the stakeholders care about?
• What are their preferences for different outcomes?
• What trade-offs are they willing to make among
different consequence dimensions (e.g., cost vs. safety)?
• What are the competing decision options to be evaluated?
• What information do the stakeholders need to make
well-informed decisions? What questions are they asking?
• What information is immediately available about the
probable consequences of different decisions?
• What data gaps and uncertainties exist, and what means
exist to reduce uncertainties?
• What analytic tools and experts are available?
• What are the resource and time constraints on making
the decision?
There is a wealth of literature and applications
regarding analytic frameworks appropriate for such evaluations,
such as multiattribute utility theory (MAUT) and
multi-criteria decision-making (MCDM)4. It is unclear why such
frameworks are not being used to help sort out complex
decisions involving genomic technologies, but simple ignorance of
the methods may be a reason.
MAUT provides a way of incorporating stakeholder
values, preferences, and risk attitudes directly into a
transparent, quantitative analytic framework that addresses
uncertainties and tradeoffs. Risks, benefits, and costs are
balanced in utility functions, which are summary measures of
each alternative's attainment of decision objectives based
on stakeholder values.
Another approach for addressing multiple objectives
is multi-criteria decision-making (MCDM) in which
"inferior" alternatives are eliminated in a systematic fashion,
rather than attempting to rank all alternatives. MCDM
methods allow for incomplete, ambiguous, and uncertain
preferences. They recognize that, while some pairs of alternatives
may not be comparable, it may be unnecessary to
explicitly compare them to identify the preferred alternative.
While approaches such as MAUT and MCDM do not,
by any stretch of the imagination, provide all the
"answers," they do provide a transparent, balanced basis for
informing a decision. These methods have been around for
decades, and are routinely applied in many different types of
risk management decision-making (such as in engineering
and in environmental management). However, genomic
technologies pose some special challenges; indeed the
potential benefits and risks associated with such technologies
may eventually exceed those associated with any other class
of technology, and uncertainties are tremendous. There is
a pressing need for research that will elucidate the best
ways to inform genomic technology decisions. Current
risk assessment protocols are inadequate to capture the
full range of risks and uncertainties, both short-and
long-term. As noted above, there is a great need for efficiency
in conducting assessments; the rate of development of
the technologies is continually increasing, and risk
managers are already overwhelmed. Thus, methodological
development and capacity-building for applied assessments
are critical. This is not an academic exercise; these are
real, very difficult decisions.
It is time to acknowledge that simplistic policy
approaches such as the precautionary principle are insufficient
to address the complex decision-making that is
associated with genomic technologies. It does not take a
complex analysis (nor a rocket scientist) to predict that regulation
of such technologies without informing decisions via
risk/benefit assessment, incorporation of stakeholder values, and
rigor-ous assessment of uncertainties and tradeoffs will result
in arbitrary decisions and negative consequences for society.
