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Measuring the economic value of genetic counselling
Katherine Payne and Martin Eden
Manchester Centre for Health Economics, Division of Population Health, Health Services
Research & Primary Care, The University of Manchester, Manchester, M13 9PL, UK
Correspondence to: Katherine Payne ([email protected])
Keywords
Genetic counselling
Cost effectiveness analysis
Cost benefit analysis
Opportunity cost
Value
Capability
Complex intervention
1
Abstract
The introduction of new genetic sequencing technologies and other advances in genomics are
likely to stimulate the development of new models of service delivery for genetic counselling,
perhaps in some instances, moving genomics into mainstream care. New models of service
delivery for genetic counselling will compete with existing traditional approaches and also other
uses of finite budgets for healthcare provision. Within this context of evolving models of genetic,
and perhaps genomic, counselling we describe if, and how, it is possible to measure their
economic value to support the informed introduction of new models of service delivery for
genetic counselling into healthcare systems. We outline the need to be cognisant of opportunity
cost and how methods of economic evaluation can be used to provide evidence of the
incremental costs and consequences of models of service delivery for genetic counselling that
can be best described as a complex intervention. We provide a brief description of the two main
types of economic evaluation, cost effectiveness analysis and cost benefit analysis, and the
underlying normative foundations of these methods. We then describe how to begin to value the
consequences of genetic counselling, which addresses similar challenges posed by other complex
interventions that can result in consequences other than health status as measured by the EQ5D
and Quality Adjusted Life Year (QALYs). To date, no practical solution exists to value
consequences beyond health status and which accounts for opportunity cost. A taxonomy of
value when using an intervention for the provision of genomic-based diagnostic information has
been suggested but further empirical work is, however, necessary to understand if, and by how
much, society is willing to offset such gains against health status to measure the economic value
of genetic counselling.
2
Introduction
Genetic counselling requires a myriad of skills and can draw on a range of information sources,
including results from genetic tests. Genetic counselling typically involves provision of a
diagnosis, risk estimation, counselling, surveillance and support to people with inherited, often
rare, genetic conditions1. No single model of genetic counselling exists, and the provision of this
service, currently viewed as a specialist service often with distinct commissioning process2, can
be subject to extensive variation within and across countries3. Within these varied models of
service delivery used by genetic counsellors, the majority of available genetic tests still aim to
identify the causative single genetic variant that results in an observed phenotype (clinical
expression). However, the advent of next generation sequencing technologies, generating new
genomic-based diagnostic tests for rare inherited disorders, have the potential to drive the
development of new approaches of service delivery towards the perhaps more aptly named
genomic counselling. This move from using genetic tests to identify germline or somatic
mutations (or pathogenic variants) that underlie high-risk, single-gene disorders towards
genomic tests, which are often applied to heterogeneous conditions, polygenic conditions or
conditions with unknown genetic causes also has the potential to move ‘genomic’ counselling
into mainstream healthcare away from the confines of the specialist service. Within this context
of evolving models of genetic, and perhaps genomic, counselling this Commentary aims to
describe if, and how, it is possible to measure their economic value to support the informed
introduction of new models of service delivery for genetic counselling into healthcare systems.
Genetic counselling: a complex intervention?
To be able to measure the economic value of genetic, or genomic, counselling (hereafter genetic
counselling for simplicity) the first necessary step is to be able to clearly describe the nature of
the intervention that comprises the model of service delivery. Analogies have been made about
how, in the context of genetic counselling, the models of service delivery fit within the Medical
Research Council (MRC) definiton of a complex intervention, comprising several interacting
components4,5. Importantly, genetic counselling also has a number of potentially relevant
outcomes linked with the multiple goals that centre around the need to manage the emotional
effects of genetic conditions, diagnosis provision and coordination of management and
surveillance strategies6-9. Genetic testing maybe embedded within models of service delivery of
3
genetic counselling. A simple typology of genetic tests can be defined: a test with no treatment
and a test with a treatment option, be that surveillance or management options. The discipline of
genetic counselling, ideally sees genetic testing as being inextricably linked with genetic
counselling both in terms of the process of offering a diagnosis but also in terms of test outcomes
being reliant on the ‘success’ of counselling5. In some ways the outcomes of genetic testing, and
genetic counselling, are only as good as the impact of subsequent management, surveillance or
treatment decisions. There is, however, also an argument that genetic counselling, and genetic
testing, have inherent value in themselves.
Opportunity cost and economic evaluation
To measure the value of genetic counselling it is necessary to be clear about the theoretical basis
underpinning the construct of value being measured. This Commentary assumes that value is
being measured in keeping with economic theories. The concept of opportunity cost is the
building block for economic theories and recognises that if there is a finite amount of money in a
system then choices have to be made about how to best spend the funds and use the resources
available. Genetic counselling is generally viewed as a specialist service2. In NHS England, for
example, genetic counselling is offered under the remit of medical genetics, which is
commissioned from a finite budget set over a specified time period, for example, one year. This
means there is a finite amount of money in the healthcare system to spend on interventions, such
as medicines and surgery, but also specialist services, such as genetic counselling. Within this
context, allocating resources for one intervention for specified populations of patients means that
those same resources can no longer be used elsewhere for the care of other (known or unknown)
populations of patients. The impact of opportunity cost can be identified by using methods of
economic evaluation that offer a structured framework to quantify the costs and consequences of
potential alternative uses of the healthcare budget10. Importantly, when using economic
evaluation, two fundamental aspects must be remembered. The outputs from an economic
evaluation suggest the most efficient use of resources and can indicate what ‘should be’ done at
the population level. The findings from an economic evaluation are only one type of information
that can be used towards evidence-based practice and using the results of an economic evaluation
requires careful appraisal and interpretation by the specified decision-maker charged with
making resource allocation decisions for their jurisdiction.
4
In general, economic evaluations involve identifying the costs and consequences of an
intervention and its relevant comparators10. All economic evaluations must have a comparative
element because the methods are underpinned by the concept of opportunity cost (alternative
uses of a budget). The data for economic evaluations can come from prospective studies, such as
trial-based economic evaluations, or from studies that use structured methods to assimilate data
from various sources, such as decision-analytic model based economic evaluations11. A number
of types of economic evaluation are available for use by analysts and described in detail in
published texts, such as, the seminal text by Drummond and colleagues10. Payne and colleagues
have also provided a description of each method in the context of evaluating genomic-based
diagnostic tests12. All economic evaluations use the same general approach to quantify the costs
of each intervention under consideration by identifying the relevant types of resources and
attaching unit costs to calculate the total cost of the intervention and comparators. Relevance in
this context is specified by the defining the study perspective (which resources are of interest)
and time-horizon (how long will the intervention impact on costs) for the economic evaluation.
Examples of study perspectives could be that of the healthcare service, as used by the National
Institute for Health and Care Excellence as part of their appraisal programmes13, or, the societal
perspective, as used by the Dutch National Healthcare Institute (ZIN)14. The time-horizon should
ideally be sufficiently long enough to capture all the relevant impacts of an intervention, for
example, the life-time of the population using the intervention13,15. In some instances, however, a
shorter time-horizon may be sufficient. The challenges associated with identifying and
measuring the costs associated with complex interventions are transferable to genetic
counselling. Such challenges are described in detail in published texts16,17. This commentary
focuses on describing how to begin to value the consequences of genetic counselling as a
complex intervention. In general, many of the challenges of valuing the consequences of genetic
counselling are the same as other complex interventions but there are some specific issues12,15.
Approaches to economic evaluations differ in terms of how the consequences are identified,
measured and valued. In practice, two types of economic evaluation, cost-effectiveness analysis
(CEA) and cost-benefit analysis (CBA), are used to inform resource allocation decisions, with
different degrees of application in different jurisdictions and settings. The methods of CEA are
5
the preferred type of economic evaluation used in the healthcare context in most jurisdictions14.
The term CEA is sometimes interchanged with the term ‘cost-utility analysis’ (CUA) and
hereafter is referred to as CEA because they use the same normative principles to underpin the
method. The use of CBA is currently limited to non-health public programmes such as
transportation and environmental interventions18. In some jurisdictions, such as France14, the
method of cost consequences analysis (CCA)19, which presents decision-makers with a number
of outcomes in a disaggregated format, is suggested as a possible method. The results of an
economic evaluation can be used to inform what ‘should’ be done in terms of making the best
use of available resources. Providing information about what ‘ought’ to be done also requires a
clear set of underlying value judgements about ‘what ought to be’ (normative principles)20-22. The
normative principles for CEA and CBA are extra-welfarism and welfarism, respectively23,24.
Some economists suggest that using CCA is not informed by a clear set of normative principles,
which is why the method should not be used25,26. This does not mean, however, that CCA is not
used and has become a method used in the context of developing public health guidance by
NICE27.
A full exposition of the meaning and interpretation of extra-welfarism and welfarism is beyond
the scope of this Commentary. Brouwer and Cuyler provided an excellent overview and aim to
describe the clear differences between each set of normative principles23. In some ways it is
possible to view the two normative principles as discrete, and relevant to CBA (welfarism) and
CEA (extra-welfarism) respectively (see Figure 1). In actuality there is sometimes overlap
between the welfarism and extra-welfarism in terms of how the outputs are mistakenly
interpreted, which makes the application of these normative principles sometimes more
challenging to understand and use in practice than the theory suggests. The practical application
of extra-welfarism, such as that used by NICE, supports the assumption that decision-makers,
who are spending a healthcare budget, want to get the most ‘health’ as possible (maximise health
gain) for the population funding the healthcare service13. Therefore, the results of a CEA can lead
to the promotion of efficiency because it can show how a specified output (health) is maximised
for the given (specified relevant to the study perspective) inputs (healthcare costs).
Figure 1: Characteristics of CBA and CEA
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Welfarism states that the value of an intervention should be assessed in terms of whether it is
‘optimal’ for the individuals who collectively make up the relevant society23. In this context, the
concept of ‘utility’, defined as the desirability or preference for a certain state of the world,
should be valued on the basis of the preferences of individuals. A specific type of methods,
called contingent valuation (because the method uses a hypothetical scenario to describe the
‘contingent’ market to be valued) that elicit willingness to pay (WTP) are used to attach a
monetary valuation of the perceived benefit for the intervention. A sample of individuals can
then be asked to state a WTP for an intervention and this valued monetary benefit can then be
used to estimate a societal value. This value can be combined with the anticipated costs to
determine the net benefit of an intervention (in a CBA)10. The observation that there are very few
examples of full CBA in healthcare may be the result of more than one limiting factor27,28. There
are many known methodological challenges that must be solved when applying contingent
valuation methods to measure consequences. Using this method may result in different socio-
economic sectors in society being favoured over others, because the measure of WTP implicitly
incorporates ability to pay. Perhaps most importantly though is that is many jurisdictions, such as
the UK, decision-makers have taken a pragmatic approach and favoured the use of CEA and
choosing interventions based on maximising health.
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A focus on health as the consequence
In keeping with the normative principle of extra-welfarism, as currently implemented in most
jurisdictions, using CEA requires an instrument, or set of instruments, that can identify, measure
and value health. Such an instrument should ideally measure and value health in a consistent way
to enable comparisons of interventions within the health-care sector, or with other sectors, where
‘health’ is the outcome of interest. In practice, health is identified more specifically as health
status that can be described using generic or disease specific measures10. The most commonly
used measure of health status is the EQ-5D29. This measure is generic (can be applied in any
healthcare setting), multi-attribute (made up of five domains: mobility; pain; self-care; ability to
care for oneself and anxiety/depression) and available in different versions. For example, one
version of the EQ-5D contains three levels (EQ-5D-3L), that asks respondents to rate their
current health for each of the five domains assuming only three possible levels (no problems;
some problems; extreme level of problems), and a newer EQ-5D-5L version uses five levels (no
problems; slight problems; moderate problems; severe problems, extreme problems). Once
health status has been measured, using for example the EQ-5D, it is then necessary to understand
how each ‘health state’ is valued. The process of valuing health status is done by generating a set
of preference scores, generally anchored on a cardinal scale ranging from zero (representing
death) to one (representing perfect health); but scores less than zero are feasible if a health state
is viewed as being as worse than being dead. A method called time-trade off was used to
generate the values for the preference scores and a social tariff for the health states described by
the EQ-5D-3L in a representative sample of the UK population30. It is these preference scores
(weights) for each health state described by the EQ-5D that then allows the change in quality-
adjusted life years (QALY) for comparative interventions to be calculated. QALYs are the
product of length of life (measured in years) and time spent in the health state measured using
the EQ-5D10. As measured in this way, a QALY is the value of being in a particular health state,
as described by the EQ5D, for one year.
There are limited examples of CEA being used in the context of evaluating models of service
delivery for genetic counselling. In general, these published examples have relied on using
intermediate outcomes, such as the impact on anxiety or knowledge about a specific genetic
condition, rather than being able to capture the impact on health31-33. In theory, there is no reason
8
why a prospective evaluation of a new model of service delivery for genetic counselling should
not use the EQ-5D as the relevant measure of the consequence of the intervention. Davidson and
colleagues conducted a feasibility study of using different types of outcome measures, including
the EQ-5D-3L, to capture the impact of a standardised genomic care model, which included a
genetic counseling component, for inherited retinal dystrophies34. This feasibility study did
illustrate one of the most likely challenges with using the EQ-5D to measure the consequences of
a model of service delivery for genetic counselling; the presence of ceiling effects. Ceiling
effects are present when a considerable proportion of people complete an outcome measure, such
as the EQ-5D-3L, giving the highest possible score35. If ceiling effects are observed then the
outcome measure will be unable to show improvements in the specific consequence at the
highest extreme of the measure’s scale. The likelihood of ceiling effects when using EQ-5D in
CEA of genetic counselling services will be relatively high as a large proportion of the
population using such services will be symptom-free, but maybe at risk of a genetic condition. A
more fundamental challenge with using the EQ-5D, however, is deciding whether health status is
the relevant outcome to use when evaluating models of service for genetic counselling.
Beyond health status: a normative view
Genetic counselling, with or without embedded genetic testing to aid a genetic diagnosis, has
many potentially relevant consequences6,8 36. If it is accepted that the relevant focus is how best to
spend the healthcare budget then maximising health status must be a necessary goal, and
consistent with an extra-welfarist perspective, used as the consequence of relevance in a CEA.
Furthermore, to allow comparison with other interventions, be they ‘simple’ interventions such
as medicines or other complex interventions, then understanding the impact on health status and
consideration of health (gained or foregone) is a fundamental requirement. There is, however,
empirical evidence that maxmising health status is not the only goal of genetic counselling6,8,37-39.
The relevant question then becomes: What should we do (and can we do) if health status is not
generally perceived to be the relevant consequence for an intervention funded by the healthcare
budget? Some commentators have suggested a relevant concept to value the benefits of genetic
testing, and so logically, genetic counselling, is ‘personal utility’. Personal utility has been used
to refer to the potential value resulting from the genetic information derived from a test in its
own right but this concept is not underpinned by theory and no measure of personal utility
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exists40-42. Taking account of economic theory and available methods, one approach to move
beyond health status would be to invoke a welfarist view. This would mean using willingness to
pay to value the consequences, hence capturing the impact on health status and non-health status.
In a pilot study, we tested this approach, developing a survey that used the contingent valuation
method to elicit willingness to pay for getting a genetic diagnosis for the cause of an inherited
eye condition called retinitis pigmentosa (RP)43. This pilot study aimed to understand if a sample
of people with prior knowledge of RP (n=25) and those without prior knowledge (n=27) would
seek genetic counselling and would want a genetic test in addition to counselling? Employing a
‘bidding game’ technique we elicited how much they would pay for genetic counselling for RP
with and without genetic testing. The overall median WTP for genetic counselling was £224.50
compared to £524.50 for counselling plus testing, illustrating the possibility of quantifying
economic value using this method. Intuitively, the elicited values had face validity, because
testing was valued more highly than counselling alone, but this was a small study and it is not
clear if the findings were generalisable to a wider population. Importantly, the impact of ability
to pay could not be understood in this small pilot study. These values could have been used in a
subsequent pilot CBA, which would have shown whether the consequences of genetic testing for
RP outweighed the costs. Remember though that using CBA uses an evaluative framework that
is consistent with the normative principle of welfarism and a decision maker would then have to
be able to compare with the results of CEA that is underpinned by the different normative
principle of extra-welfarism. Buchanan and colleagues have already outlined the challenges with
doing this in the absence of a set of clear decision rules44.
There is emerging work by health economists to consider how to move beyond health status, as
measured using the EQ-5D, to capture the broader consequences of complex interventions, for
example those use in mental healthcare, public healthcare and social care, using the concept of
capability45. Measures of capability that have been developed for use in economic evaluations,
are linked with Amartya Sen's capability approach that suggests wellbeing should be viewed in
terms of an individual's ability to 'do' and 'be' the things that are important in life46. Examples of
such measures include the suite of ICECAP (ICEpop CAPability measure) instruments47. Using
the concept of capability would keep the evaluative framework consistent with that of CEA and
the extra-welfarist view of the world. Payne and colleagues completed a series of empirical
10
studies that suggested the consequences associated with genetic counselling could be captured
using a concept related to the capability to make an informed decision (in the study referred to as
‘empowerment’)5. This empirical approach stopped short of developing a measure of capability
to make an informed decision. Subsequent work by Davison and colleagues did explore using a
measure of capability, the ICECAP for adults, in the feasibility pilot study to test how to evaluate
a standard genomic care model for inherited eye diseases. This study showed negligible changes
in ICECAP-A from baseline to one-month follow-up scores, suggesting either that one month
was not the most appropriate follow-up time period or that ICECAP-A was not the best
instrument to detect the consequences of a standardised genomic care model34. Intuitively the
latter reason seems reasonable given that ICECAP-A was developed to detect the impact on
functional, rather than cognitive capability, suggesting further empirical work is needed to
generate a capability-based instrument that captures the impact on the relevant consequences of
genetic counselling.
Health status and beyond: back to opportunity cost
Payne and colleagues, appropriately suggested, that if non-health status consequences are to be
included as an additional factor in CEA of genetic counselling, this must be done by taking
account of opportunity cost5. Recognising that to be consistent with the economic evidence
produced for other healthcare interventions, it is necessary to maintain the extra-welfarist view,
the relative value of health status (as measured using the EQ-5D) compared with broader
(capability) or non-health consequences must be quantified. Recall that opportunity cost infers
people are willing to give-up one thing for more of another in terms of its relative value. To
move towards developing an instrument that can be used in CEA of genetic counselling, being
cognisant of opportunity cost when spending a healthcare budget, the vital first step is being
clear what is meant by value. Eden and colleagues have started this process by synthesising
existing qualitative evidence from 37 studies, using meta-ethnography to develop a taxonomy of
value for achieving a genetic diagnosis, using interventions such as genomic-based diagnostic
tests or genetic counselling48. The suggested taxonomy of value comprises three elements with
clear conceptual overlaps with the notion of capability: value of informed decision making;
intrinsic value of knowing; value of benefit to others. Knowing this taxonomy of value for the
information associated with achieving a genetic-based diagnosis facilitates the next required step
11
which is to understand if people are indeed willing to ‘trade-off’ different consequences such as
health status against these broader concepts of value. Fundamentally this would infer that health
status and other consequences can be considered to be direct substitutes but it is not clear that
this is supported by empirical evidence. There is some evidence, in the context of interventions
for older people, that health status and the broader concept of capability are actually
complements to a certain extent, in that you need a certain level of health before capability
becomes important49. To date, however, there is no evidence to support if, and by how much,
people are willing to offset some loss in health staus against a gain in value as defined by Eden
and colleagues.
Conclusion
Methods of economic evaluation are a useful source of evidence to understand how best to spend
a finite budget for the provision of healthcare interventions by measuring the incremental costs
and consequences of new approaches to healthcare including evolving models of genetic
counselling. It is clear that measures beyond health status maybe important to understand in the
context when valuing genetic counselling. The method of CBA, consistent with the welfarist
view of the world, in theory allows measurement of value beyond health status but it is not the
predominant method of economic evaluation used in practice. To generate an economic evidence
base that shows the value of genetic counselling that is consistent with that for other healthcare
interventions, it is necessary to maintain the extra-welfarist view of the world and use CEA. To
date, no practical solution exists to use CEA that takes account of consequences beyond health
status as measured by the EQ-5D whilst accounting for opportunity costs. A taxonomy of value
deriving from interventions which provide a genetic diagnosis has been suggested. Further
empirical work is, however, necessary to understand if, and by how much, society is willing to
offset such gains against health status to measure the economic value of genetic counselling.
12
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