Cost-effectiveness analyses of genetic and genomic diagnostic technologiesKatherine Payne, Sean P. Gavan, Stuart J. Wright and Alexander J. Thompson
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])
AbstractDevelopments in next-generation sequencing technologies have driven the clinical
application of diagnostic tests that interrogate the whole genome, which offer the
chance to diagnose rare inherited diseases or inform the targeting of therapies. New
genomic diagnostic tests will compete with traditional approaches to diagnosis,
including the genetic testing of single genes, and other clinical strategies for the
available finite healthcare budget. In this context, decision analytic model-based cost-
effectiveness analysis is a useful method to help evaluate the costs versus benefits of
introducing new healthcare interventions such as genomic diagnostic tests. This Review
presents key methodological, technical, practical and organizational challenges that
must be considered by decision-makers responsible for the allocation of healthcare
resources to provide robust and timely information about the relative cost-effectiveness
of the increasing numbers of emerging genomic tests.
Introduction Historically, genetic tests have focused on identifying the causative single genetic
variant that results in an observed phenotype (clinical expression). This form of testing
usually occurs in the context of specialist genetic center that provide a diagnosis, risk
estimation, counselling, surveillance and support to people with inherited, often rare,
genetic conditions1. Of note, no single model of genetic service delivery currently exists,
and there is extensive variation within and across countries2. Advances in next-
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generation sequencing technologies have yielded genome-wide diagnostic tests
(hereafter called ‘genomic tests’), which are based on either multi-gene panels, whole-
exome sequencing or whole-genome sequencing. Informed by both public and privately
funded research, genomic tests are emerging in countries with advanced market
economies4, such as the United States of America5,6, Canada7,8, France9 and the United
Kingdom10, and some genomic tests are starting to replace existing diagnostic
strategies, including in some instances genetic tests, in clinical practice3 (Table 1)11.
Practicing clinical geneticists understand the impact of the diagnostic odyssey on
patients and their families. Published evidence also highlights the detrimental effect that
not having a clear diagnosis of a condition that causes distressing symptoms can
have12. Genomic tests may offer an increased chance of diagnosis by moving the gene-
based diagnostic component to the front of the diagnostic care pathway13. Moreover in
some instances genomic tests have the potential to inform treatment decisions.
However, the existence of fixed annual healthcare budgets alongside the continued
development and emergence of new interventions to prevent or treat health conditions
provides decision-makers charged with the allocation of healthcare resources with an
enduring challenge. Evidence of the relative cost-effectiveness, which explicitly
quantifies the additional resource use (costs) and consequences (benefits and harms)
of a new intervention compared with other potential uses of a healthcare budget, can
provide decision-makers with useful information to guide resource allocation decisions
(Figure 1). Methods of economic evaluation, such as cost-effectiveness analysis (CEA)
and cost-benefit analysis (CBA), are available to obtain this evidence. In some
countries, such as the Australia and UK, national decision-making bodies use evidence
of cost-effectiveness to inform clinical guidelines or reimbursement decisions. However,
in contrast to most healthcare interventions, the nature of the decision-maker charged
with allocating finite healthcare resources to genetic and genomic technologies, and
associated diagnostic tests, is not well defined. Such decision-makers operate within
different levels of existing healthcare systems, and their roles and responsibilities differ
within and across countries.
This Perspective reviews the role of cost-effectiveness analysis in informing the
introduction of genetic and genomic tests for the diagnosis of inherited rare diseases or
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targeted therapies, with an emphasis on evaluative frameworks employed by national
decision-making bodies such as the National Institute for Health and Care Excellence
(NICE) under the remit of NHS England. We review the key elements to consider in the
cost-effectiveness analysis (CEA) of genetic and genomic tests and services, before
specifically describing the potential of decision analytic model-based CEA, as well as its
methodological, technical, practical and organizational challenges. The emerging
economic evidence base for genetic tests, but lack of associated evidence for genomic
tests, is also summarized.
The cost of sequencing The increase in the clinical application of genomic tests is generally credited to the
decline in the cost of sequencing, which was facilitated by the move from Sanger
sequencing to next-generation sequencing technologies. According to data from the
National Human Genome Research Institute (NHGRI), up until the end of 2007, the cost
of sequencing a whole genome was estimated to be around US$10million , which by
late 2015 had fallen below US$1,50014. Large public and private funding programmes
have the potential to drive down costs further by sequencing genomes at high volume
and generating substantial economies of scale15. Consequently, the sequencing cost of
whole-genome sequencing is on a trajectory to break the $1,000 barrier in the very near
future16. However, most cost estimates within the literature (including those produced by
the NHGRI for whole-genome sequencing) tend to focus largely on the procurement
and resources used to run platforms but do not factor in downstream analysis and
interpretation of data that would be needed for use in routine clinical practice17,18,19.
Hence, the currently available cost estimates of whole-genome sequencing are of
limited use to decision-makers (Figure 1), who require a broader understanding of the
costs of introducing genomic technologies into clinical practice.
Importantly, the ‘cost’ of a technology differs from the ‘price’ advertised to payers in a
commercial setting. Prices are likely to be marked up for profit or to cross-subsidise
other tests on offer. Websites such as Genohub [https://genohub.com/] provide a useful
source of prices from a global network of laboratories offering next-generation
sequencing-based tests, including whole-genome sequencing (price range $1,105–
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$16,420; prices as of October 2017) whole-exome sequencing (price range $534–
$7,637) and gene panels (price range $400–$5,800). The cost for a complete
‘sequencing service’, includes DNA preparation and is dependent on the number of
samples required; sequencing coverage and depth; number of reads; platform used;
general laboratory costs, such as overheads, skill-mix (for example, salary costs for the
relevant grade of molecular scientist or bio-informatician); data analysis and
interpretation and capacity to deliver sufficient number of tests.
Budget impact of genomic testsThe ‘budget impact’ of introducing genomic tests into a healthcare system, in terms of
knowing the direct healthcare resources required to implement widespread testing and
then potentially changing treatment, is harder to quantify than the cost of performing
sequencing and, to date, no accurate published figures exist for the total budget impact
of introducing genetic or genomic tests into practice. Budget impact estimates are a
function of the expected current and future eligible population size, which is measured
using expected prevalence or incidence rates of the condition of interest, multiplied by
the acquisition cost (if tests are offered at cost) or price of the technology used to treat
and/or diagnose20. However, population size will change with a test that improves
diagnostic yield, and tests may reveal incidental findings that can stimulate additional
healthcare costs19. Moreover, it is important to distinguish between the genomic
technology used to perform the sequencing (such as a gene panel or whole-exome
sequencing), the suitability and use of the technology as a diagnostic (that is, its
capacity to provide an actionable test result) and the mechanism used to offer the
relevant patient population the diagnostic service (model of service delivery)21. Even at
a seemingly simplistic level, the actual cost of providing services to deliver genomic
tests is not well documented. For example, in England, the cost of providing genomic
medicine is embedded within the specialist commissioned services22. However, some
evidence is starting to emerge for some example tests. The budget impact of
introducing whole-exome sequencing to the front of the diagnostic pathway for patients
with a high probability of having a rare inherited disease who attend special genetic
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services in the UK, was estimated to lead to an increase pathway cost of £939 per
patient (or 52.5% increase) when compared with the usual testing strategy23.
Even if the population size is known with certainty, a fundamental challenge for
estimating the budget impact of genetic and genomic tests is the lack of a transparent
national tariff in some countries, and the use of charges, such as a flat rate per gene,
rather than the cost of delivering testing in terms of use of healthcare resources, in
others24.
Opportunity cost and economic evaluationAllocating resources for one intervention for a specified population means that those
same resources can no longer be used elsewhere for the care of other (known or
unknown) patient populations. Economic evaluation provides an explicit framework to
estimate the ‘opportunity cost’ of introducing a new intervention by comparing relevant
alternative uses of the healthcare budget in terms of their costs and consequences25.
Existing evaluation frameworks for genetic tests for rare inherited disorders26,27 are not
consistent with methods used in the evaluation of the economic impact of other
healthcare interventions. For example, medicines such as erlotinib for people with
locally advanced or metastatic non-small-cell lung cancer confirmed to be epidermal
growth factor receptor tyrosine kinase (EGFR-TK) mutation-positive, or diagnostic tests
such as testing EGFR–TK mutations in the tumors of adults with untreated NSCLC, are
assessed within the technology appraisal programme and the diagnostic assessment
programme, respectively, as part of national decision-making frameworks employed by
the National Institute for Health and Care Excellence (NICE) (Figure 1). NICE advocate
consideration of the opportunity cost for the healthcare budget and the use of
appropriate methods of economic evaluation to quantify the impact of a new technology
on healthcare costs and population outcomes28,29. In this context, two key tenets must
be remembered when using economic evaluation. First, the results should inform
resource allocation decisions at the population level rather than individual clinician–
patient decisions. Second, the results should be used as a source of information
towards evidence-based practice and cannot be seen as a substitute for careful
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interpretation and appraisal by regional and local decision-makers charged with making
resource allocation decisions within their own jurisdiction.
Different types of economic evaluation exist (Table 2)30-39. However, in terms of
application in practice, broadly two types of economic evaluation are used to quantify
the costs and consequences of a new intervention: cost-effectiveness analysis (CEA;
sometimes interchanged with the term ‘cost-utility analysis’ (CUA)) and cost-benefit
analysis (CBA). If economic evaluation is used to make ‘ought’ statements about what
decision-makers should do, it must have some underlying value judgements or
normative (‘what ought to be’) principles40,41,42. The normative underpinnings of CEA and
CBA are extra-welfarism and welfarism, respectively43, 44. To date the practical
application of extra-welfarism, such as in the context of NICE appraisal programmes,
supports the premise that decision-makers want to maximize health, which is measured
using quality-adjusted life-years (QALYs). Therefore, globally, CEAs have become the
type of economic evaluation recommended most frequently in guidelines to inform
resource allocation and/or reimbursement decisions45.
Trial-based versus model-based CEATwo approaches to produce the data to conduct a CEA exist: trial-based, whereby data
for decision-making are typically and almost exclusively collected from within a single
randomized controlled trial (RCT), or model-based, whereby data are collected from
multiple diverse sources and synthesized within an economic model.
Trial-based CEA
The timeliness for generating evidence from trial-based studies that collect cost and
consequences data at the individual patient-level can limit their use for decision-making
in practice46. Trial-based studies are also resource-intensive, are relevant only to the
trial protocol and have challenges when generalizing to other jurisdictions or patient
populations47. Using a trial-based CEA poses a specific practical challenge in the
context of genomic diagnostic tests. A genomic test must be embedded within a service
delivery model, which is likely to preclude the practical implementation of a
randomization process necessary for an RCT comparing diagnostic tests using next-
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generation sequencing technologies with current diagnostic strategies. This approach is
further complicated by the prohibitively large research budget needed to follow up
patient outcomes and resource use over a sufficient time horizon to produce relevant
evidence for decision-makers.
Model-based CEA
Using a model-based CEA allows all interventions and relevant comparators
(comparators are defined as ‘relevant’ by the stated decision problem), such as current
clinical practice, to be included in the analysis using methods such as, for example,
network meta-analysis47. Results can be extrapolated to capture the impact of an
intervention over the lifetime of a cohort of patients and also quantify the impact of
uncertainty48,49,50. Uncertainty in a model-based study can arise for a number of reasons:
a methodological uncertainty of how to value costs and benefits that will occur in the
future using the appropriate discount rate; a structural uncertainty about which
diagnostic and treatment pathways should be represented in the model structure; a
parameter uncertainty about the ‘true’ value of each input parameter used within the
model50. Sensitivity analysis is, therefore, a key component of any model-based
economic evaluation, and methods such as probabilistic sensitivity analysis to measure
the impact of parameter uncertainty are viewed as prerequisites to a robust analysis 51,52.
Combined, the ability to represent many comparators, include the impact of uncertainty
and extrapolate to a long time horizon, have all contributed to decision-analytic model
based CEA globally becoming the preferred approach to quantify the incremental costs
and consequences of a new intervention27,28,53,54.
A key challenge for model-based CEA for genetic and genomic tests, and more broadly
for any diagnostic test, is whether evidence exists that spans the three component parts
of the intervention simultaneously: (1) the technology; (2) the diagnostic component; (3)
the technology within the health service delivery framework21. NICE describe such
evidence as being ‘end-to-end’, that is, starting from performing the original test all the
way to patient outcomes and costs29. The only known example of a published end-to-
end study in the context of genetic testing used a pragmatic trial design 55 with integrated
CEA56 to assess the use of genotyping tests for single-gene mutations (versus no
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genotyping) when treating patients with azathioprine, and demonstrated the problem of
simply assuming that genetic information will mean clinicians appropriately modify their
treatment decisions. Where such studies are not available, assumptions must be made
as to how the component parts of the diagnostic technology interact, such as how
different diagnosis patterns from the new technology lead to changes in treatment
choices (and subsequent benefits and costs) when the set of two studies are connected
within an economic model.
Model-based CEA of genomic tests The key steps in a model-based CEA of a genetic or genomic test broadly mirror those
for diagnostic tests (Figure 2)25, 57-64. To ensure the resulting model-based CEA is
relevant for decision-making, the fundamental step is to explicitly define the decision
problem, underpinned by appropriate normative principles. That is, the following aspects
must be defined: the study perspective, the ‘time horizon’, the relevant study population,
the intervention to be evaluated and its relevant comparators.
The study perspective should be informed by the end user of the results of the CEA. If
the decision-maker (Figure 1) is based within a hospital then the relevant perspective
will be defined by the hospital budget. By contrast, a national level decision-maker
would need a study perspective that included all healthcare resource use, consistent
with informing how to spend the national healthcare budget. The study perspective is
most often constrained to the healthcare system because CEA are generally used to
inform the cost-effective use of a national budget for healthcare28,29. However, guidelines
used by some jurisdictions, such as The Netherlands, suggest broadening the viewpoint
to use a societal perspective, which would then mean patient costs are included in
addition to healthcare costs. The decision to use the societal perspective in The
Netherlands is based on a value judgement that costs outside of the healthcare sector
ought to be considered45.
The time horizon for a study should be sufficient to allow all relevant costs and
consequences to be incorporated which, in general, requires the lifetime time horizon
for the defined patient cohort to be used23. For example, a lifetime horizon would
capture the lifelong costs and consequences of using a test to target the use of a
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medicine to treat cancer. Importantly, defining the decision problem also involves having
a clear understanding of the relevant patient population that will accrue the benefits
(and harms) of the intervention. However, defining the relevant population can be
problematic for genomic tests. For example, when considering the added value of
whole-genome sequencing, the relevant patient population may extend beyond the
index patient first tested and include the benefits (and harms) to family members. In this
instance, the decision problem could be specified to take account of externalities that
capture the additional impact of genomic testing on the family (sometimes referred to as
‘spillover’ effects)65. Including spillover effects introduces another problem that
compounds how to conceptually define a ‘lifetime’ as it is no longer readily apparent
which lifetime for which patient/s is appropriate and the potential value of the
intervention may span generations13.
Defining the decision problem requires an explicit statement of the intervention and the
relevant comparators, which may include strategies beyond the test itself. For example,
companion diagnostics (used in ‘test-treat’ strategies) may be compared with other
medicines that are prescribed without a test. Different tests may also be considered as
relevant comparators. For example, a range of technologies can be used to assess
EGFR-TK mutations for patients with NSCLC who are treated with different targeted
medicines (such as the tyrosine kinase inhibitors gefitinib or erlotinib), depending on the
identified mutation66. Numerous diagnostic applications of genetic and genomic tests
also exist, as listed in the UK Genetic Testing Network database
[https://ukgtn.nhs.uk/find-a-test/], and are emerging for disorders for which there are
currently no available treatments, as is often the case with rare inherited conditions. In
these cases, and specifically for a genome-based diagnostic test, where incidental
findings are possible18, there is a need to be explicit when defining the intervention
under evaluation about how (and whether) such findings will be reported to the clinician
ordering the test and the affected patient. Furthermore, the approach used to interpret
incidental findings needs to be known and described. Multi-disciplinary committees
comprising groups of specialist geneticists, counsellors and molecular scientists are
becoming common practice as an approach to decide on the reported test result in
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some, but not all, clinical settings where next-generation sequencing technologies have
been used to produce a diagnostic test18.
Challenges of CEAs for genomic testsThe challenges of conducting model-based CEA for genetic or genomic tests can be
grouped into (a) whether a known treatment is available, such as in the case of a
companion diagnostic (linked test-treat strategies) or where subsequent preventative
strategies may be offered (for example, in the case of inherited cancers), and (b) if no
treatment options are currently known, such as in the case of diagnostic tests for some
rare inherited conditions (for example, in the case of hereditary ataxias, a group of rare,
neurodegenerative diseases)12.
Current economic evaluations of companion diagnostic tests tend to focus on the value
of the treatment or preventative strategy rather than quantifying the value of the
diagnostic test per se67. Some evaluation programmes have been developed specifically
to assess the added value of diagnostic tests; however, these currently still focus on
quantifying the value of the suggested treatment or preventative strategy in the CEA 29.
Box 1 shows an example of when a model-based CEA was used to assess the added
value of different companion diagnostic tests that were being used in clinical practice.
The challenges associated with conducting CEA of genomic-targeted ‘companion’
diagnostic tests have been well described67. They include, for example, the need for
clarity about whether the companion diagnostic test is designed to detect somatic or
germline mutations, and the careful framing of the study question and relevant clinical
pathways. It is also important to have analytical strategies in place to deal with the lack
of the necessary key requirements for a robust economic evaluation such as data on:
the clinical effectiveness; clinical utility; impact on health status and resource use; and
uptake of the test in practice.
By contrast, model-based CEA has not yet become routine to inform the introduction of
genetic or genomic tests for disorders with no available treatments, such as rare
inherited disorders that affect fewer than 1 in 2,000 individuals. This is likely to be the
result of the many notable challenges associated with using model-based CEA in this
context12,18,68-71. Although economic evaluations of genetic and genomic tests for rare
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diseases have been published (see Supplementary information S1), model-based CEA
has yet to be used to inform the actual introduction of these diagnostic tests into clinical
practice. A summary of the notable challenges, when conceptualizing, designing and
using the results of model-based CEA for genomic tests, specifically when no treatment
options are available, will now be described and categorized according to four types of
challenge: methodological, technical, practical and organizational (Table 3).
Methodological challenges
Relevant methods must be selected to address the defined decision problem while
remaining consistent with the agreed normative framework. Table 3 outlines some key
methodological challenges and three of these are now described. A review conducted in
2015 explored the impact of using CBA (underpinned by welfarism) or CEA
(underpinned by extra-welfarism) on technology adoption decisions72. This study found
that the two methods often led to different recommendations on which health care
technology to adopt. The authors concluded that health economists do not currently
provide the users of economic evaluations with sufficient guidance about which method
(CBA or CEA) should be preferred, which should be the focus of future work.
Some commentators have suggested extending economic evaluations beyond health
status to include ‘personal utility’ to value the benefits of genetic or genomic tests.
Personal utility has been used to refer to the potential value resulting from the genomic
information derived from a test in its own right (positive or negative) regardless of
whether treatment decisions and the health of the population are affected by the test
result73-76. To date, however, no measure exists for personal utility to be used in an
economic evaluation. One published study took an empirical approach to understand
whether health status alone was sufficient to capture the consequences of genetic tests
and services77. The study suggested that the consequences could be captured using a
concept related to the capability to make an informed decision (in the study referred to
as ‘empowerment’). This conclusion should also be applicable to the evaluation of
genomic tests. However, the study did not produce an instrument that could measure
‘capability to make an informed decision’. The authors suggest, appropriately, that
further empirical research is required to understand the impact on opportunity cost if
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non-health-related consequences are to be included as an additional factor to be
considered in CEA. Instruments have been developed that allow a broader
interpretation of extra-welfarism, such as the suite of measures of capability called
ICECAP (ICEpop CAPability measure)78,79, which capture the impact on well-being
(interpreted as ‘the ability to function’). NICE guidance currently recommends the
ICECAP measures as an option in the economic evaluation of social care
interventions78. However, using such measures of capability in a CEA does not currently
yield meaningful ways of presenting the results to decision-makers that are equivalent
to the presentation of QALY-based analyses, although work in this area is ongoing1,82.
Another methodological challenge being addressed is the potential need to move away
from estimating average costs and benefits for a population, and take account of the
relative cost-effectiveness at sub-group or the individual patient level when calculating
the relative cost-effectiveness of genomic-based interventions. Analytical approaches
calculating the value of heterogeneity (in, for example, patient or disease
characteristics) or value of preference heterogeneity use new theoretical frameworks
developed to assess the potential value of identifying cost-effective treatments for
defined sub-groups of patients, or individual patients given their preferences,
respectively. These approaches can in theory, therefore, compare the value of
‘individualized’ treatment decisions with the value of treatment decisions based on
traditional population-level cost-effectiveness analysis but, to date, there are no
published examples in the area of genomic tests.83,84
Technical challenges
Technical challenges refer to the need to account for the complexity of the decision
problem in the model-based CEA in terms of the characteristics of the relevant study
population, the number of relevant comparators and the potential diagnostic and, if
appropriate, subsequent treatment pathways. Extensive model conceptualization
techniques may be valuable to ensure that the structure of a de novo decision analytic
model is relevant to the prevailing decision problem (Figure 2)57. The most commonly
used models in published CEAs are state transition Markov models, which use a cohort-
based approach, sometimes combined with decision trees85,86. In theory, these types of
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models can cope with any number of relevant comparators but, in practice, they are
generally limited to comparing two or three interventions. One published example, a
CEA of stratified breast screening programmes87, used optimization methods to inform
how to reduce the number of comparators by selecting only the optimal programmes for
inclusion in the analysis. An optimal programme was defined by solving the knapsack
problem88. The knapsack problem defines the decision problem in terms of how to ‘pack’
a set of items (individual screens in the breast screening programme) of defined value
(total number of QALYs) into a knapsack (the screening programme) that can hold a
fixed total amount (total screens in a lifetime) in such a way that the value of items
‘packed’ is maximized. Using optimization methods has advantages over current
methods that consider a large number of scenario analyses within a CEA to represent
the many potential alternative screening programmes89,90. The inherent characteristics of
cohort state transition Markov models mean they may not be sufficient to capture the
impact of comparing multiple interventions together with the extensive individual patient
variation in terms of underlying genotype and presenting phenotype58. For example,
when comparing a new genomic test, such as a multiple (105) gene panel test for the
diagnosis of inherent retinal dystrophies, with the many potential existing diagnostic
strategies that use Sanger sequencing of individual genes and existing non-genetic
diagnostic strategies requires a model to include many potential comparisons and
account for individual patient variation91.
More relevant but potentially complex models, such as discrete event simulation (DES),
can be used to reflect the multitude of care pathways, heterogeneity in patient-level
characteristics and time-related (temporal) health events that occur in the progression of
disease or the diagnosis and treatment pathway 59,60,92. Such models allow the health
experiences of individual patients within the model to depend on their characteristics
(such as age or gender) or other relevant individual-level factors (such as genotype or
phenotype). Using DES also facilitates the impact of a key stated advantage of genomic
tests (offering patients the opportunity of an earlier diagnosis compared with existing
strategies) to be estimated and assess whether the expected positive impact is
realized22 and personal communication Niall Davison, The University of Manchester).
However, the use of DES in this context remains unpublished93.
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The majority of current applications of model-based CEA assume that all processes of
providing care and associated items of resource use are available with no limits,
regardless of the volume of the intervention required58. Effectively, there are no
‘constraints’ on the capacity of the system to provide healthcare services. The
assumption of infinite capacity may not be the case for a variety of reasons; there are
many potential types of constraints that are internal to a healthcare-system, such as
lack of equipment, data storage or healthcare workforce. Such ‘capacity constraints’ will
affect the ability of a healthcare system to reach its stated goal, such as the provision of
healthcare to realize patient benefits. In reality, all systems, including healthcare
systems, resemble networks of chains, in which each event is dependent on the
preceding event94. The discipline of mathematical optimization offers programming
techniques to select the best combination of inputs from a set of available alternatives,
to maximize output (for example, health gain) while taking into account capacity
constraints95,96. There is emerging interest in the combined use of decision analytic
modelling with mathematical optimization techniques to consider whether, and how,
model-based CEA of healthcare interventions can consider the impact of capacity
constraints when introducing a genetic or genomic diagnostic test into practice.
Practical challenges
Even if the methodological and technical challenges have been addressed, practical
challenges regarding research design may arise. A practical challenge associated with
the use of model-based CEA is the lack of the required robust data to populate the
model input parameters for outcomes, probabilities and resource use, in general, and
end-to-end evidence, specifically. New genomic sequencing technologies do not have a
defined development and regulatory pipeline that subsequently drives the available
evidence base for use in model-based economic evaluations. Key parameters, such as
the prevalence of the mutation and disease and the predictive value of the genomic test
are often not informed by a robust evidence base, meaning there is a need to rely on
information gained from further down the hierarchy of evidence97 to populate the model-
based CEA.
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A further practical challenge that compounds the lack of accurate resource use data is
the absence of a transparent national pricing tariff for the genomic test 24. One
systematic review indicated the impact of this challenge when it aimed to identify
economic evaluations of genomic sequencing technologies but found only five studies
purportedly conducting cost analyses of sequencing technologies with insufficiently
reported detail on the actual methods used to identify resource use and attach unit
costs98. There are emerging examples of using micro-costing methods to understand
the cost of tests, such as the approach taken to identify the cost of an anti-drug antibody
and drug level test to target the use of adalimumab for people with rheumatoid
arthritis99. The lack of a nationally agreed unit cost effectively means that an analyst is
trying to perform a CEA of a technology for which the acquisition cost is not known. In
practice, the analysis can still be done using an estimated unit cost for the test, but the
results of a CEA may then not be useful to decision-makers, who will not know whether
the results apply in their setting.
Organizational challenges
Although mentioned last, organizational challenges present the most substantial hurdle
for decision-makers who want to use the results of model-based CEA of genomic tests
to inform treatment strategies or for diagnosing rare inherited diseases. Organizational
challenges refer to the apparent disconnect between the organization of healthcare
systems and funding streams. These differences mean that a decision-maker must
understand whether and how the results of a model based CEA are relevant to their
own setting based on the scope of the analysis as defined by the decision problem.
Both within and across countries, healthcare systems have different service provision
and staffing models for different sectors (community, general practice, hospital,
specialist) and a means of allocating funding to these different sectors. The existence of
‘silo’ budgets, inextricably linked to the silo approach to the management of disease,
creates another specific challenge when using the results of CEAs. Methods of
economic evaluation assume that money can be freely moved around a healthcare
system so that patient benefits can be maximized by funding the most efficient service
as identified by the results of the analysis. In practice, this is not the case, and this
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organizational issue is particularly relevant for specialized services such as genomic
medicine for people with complex diseases, who require access to many types of
services across the healthcare system. The cost of a genomic-targeted diagnostic test
may, for example, fall to the clinician ordering the test from cardiology, as problems with
the patient’s heart were the presenting symptom, even if it emerges that the patient has
a syndromic condition. The results of a model-based CEA may show that achieving a
diagnosis can realize benefits across disparate and unconnected areas of the
healthcare service. However, a decision-maker may be in charge of one distinct budget,
such as for cardiology, and may not appreciate other benefits that are accrued.
On a global scale, genomics and associated Health Technology Assessment (HTA) is
the dominion of the prosperous and, perhaps unsurprisingly, focused within high-income
countries with relatively large healthcare budgets and formalized models of service
delivery. Some approaches have attempted to overcome global disparities in HTA. For
example, NICE International provides practical support and advice to any governments
and funding agencies about the process and methods of HTA in healthcare policy
decisions. The premise was that most countries could not afford to set up an equivalent
system to NICE owing to a lack of funds and expertise. In 2013, the Bill and Melinda
Gates Foundation funded a collaboration led by NICE International to develop the Gates
Reference Case, which outlined eleven principles to ensure economic evaluations
provide robust evidence for global health care decision-making100,101. NICE International
has now been replaced by the Global Health and Development Group
[https://www.imperial.ac.uk/global-health-innovation/ ] to continue to provide advice to
contribute to the effective, equitable use of resources and better global health.
Realizing the benefits of genomic tests?There is substantial but disparate evidence to support the economic impact of genetic
tests and precision medicine more broadly, but limited examples exist in the area of
genomics. Precision medicine includes approaches that use genetics but also other
influential factors, such as the environment and patient characteristics, to identify the
most suitable treatment or preventative strategy for an individual102. Some 45 systematic
reviews have assimilated economic evaluations in the general area of precision
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medicine and related areas, covering ‘test-treat’ strategies (of which, some used non-
genomic strategies) and genomic tests used for diagnosis, generally for single gene
disorders (see Supplementary information S1). Until 2016, there were no published
economic evaluations of new sequencing technologies, although examples of CEA,
some using diagnostic yield as the measure of effectiveness and some using QALYs,
are now emerging that evaluate the use of next-generation sequencing in the context of
gene panels, for exome sequencing and three hypothetical applications
(cardiomyopathy, colorectal cancer, healthy individuals) of returning incidental
findings23,103-108. This collective evidence base shows that the greatest focus is directed
towards evaluations in developed countries and that it is difficult to reach a collective
definitive view on whether healthcare systems can realize the potential benefits from
genomic tests. Collating this literature illustrates a substantial challenge for decision-
analysts and decision-makers who want to have an overall view of the supporting
evidence base on the economics of genomic tests: inconsistency in terminology. There
is no agreed or standardized use of terms within published papers, or electronic
databases, relevant to genomic-targeted diagnostic tests such as: precision medicine;
stratified medicine; personalized medicine; individualized medicine; targeted medicine;
pharmacogenetics; pharmacogenomics109. The early stages of any health technology
assessment or the conceptualization and development (Figure 2) of a decision analytic
model is to collate all available economic evidence, in the form of published economic
evaluations, to understand the current knowledge base on a topic and to identify
whether any existing decision analytic models can be used as the basis for a proposed
economic evaluation28,28. Examining the disparate nature of the results from 45
systematic reviews (see Supplementary information S1) reveals that it is virtually
impossible to identify all relevant evidence from one review given the inconsistency in
nomenclature. If proponents of the use of genomic diagnostic tests want to realize their
benefits in practice, an important first step will be to agree a set of consistent terms and
definitions. However, reaching agreement about the appropriate terms and definitions
across the broad community of stakeholders, which include clinicians, patients,
industrial partners who manufacture medicines and tests, and national assessment
17
agencies, will require a concerted effort by internationally recognized experts using
appropriate consensus methods.
ConclusionsDriven by the development of new sequencing technologies and national research
agendas, there will be an increase in the number of available genomic tests in the
developed world. Evaluation frameworks for genetic tests exist but it is reasonable to
suggest that these may not be sufficient given the potential new demand for genomics
within existing models of service delivery and beyond into mainstream medicine. Given
the finite nature of healthcare budgets, decision-makers need to understand the
opportunity cost of introducing new genomic diagnostic tests. Model-based CEA are a
useful tool embedded within health technology assessment programmes to provide
explicit information on the incremental costs and consequences of new interventions. To
inform decisions about efficient allocation of healthcare resources, model-based CEAs
must be supported and populated by robust studies, which ideally span the three
component parts of a genomic test: (1) the technology; (2) the diagnostic component;
(3) the model of service delivery to provide the diagnostic test and, if appropriate,
subsequent treatment options. The area of genetics has an emerging economic
evidence base. National research agencies have appropriately started funding the
development of new technologies to inform new genomic tests that may replace existing
genetic tests. It is now time to direct funding to support the empirical research needed to
develop the use of decision-analytic model based CEAs of genomic tests, while being
cognizant of the known methodological, technical, practical and organizational
challenges, to maximize the potential benefits to patient populations.
Box 1 | Example of using model-based CEA to inform clinical practice. This Box
shows an illustrative example when model-based cost effectiveness analysis was used
to inform the use of a test in practice.
Context National Institute for Health and Care Excellence Diagnostics Guidance 9 [https://www.nice.org.uk/guidance/dg9] published August 2013
18
Decision problem: What is the relative effectiveness and cost-effectiveness of epidermal growth factor receptor tyrosine kinase (EGFR–TK) mutation testing in patients with locally advanced or metastatic non-small cell lung cancer?
Technology: Ten ways of testing for EGFR mutations
Method of analysis: Model-based cost effectiveness analysis using a decision tree combined with a state transition Markov model
Costs: 2,012 (£)
Outcomes: Quality-adjusted life years
Decision rule: The NICE threshold of £20,000 to £30,000 per QALY gained
Recommendation: Five tests were recommended as options for detecting EGFR-TK mutations, when used in accredited laboratories participating in an external quality assurance scheme:
therascreen EGFR RGQ PCR Kit (CE-marked, Qiagen)
cobas EGFR Mutation Test (CE-marked, Roche Molecular Systems)
Sanger sequencing of samples with more than 30% tumour cells and therascreen EGFR RGQ PCR Kit for samples with lower tumour cell contents
Sanger sequencing of samples with more than 30% tumour cells and cobas EGFR Mutation Test for samples with lower tumour cell contents
Sanger sequencing followed by fragment length analysis and polymerase chain reaction (PCR) of negative samples.
There was insufficient evidence for the Committee to make recommendations on the following five methods:
high-resolution melt analysis
pyrosequencing combined with fragment length analysis
single-strand conformation polymorphism analysis
19
next-generation sequencing
therascreen EGFR Pyro Kit (CE-marked, Qiagen)
Figure 1 | Examples of decision-makers. This figures shows some illustrative
examples of the types of decision-makers who work within different levels of the system
for providing healthcare. This figure only shows some examples and is not a
comprehensive description of all the decision-makers within the healthcare system for
the UK.
Figure 2 | Key elements in the design and conduct of decision analytic model-based CEA. This flowchart should be read from the top to the bottom and each box
explains the relevant steps in the conduct of a model-based CEA with references
illustrating the key considerations for each step.
20
Table 1 | Example types of genetic and genomic tests Example test Type of
test11
Technology
Relevant population
Example source
Tests to inform treatment or for diagnosing disorders with known treatments
HLA-B 5701
genotype,
abacavir
hypersensitivity
, blood
genotyping
test for
known
disease or
treatment-
associated
variant
Qualitative
allele-
specific
Real-time
PCR
Individuals
with an
increased risk
of
hypersensitivit
y reactions to
the
antiretroviral
agent
abacavir,
based on the
presence of
the HLA-
B*57:01 allele
Mayo Clinic
[https://www.mayomedicallaboratories.com/
test-catalog/Overview/89346]
EGFR mutation
testing in
NSCLC
genotyping
test for
known
disease or
treatment-
associated
variant
Sanger
sequencing
Adult patients
with previously
untreated,
locally
advanced or
metastatic
NSCLC
Manchester Centre for Genomic Medicine
[http://www.mangen.co.uk/lab-services/view-
details.php?tag=55]
Testing to
identify at least
one G551D
mutation in the
CFTR gene to
indicate
treatment with
ivacaftor in
patients with
cystic fibrosis
genotyping
test for
known
disease or
treatment-
associated
variant
PCR-based
multiplex
assay
Test used to in
people with
CF to identify
those with a
specific
genetic
mutation
CFTR Elucigene Eu2v1
(http://www.elucigene.com/)
ivacaftor for the management of cystic fibrosis.
Formulary Submission Dossier.
Cambridge, MA:
Vertex Pharmaceuticals Inc. (2012)
Diagnostic test for disorders with no known treatment
Targeted
mutation
analysis for
Huntington
genotyping
test for
known
disease or
Sanger
sequencing
Patient and
family
members with
rare inherited
UK Genetic Testing Network
[https://ukgtn.nhs.uk/find-a-test/search-by-
disorder-gene/huntington-disease-203/]
21
disease treatment-
associated
variant
disorders
Retinal
degeneration
disorders 105
gene panel
disease-
targeted
panel tests
for genes
known to be
associated
with the
disorder or
treatment
Next-
generation
sequencing
gene panel
Patient and
family
members with
rare inherited
disorders
UK Genetic Testing Network
[https://ukgtn.nhs.uk/find-a-test/search-by-
disorder-gene/retinal-degeneration-disorders-
105-gene-panel-568/]
Epilepsy
disorders 110
gene exome
panel
exome
sequencing
test, which
interrogates
nearly all
protein-
coding
regions
Whole-
exome
sequencing
Patient and
family
members with
rare inherited
disorders
UK Genetic Testing Network
[https://ukgtn.nhs.uk/find-a-test/search-by-
disorder-gene/epilepsy-disorders-110-gene-
exome-panel-879/]
Whole genome
sequencing for
the diagnosis
of rare
inherited
disorders
(research
context only)
genome
sequencing
test, which
interrogates
nearly the
entire genetic
code
including
non-coding
regions
Whole-
genome
sequencing
Patient and
selected family
members with
rare inherited
disorders
(research
context only)
100,000 Genomes project
[https://www.genomicsengland.co.uk/the-
100000-genomes-project/]
There is no accepted definition of how a genetic test differs to a genomic test. For the purpose of this
paper we use the following definitions: genetic tests aim to identify germline or somatic mutations (or
pathogenic variants) that underlie high-risk, single-gene disorders, whereas genomic tests search for
germline or somatic mutations (or pathogenic or modifying variants) in coding and non-coding DNA
across the whole genome. Genomic tests are often applied to heterogeneous conditions, polygenic
conditions or conditions with unknown genetic causes, including conditions where epigenetic factors may
play a role in disease risk. CFTR, cystic fibrosis transmembrane conductance regulator; EGFR, epidermal
growth factor receptor; HLA, human leukocyte antigen; NSCLC, non-small-cell lung cancer.
22
Table 2 | Types of economic evaluationEconomic evaluation (acronym)a
Normative underpinning
Consequences Presentation of results
Example decision rule
Application to inform decision-making
Generally recommended for evaluation of healthcare programmes?
Cost-
effectivenes
s analysis
(CEA)b
Extra-
welfarism
Clinical
effectiveness
eg.
Life years
gained (LYG)
calculated from
the proportion of
treatment
responders
measured using
a clinical
outcome
Calculate
incremental
costs and
incremental
benefits
and, if
appropriate
ICERc
ICER <
pre-
defined
threshold
eg.
£20,000
per LYG
Quantifies impact on
health using a
measure of eg. life-
years gained within
for example NICE
Technology
Appraisals or
Diagnostics
Assessment
programmes
[https://www.nice.org
.uk/]
Yes as data
on
effectiveness
often
available
from trials
Cost-utility
analysis
(CUA) b
Extra-
welfarism
Quality adjusted
life years
(QALYs)
calculated by
combining
remaining years
of life with a
quality
adjustment to
reflect health
status from
using a generic
measure (eg.
https://euroqol.o
rg/) and
published
preference
weights30
Incremental
costs and
incremental
benefits
and, if
appropriate
ICERc
ICER <
pre-
defined
threshold
eg.
£20,000
per QALY
Incremen
tal net
benefit
(INB)31
Quantifies the impact
on health using eg.
QALYs within for
example NICE
Technology
Appraisals or
Diagnostics
Assessment
programmes
Yes as can
be used to
compare
across
programmes
23
Cost-benefit
analysis
(CBA)
Welfarism Monetary
benefits
estimated from
a contingent
valuation
method eg.
Willingness to
pay survey32
Total costs
and
monetary
benefits for
each
intervention
Benefit
(£) / cost
(£) for
interventi
on A >
Benefit
(£) / cost
(£) for
interventi
on B
Benefit
(£) > cost
(£)
Quantifies impact of
non-healthcare
public programmes33
and suggested as an
option within NICE
public health
programme to allow
consequences other
than health status to
be measured
[https://www.nice.org
.uk/]
No because
of
methodologic
al and ethical
challenges34
Cost-
minimizatio
n analysis
(CMA)
None None as
benefits
assumed to be
equal
Total costs
for each
intervention
Cost for
interventi
on A <
cost for
interventi
on B
Pragmatic approach
advocated when
differences in benefit
between
technologies can be
assumed to be zero
or when the
differences in cost so
large that any
difference in benefits
unlikely to change
the decision
No because
of inherent
uncertainty
when
estimates of
benefits and
costs are
combined
comparing
costs alone
can produce
biased
results35,36
Cost-
consequenc
es analysis
(CCA)
None A suite of
consequences
from pre-
defined
outcomes that
can include
measures of:
clinical
effectiveness;
health status;
capability; non-
health or
process
measures (eg.
Total costs
for each
intervention
and total
consequenc
es (for each
included
outcome)
A suite of
ICERsc for
each
consequenc
e, if
None Pragmatic approach
that leaves decision-
makers to decide on
a case-by-case basis
how trade-offs
should be made
within NICE public
health or Medical
Technology
Evaluation
Programmes
[https://www.nice.org
.uk/]
No because
opportunity
cost not
consistently
considered
across
programme
s38,39
24
quality of a
genetic
counselling
episode)36
relevant,
can be
presented
a same general approach to quantify the costs of each relevant technology by identifying
the relevant types of resources. Relevance in this context is defined by the chosen
study perspective and time-horizon, and using unit costs for each resource to calculate
the total cost.b CEA is, in some situations, such as within the NICE programmes, used instead of the
perhaps more accurate term CUA, which is what NICE is actually advocating as they
suggest quality adjusted life years (QALYs). This paper adopts this approach.c Incremental Cost Effectiveness Ratio (ICER) = difference in costs (eg. Cost
intervention A – Cost intervention B) / difference in effectiveness (eg. Life-years gained
for intervention A – life-years gained for intervention B)
25
Table 3 | Typology of example challenges for CEA of genomic testsType of challenge Example of challenge Description of challenge
Methodological Selecting the
appropriate evaluative
framework
Is the standard extra-welfarist view and use of CEA appropriate or
should the distinct theoretical approach reflecting the welfarist view
and use of CBA be adopted to allow consequences other than
health gain, such as the value of diagnostic information from the
genomic-targeted diagnostic test, to be valued?
Relevant study
perspective
Is the standard recommendation to focus on the use of healthcare
services appropriate when the genomic-targeted diagnostic test
may provide information that affects the use of other services such
as education or employment?
Relevant time horizon Is a lifetime sufficient when the impact of a genomic-targeted
diagnostic test may extend to infinite time horizons that are not
limited by the lifespan of one individual?
Defining the relevant
study population
Is the standard definition of a patient (the person receiving the
technology) appropriate when there could be ‘spillover’ effects to
family members (currently alive or to be born) as a result of
information from a genomic-targeted diagnostic test?
Valuing consequences Is identifying and measuring the impact on health status alone
sufficient to capture the (good and bad) consequences of a
genomic-targeted diagnostic test?
Technical Variation in the
individual characteristics
of the relevant study
population
The use of cohort state transition Markov models, sometimes
combined with decision trees, cannot easily capture the impact of
individual patient variation within a population with different
genotypes and phenotypes
Number of diagnostic,
and if appropriate,
subsequent treatment
pathways
The use of cohort state transition Markov models, sometimes
combined with decision trees, cannot easily account for multiple
comparators often needed when evaluating a new genomic-targeted
diagnostic test
Capture impact of
reduced time to
diagnosis
The use of cohort state transition Markov models, sometimes
combined with decision trees, cannot account for the impact of
reduced time to achieve a diagnosis, which is often a proposed
benefit of a genomic-targeted diagnostic test
Capture impact of
capacity constraints
Decision analytic model-based CEA currently assume limitless
capacity within healthcare systems, which is often not a reasonable
assumption when introducing a genomic-targeted diagnostic test to
populations for whom a diagnosis was not previously available
Practical Availability of data There is often a lack of data available to populate decision analytic
model-based CEA
National tariff of test No national tariff for genomic-targeted tests exist
26
cost
Organizational Complex healthcare
systems
Decision analytic model-based CEA assume that money saved, and
benefits accrued, are transferable but this is often challenging in
complex healthcare systems that comprise: an overarching funding
mechanism (public, private, insurance); service and staffing model
for providing care for different sectors (community, general practice,
hospital, specialist); and a means of allocating funding to these
different sectors
Generalizability of
results
Decision analytic model-based CEA are only relevant to the defined
decision problem and decision-makers who want to use the results
must decide if the focus of the analysis is relevant to their own
jurisdiction
Expensive nature of
health technology
assessment
Decision analytic model-based CEA conducted within national
health technology assessment processes requires significant
funding and expertise that is not available to all, which may
contribute to the inequity in access to new genomic-targeted
diagnostic tests across the world
27
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context of personalized or precision medicine
36
AcknowledgementsWe would like to acknowledge the help of Hunter Moore with the systematic review of
published economic evaluations of precision medicine in the Supplementary Appendix.
We would also like to acknowledge the expert advice on the applications of genomic
tests from Graeme Black, George Burghel, Georgina Hall and Bill Newman.
K.P. has a research programme supported by grants awarded from: National Institute
for Health Research; Medical Research Council (MRC); Engineering and Physical
Sciences Research Council (EPSRC); Lupus UK; The Swedish Foundation for
Humanities and Social Sciences. S. W. is funded by a Wellcome Trust PhD
Studentship. A. J. T. is funded by a grant awarded to The University of Manchester for
the Manchester Molecular Pathology and Innovation Centre (MMPathIC) by the Medical
Research Council (MRC) and Engineering and Physical Sciences Research Council
(EPSRC). S. P. G. is supported by two grants awarded to The University of Manchester
for MASTERPLANS, funded by the Medical Research Council (MRC) [grant number
MR/M01665X/1] Stratified Medicine Programme and by Lupus UK. The views
expressed in this article are those of the authors and not the funding bodies.
37
Further informationDuke Center for Applied Genomics and Precision Medicine
https://precisionmedicine.duke.edu
EuroQoL https://euroqol.org/
Genetics Home Reference https://ghr.nlm.nih.gov/primer/testing/costresults
Manchester Centre for Genomic Medicine http://www.mangen.co.uk/lab-
services/MolecularPathology.php
Genohub. https://genohub.com/
The Mayo Clinic http://www.mayomedicallaboratories.com
NHS England specialised services
https://www.england.nhs.uk/commissioning/spec-services/
NIH National Human Genome Research Institute
https://www.genome.gov/sequencingcosts/
National Institute for Health and Care Excellence https://www.nice.org.uk/
UK Genetic Testing Network – find a test https://ukgtn.nhs.uk/find-a-test/
38
GLOSSARY
Budget impact: The total budget impact of a new technology in terms of the cost
falling on the budget-holder. Budget impact should be calculated by considering: the
perspective of the specific health care decision maker; the size and characteristics of
the population; the current and new treatment mix; the effectiveness and safety of the
new and current treatments; the resource use and costs for the treatments and
symptoms as would apply to the population of interest.
Companion diagnostic (also sometimes referred to as the test in a ‘test-treat’
strategy): A diagnostic test (typically an in-vitro diagnostic) co-developed alongside a
pharmaceutical and stated explicitly within the product label as it is essential for the safe
and effective use of the corresponding medicine.
Complementary diagnostic (also sometimes referred to as the test in a ‘test-treat’
strategy): A diagnostic test (typically an in-vitro diagnostic) developed to guide the use
of a pharmaceutical. The test may be mentioned in the medicinal product label but no
specific test is named
Conceptual model: An abstract representation of a phenomenon of interest, often
illustrated diagrammatically, to assist in determining the final structure of a de novo
decision analytic model in a model-based economic evaluation.
Cost-benefit analysis (CBA): A type of economic evaluation that compares the
relative costs and consequences of different courses of action in which the
consequences are measured using an approach that captures the impact in terms of
money, such as willingness-to-pay.
Cost-consequences analysis (CCA): A type of economic evaluation that compares
the relative costs and consequences of different courses of action in which the
consequences are measured using different outcomes and presented separately.
39
Cost-effectiveness analysis (CEA): A type of economic evaluation that compares the
relative costs and consequences of different courses of action in which the
consequences are measured using outcomes that capture the impact on clinical
effectiveness.
Cost-effectiveness threshold: The additional cost that must be imposed on the budget
for health care to displace one QALY elsewhere within the health care system.
Interventions which increase cost but with an incremental cost per QALY below the
threshold are typically viewed as being cost-effective. NICE uses a threshold range
thought to be between £20,000 and £30,000 per QALY gained.
Cost-minimization analysis (CMA): A type of economic evaluation that compares the
only the relative costs of different courses of action as it is assumed that the
consequences are equal.
Cost-utility analysis (CUA): A type of economic evaluation that compares the
relative costs and consequences of different courses of action in which the
consequences are measured using quality adjusted life years (QALYs). Often used
synonymously with cost-effectiveness analysis.
Decision analytic model: A series of mathematical relationships that represent the
progression of a patient’s diagnosis or disease and the impact of a health technology on
diagnosis and/or disease progression. The output of a decision analytic model can be
expressed in terms of the expected outcomes of interest for each alternative comparator
strategy.
Decision problem: An explicit statement of the resource allocation decision under
consideration.
40
Decision tree: A decision analytic modelling technique that simulates a cohort of
patients following a pre-defined pathway with associated probabilities, costs and
outcomes. Decision trees do not typically incorporate a time-component.
Discrete event simulation: A decision analytic modelling technique that simulates the
histories of individual patients over time, characterized by the specific events that they
may experience.
Economic evaluation: Defined by Drummond et al (2015) as “…the comparative
analysis of alternative courses of action in terms of both their costs and consequences ”.
An alternative typically refers to a health technology and the consequence is typically
expressed in terms of health benefits.
End-to-end evidence: Evidence that captures all elements of a test-and-treatment
strategy, by following a patient (i) from their observed test result, (ii) to a specific
treatment decision, and then to their final (health and resource) outcomes
Externalities: Occur when the effect of a production or individual consumption of goods
and services imposes costs or benefits on others. Externalities can be positive (benefit)
or negative (harm) and are sometimes called ‘spill over’ effects.
Extra-welfarism: A set of normative principles which guides the conduct, design and
interpretation of an economic evaluation. Extra-welfarism is typically taken to underpin
the use of cost-effectiveness analysis as the method of economic evaluation and the
Quality-Adjusted Life-Year as the metric of benefit/outcome.
Health technology: A technology that is used to create an intervention used in the
delivery of health care. For example, a medicine, diagnostic test, or medical device.
Incremental cost: The difference in cost between two alternative interventions.
41
Incremental cost-effectiveness ratio: The ratio of incremental costs to incremental
health benefits.
Incremental QALY: The difference in QALYs between two alternative interventions.
Incremental net benefit: incremental net benefit can be measured in monetary units
(incremental net monetary benefit) or units of health gain (incremental net health
benefit). If measured in monetary units (eg, dollars or euros), the incremental net benefit
(INB) is the monetary difference between expected net benefit of the new intervention
and the expected net benefit of the relevant comparator.
Normative: what ‘ought to be’; a statement underpinned by value judgments, rather
than a positive statement of ‘what is’.
National Institute for Health and Care Excellence (NICE): The decision-making
authority responsible for making recommendations regarding the allocation of
population health care resources in England.
Opportunity cost: The benefit forgone from the next-best use of a specific resource.
The opportunity cost of resource allocation decisions for health care can be expressed
in the health benefits forgone.
Perspective: The scope of the costs that should be included in an economic evaluation.
The perspective is typically defined by the budget constraint of the decision-maker.
Examples include a health care system perspective and a societal perspective. Also
helps to determine the relevant outcome chosen for analysis.
Probabilistic sensitivity analysis: A form of sensitivity analysis used where
uncertainty is propagated through the: (i) characterization of input parameters as
probability distributions and (ii) sampling of values for parameters using Monte Carlo
simulation.
42
Quality-adjusted life year (QALY): A generic outcome measure of health benefit,
calculated by multiplying each year of life by a weight that represents its health-related
quality of life. Weights are calculated according to the reference points of one (full
health) and zero (death); states worse than death are possible.
Reference case: A pre-specified preferred criteria for conducting an economic
evaluation. A reference case is typically an expression of a decision-maker’s value
judgement.
State transition Markov models: A type of decision analytic model that conceptualizes
a problem by defining relevant health states through which a cohort of patients transition
over time.
Time horizon: The scope of the costs and consequences that should be included in an
economic evaluation, from the present until a defined point in the future. The time
horizon for a study should be sufficient to allow all relevant costs and consequences to
be incorporated which, in general, requires a ‘lifetime time’ horizon to be used. The
lifetime is taken to be that of the last dying patient within the analysis cohort.
Uncertainty: Understanding the impact of uncertainty is a key component of any
economic evaluation. There are different types of uncertainty such as parameter,
methodological and decision. The impact of parameter and methodological uncertainty
can be captured using sensitivity analysis, such as probabilistic sensitivity analysis or
scenario analysis, respectively. Decision uncertainty is the probability that an incorrect
decision is made, in the context of resource allocation decisions for health care.
Welfarism: A set of normative principles which guides the conduct, design and
interpretation of an economic evaluation. Welfarism places individual utilities at the
heart of the evaluative space, is typically taken to be consistent with the use of cost-
benefit analysis and the use of ‘willingness to pay’ as the metric of benefit/outcome.
43
Author biographies
Katherine Payne holds a personal Chair in health economics at The University of
Manchester and leads a research group (the Manchester Centre for Health Economics
Precision Medicine Economics Team: MCHE-PMET) that applies and develops
methods of economic evaluation and valuation to understand the economic impact of
precision medicine and genomic technologies.
https://www.research.manchester.ac.uk/portal/Katherine.Payne.html
Sean P. Gavan is a research fellow in health economics at the Manchester Centre for
Health Economics, The University of Manchester. Sean has an interest in the economic
evaluation of precision medicine and the generation of evidence to support the uptake
of diagnostic health technologies within healthcare systems.
https://www.research.manchester.ac.uk/portal/sean.gavan.html
Stuart J. Wright is a Wellcome Trust funded PhD student at the Manchester Centre for
Health Economics, The University of Manchester. Stuart is interested in evaluating the
economic evidence for the introduction of precision medicine into clinical practice with a
particular focus on the impact of capacity constraints within healthcare systems.
https://www.research.manchester.ac.uk/portal/stuart.wright-2.html
Alexander J. Thompson is a research fellow in health economics and at the
Manchester Centre for Health Economics, The University of Manchester. Alex is
interested in developing value-frameworks and evaluating the economic evidence for
the introduction of precision medicine into clinical practice.
https://www.research.manchester.ac.uk/portal/alexander.thompson.html
44
Key points Informed by public and privately funded programmes, advances in next-
generation sequencing technologies have yielded genomic-targeted diagnostic tests,
based on either multi-gene panels, whole-exome sequencing or whole-genome
sequencing, are starting to replace some genetic tests in clinical practice.
Cost-effectiveness analysis of new genomic technologies, that explicitly
quantifies the costs and consequences of the new technology compared with other
potential uses of a healthcare budget, can provide decision-makers with information to
guide resource allocation decisions.
This review first summarizes what is known about, and describes the challenges
when estimating the magnitude of, the budget impact of genetic and genomic tests and
services. Existing literature has focussed on the downward-trend in the cost of
sequencing. We highlight the importance of estimating the opportunity cost (by
considering both the cost and consequences) of introducing new genomic tests.
The review then describes the potential role of decision analytic model-based
cost-effectiveness analysis, when compared with trial-based cost-effectiveness
analysis, and then outlines the key steps to consider in the evaluation of genetic and
genomic tests.
To inform decisions about the efficient allocation of healthcare resources, model-
based CEAs must be supported and populated by robust studies, which ideally span the
three component parts of a genomic test: the technology; the diagnostic component; the
model of service delivery to provide the diagnostic test and, if appropriate, subsequent
treatment options.
An emerging economic evidence base for genetic tests has been identified. It is
now time to direct funding to support the empirical research needed to develop the use
of decision-analytic model based CEAs of genomic tests, while being cognizant of the
known methodological, technical, practical and organizational challenges, to maximize
the potential benefits to patient populations.
45