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 (katherine.payne@manchester.ac.uk)

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

6

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.

7

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

9

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

References

1. Donnai, D. & Elles, R. Integrated regional genetic services: current and future provision.

BMJ  322, 1048 (2001) 

2. Medical Genetics.

https://www.england.nhs.uk/commissioning/spec-services/npc-crg/group-e/e01/. Last

accessed: 4 July 2018

3. Godard, B., Kääriäinen, H., Kristoffersson, U., Tranebjaerg, L., Coviello, D., Aymé, S.

Provision of genetic services in Europe: current practices and issues. European Journal of

Human Genetics 11 (Suppl 2), S13–S48 (2003)

4. MRC. Developing and evaluating complex interventions: new guidance. MRC: London

2008

5. Payne, K., McAllister, M., Davies, L.M. Valuing the economic benefits of complex

interventions: when maximising health status is not sufficient. Health Economics 22(3),

258-71 (2013)

6. Payne K, Nicholls S, McAllister M, MacLeod R, Donnai D, Davies LM. Outcome

measurement in clinical genetics services: a systematic review of validated measures.

Value in Health, 11(3), 497-508 (2008)

7. McAllister M, Davies LM, Payne K, Nicholls S, Donnai D, Macleod R. The emotional

effects of genetic diseases: implications for clinical genetics. American Journal of

Medical Genetics, 143A,2651–2661 (2007)

8. Payne K, Nicholls S, McAllister M, MacLeod R, Middleton-Price H, Ellis I, Donnai D,

Davies LM. Outcome measures for clinical genetics services: a comparison of genetics

healthcare professionals and patients’ views. Health Policy, 84, 112-122 (2007)

9. McAllister M, Payne K, Nicholls S, Macleod R, Donnai D, Davies LM. Improving

service evaluation in clinical genetics: identifying effects of genetic diseases on

individuals and families. Journal of Genetic Counseling, 16(1), 71-83 (2007)

10. Drummond MF, Sculpher MJ, Torrance GW, O'Brien BJ & Stoddart GL. Methods for the

economic evaluation of health care programmes. Oxford University Press Inc: New York

(2005)

11. Sculpher MJ, Claxton K, Drummond M, McCabe C. Whither trial-based economic

evaluation for health care decision making? Health Econ, 15(7), 677-87 (2006)

13

12. Payne K, Gavan S, Wright SJ, Thompson A. Cost-effectiveness analyses of genetics and

genomics-targeted diagnostic technologies. Nature Reviews Genetics 2018

doi:10.1038/nrg.2017.108

13. National Institute for Health and Care Excellence. Guide to the methods of technology

appraisal (2013)

14. https://www.ispor.org/PEguidelines/index.asp. Last accessed 15 July 2018

15. Spackman E, Hinde S, Bojke L, Payne K, Sculpher M. Using cost-effectiveness analysis

to quantify the value of genomic-based diagnostic tests: recommendations for practice

and research. Genetic Testing & Molecular Biomarkers (2017)

https://doi.org/10.1089/gtmb.2017.0105

16. Husereau D, Jacobs P, Manns B, Hoomans T, Marshall D, Tamblyn R. Economic

Evaluation of Complex Health System Interventions: A Discussion Paper. Institute of

Health Economics (2014)

17. Payne K & Thompson A. Economic evaluations of complex interventions in Complex

Interventions Handbook. (2015)

18. The Green Book: appraisal and evaluation in central government see

https://www.gov.uk/government/publications/the-green-book-appraisal-and-evaluation-

in-central-governent

19. Coast, J. Is economic evaluation in touch with society's health values? Br Med J 329,

1233-1236 (2004)

20. Culyer, A.J. The normative economics of health care finance and provision. Oxford

Review of Economic Policy 5(1), 34-58 (1989)

21. Culyer, A.J., Evans, R.G. Mark Pauly on welfare economics: Normative rabbits from

positive hats. J Health Econ 15, 243–51 (1996)

22. Hurley, J.E. An Overview of the Normative Economics of the Health Sector in Handbook

of health economics 1(part A), 55-118 (2000)

23. Brouwer, W.B., Culyer, A.J., van Exel, N.J., Rutten, F.F. Welfarism vs. extra-welfarism.

J Health Econ 27(2), 325-38 (2008)

24. Payne K & Thompson AJ. Economics of pharmacogenomics: rethinking beyond

QALYs? Pharmacogenomics 11(3), 187-95 (2013)

14

25. Brazier, J., McCabe, C., Edlin, R. Health economics and cost consequences analysis: a

step back in time. BMJ Rapid Response (2005)

26. Claxton, K.P., Sculpher, M.J., Ades, T. Cost consequences: implicit, opaque and anti

scientific. BMJ Rapid Response (2005)

27. Smith, R. D. Construction of the contingent valuation market in health care: a critical

assessment. Health Econ 12, 609–628 (2003)

28. Smith. R.D. & Sach, T.H. Contingent valuation: what needs to be done? Health

Economics Policy and Law 5, 91-111 (2010)

29. EQ-5D. Available from: http://www.euroqol.org/. last accessed 15 July 2018

30. Dolan, P., et al., A social tariff for EuroQol: results from a UK general population survey.

York: University of York Discussion Paper (138) 1995

31. Balmana J, Sanz J, Bonfill X et al. Genetic counseling program in familial breast cancer:

Analysis of its effectiveness, cost and cost-effectiveness ratio. International Journal

Cancer, 112(4), 647-652 (2004)

32. Brain K, Norman P, Gray J, Rogers C, Mansel R, Harper P. A randomized trial of

specialist genetic assessment: psychological impact on women at different levels of

familial breast cancer risk. Br J Cancer 86:233–238 (2002)

33. Beech R, Rona RJ, Mandalia S. The resource implications and service outcomes of

genetic services in the context of DNA technology. Health Policy, 26(3), 171-190 (1994)

34. Davison N, Payne K, Eden M, McAllister M, Roberts SA, Ingram S, Black GCM,

Hall G. Exploring the feasibility of delivering standardised genomic care using

ophthalmology as an example. Genetics in Medicine, 19(9), 1032-1039 (2017)

35. Herdman M, Gudex C, Lloyd A et al. Development and preliminary testing of the new

five-level version of Eq5D (EQ5D-5l). Qual Life Res, 20(10), 1727-1736

36. Wang C, Gonzalez R, Merajver SD. Assessment of genetic testing and related

counselling services: current research and future directions. Soc Sci Med, 58,1427–42

(2004)

37. Pithara C. Identifying outcomes of clinical genetic services: qualitative evidence and

methodological considerations. J Genet Couns, 23, 229–238 (2014)

15

38. Sarangi S, Bennert K, Howell , Clarke A, Harper P, Gray J. (Mis)alignments in

counseling for Huntington’s Disease predictive testing: clients’ responses to reflective

frames. J Genet Couns ,14, 29–42 (2005)

39. Mendes AF, Santos TA, Sousa L. Experiencing genetic counselling for hereditary

cancers: the client’s perspective. Eur J Cancer Care, 20, 204–211 (2011)

40. Grosse SD, Khoury MJ. What is the clinical utility of genetic testing? Genetics in

Medicine, 8(7), 448-450 (2006)

41. Foster MW, Mulvihill JJ, Sharp RR. Evaluating the utility of personal genomic

information. Genetics in Medicine, 11(8), 570-574 (2009)

42. Mills RA, Haga SB, Ginsburg GS. Genetic testing: clinical and personal utility. Virtual

Mentor, 14(8), 604-609 (2012)

43. Eden M, Payne K, Combs RM, Hall G, McAllister M, Black G. Valuing the benefits of

genetic testing for Retinitis Pigmentosa: a pilot application of the contingent valuation

method. British Journal of Ophthalmology, 97(8),1051-6 (2013)

44. Buchanan J & Wordsworth S. Welfarism versus extra-welfarism: can the choice of

economic evaluation approach impact on the adoption decisions recommended by

economic evaluation studies? Pharmacoeconomics, 33, 571 - 579 (2015)

45. Coast J, Smith R, Lorgelly P. 2008a. Welfarism, extra-welfarism and capability: the

spread of ideas in health economics. Social Science and Medicine, 67, 1190-1198

46. Sen A. Capability and well-being. In the Quality of Life, Nussbaum MC (ed). Clarendon

Press: Oxford (1993)

47. http://www.birmingham.ac.uk/research/activity/mds/projects/HaPS/HE/ICECAP/

index.aspx. Last accessed 15 July 2018

48. Eden M, Daker-White G, Black G, Payne K. Developing a taxonomy of non-health value

for genomic-based diagnostic tests. Value in Health, 20(5), A6 (2017)

49. Franklin M, Payne K, Elliott RA. Quantifying the relationship between capability and

health in older people. Medical Decision Making, 38(1),79-94 (2018)

16