From Future Science Group via Jisc Publications RouterHistory: received 2021-02-21, accepted 2021-04-14, online 2021-05-11, pub-electronic 2021-05-13, pub-print 2021-07Publication status: Publishe

Eden, Martin

From Future Science Group via Jisc Publications RouterHistory: received 2021-02-21, pub-print 2021-04, accepted 2021-04-14, online 2021-05-11, pub-electronic 2021-05-13Publication status: Publishe

ChesterRep

EditorialFor reprint orders, please contact: reprints@futuremedicine.comDeveloping new frameworks to valuegenomic information: accounting forcomplexityMartin Eden*,11Manchester Centre for Health Economics, The University of Manchester, Manchester, UK*Author for correspondence: martin.eden@manchester.ac.ukFirst draft submitted: 21 February 2021; Accepted for publication: 14 April 2021; Published online:11 May 2021Keywords: economics • genomics • methodologyCurrent frameworks & their limitationsDecisions have to be made about how to allocate the finite resources available to healthcare systems. Frameworksexist that can aid decisions about whether a new healthcare intervention should be approved for use in a healthcaresystem. These frameworks are typically underpinned by a formal approach to economic evaluation called cost–effectiveness analysis (CEA) and are primarily driven by the tenet that population health should be maximized.In a CEA, two or more interventions are assessed in terms of their costs and outcomes to determine the relativecost–effectiveness of an intervention compared with its alternatives. To enable comparisons between different typesof healthcare interventions in a CEA, it is preferable to use a standard, common outcome measure: the quality-adjusted life-year (QALY). Mortality effects and quality of life attributable to ‘health’ are captured by the QALYwith ‘health’ being narrowly defined by the scope of recommended survey tools such as the EQ-5D [1].Generally speaking, the frameworks have proved useful for resource allocation decision making and have beensuccessfully adopted in a number of jurisdictions across the world. There are, however, specific situations in whichQALY-based frameworks, as currently applied, are inadequate. Notably, limitations have been identified wheregenomic information forms part of a complex intervention, for example, in precision medicine initiatives [2,3].Interventions which utilize genomic information are complex, in that multiple elements combine to producemultifaceted outcomes. Importantly, these outcomes can extend beyond the confines of ‘health’ as conceptualizedin the QALY.The role & complexity of genomic informationGenomic information can be used for a variety of purposes. In a precision medicine context it can guide treatmentdecisions. The extent to which these decisions are driven by the providers and/or users of healthcare is dependenton the specific application of precision medicine.In other applications, genomic information can confer a diagnosis or provide a likely prognosis of diseaseprogression. The distinction between genetic and genomic information is brought into sharp relief in this respect;over the last decade, new diagnostic technologies have provided a means of identifying the etiological basis of rarediseases for which older genetic tests were ill-equipped [4]. Juxtaposing the ‘precision’ label, it should be noted thata residual uncertainty often accompanies diagnostic and prognostic information because phenotypic traits can varyin how they present in individuals who share a common genomic diagnosis.Uncertainty can also be seen in relation to the provision of individualized risk information. Genomic-baseddata can be used to estimate a person’s likelihood of experiencing a health condition over a given period of time.The inherent uncertainty in this likelihood is further compounded by concerns about how risk information isinterpreted by individuals.Taking a broader system-wide perspective, complexity in genomic medicine also manifests in bureaucracy,infrastructures and dynamic relationships between multiple stakeholders. Jurisdictions differ in their approaches tothe approval and delivery of healthcare. Within healthcare systems, variations in working practices are evident [5] andPer. Med. (Epub ahead of print) ISSN 1741-054110.2217/pme-2021-0023 C© 2021 Martin EdenEditorial Edenhave resulted in apparent inequities in, for example, the provision of genomic testing [6]. Technological innovation,ostensibly beneficial, has the potential to re-inforce existent negative elements or introduce new system-wideproblems.Addressing challengesLimitations with decision-making frameworks ascribable to these complexities could be addressed by refining ap-proaches to the economic evaluation of interventions reliant on genomic information. A White Paper from 2016 [2]highlighted challenges with a particular focus on issues to consider, relating to the evaluation of ‘complementarydiagnostic’ technology. The report describes how existing frameworks fail to account for the true comprehen-sive value of diagnostic technologies and, consequently, do not provide adequate incentives – from an industryperspective – to foster technological innovation [2].The focus, in CEA, on a narrowly defined conception of health maximization means that current frameworksmay not adequately account for the true value of diagnostic technology as perceived by individuals [2,7]. For example,genomic test information per se can confer value irrespective of any associated treatments; it has been demonstratedthat people attach value to genomic-based diagnostic information in relation to untreatable familial conditions [8].Qualitative and quantitative approaches have been combined to define and measure how people value genomic-based diagnostic information. It is considered useful in aiding informed decision-making and is also valued in itsability to reduce uncertainty about a family’s condition. Importantly, for inherited conditions, individuals valuegenomic test information because it may have implications for others as well as themselves [9]. These types of valueattributable to genomic information, using a QALY-based framework, would not inform approval decisions.As well as offering gains in perceived value, genomic information has the potential to impact upon individu-als’ behavior. For example, clinicians from an increasing range of disciplines are being confronted with new waysof working as more and more treatment-guiding genomic tests become available. In dedicated clinics, geneticcounselors use genomic information to tailor their intervention aimed at helping people come to terms with adiagnosis, understand its implications and to help them make plans for the future. In principle, effects of suchbehavior change could be predicted, measured and incorporated into economic evaluation but, again, the extentto which the true value of genomic information is captured rests on approaches to outcomes measurement.Algorithms to predict risk of preventable disease can be augmented with genomic information. The expectationhere is that, armed with knowledge, individuals might alter their behavior to maximize health outcomes. However,current evidence does not support this expectation; the provision of genetic risk information in isolation does notappear to effectively change behavior [10]. More work is needed to understand and, if appropriate, quantify theimpact on individuals’ behavior of genomic information as a complex intervention component.Genomic information is used for different purposes in different settings, can be used as a complement to otherinterventional components and can produce varied outcomes. More broadly, resource allocation decision-makingin this area is further complicated by various factors including the involvement of multiple stakeholders withdistinct agendas. As alluded to earlier, one key factor of complexity relates to the failure of current frameworksto account for the often synergistic effects of interventions attributable to genomic information. This has servedto stifle technological innovation and – alongside the inadequate measurement of value from the individual’sperspective – has undoubtedly hindered the development and timely approval of novel diagnostics. A frameworkwhich accounted for the true value of genomic information and which was fairly set up to reward innovation wouldaccelerate the diffusion of benefits deriving from novel technologies across the healthcare system.New approachesProgress has been made. It has followed from the identification of certain areas of concern [2,11,12]. But the keyissue, running through each of these individual concerns, has yet to be addressed. Complexity is also manifestthroughout the overarching systems in which decisions are made and where healthcare is provided. As indicated inthe White Paper [2], it is possible to address each area of concern by measuring additional elements of value but thereal challenge is to combine evidence in order to construct a new fit-for-purpose framework which is cognizant ofsystem-wide complexity.The characterization of each area of concern as a distinct problem to fix before a new framework can be developed,might serve to compound the current unacceptable situation. It does not promote a holistic approach which wouldallow the targeting of multiple issues simultaneously. Such an approach could address areas of complexity which arecurrently unknown or difficult to target. For example, dynamic relationships between industry-based stakeholders,10.2217/pme-2021-0023 Per. Med. (Epub ahead of print) future science groupValuing complex genomic interventions Editorialapproval bodies and individuals would represent a useful focus of enquiry. A recognition that the implementationof new interventions within healthcare systems might apparently produce only slight changes in the short termwill be required. An understanding of the value and impact of genomic information from multiple perspectivescould also play a role. Complex systems approaches have been used in the field of public health research to considerthe interconnectedness of system elements and how interventions have the potential to effect change across thesesystems over extended periods of time [13].The specific implications of systems theory approaches for economic evaluation have been explicitly consid-ered [14]. Primarily, there is a need to recognize when traditional economic evaluation methods will suffice (i.e., inthe case of complex interventions applied in a noncomplex system) and when characteristics of a complex systemdemand that alternative approaches be enlisted [14]. The scope for interventions to redefine and shift values heldwithin a system is a characteristic to consider in the development of frameworks seeking to incentivize innovationin diagnostic technologies used to synergistic effect.Using a complex systems theory approach to framework development will require multidisciplinary working,employing mixed-methods techniques. Where progress has been made to date, it has arisen with methodologicalinput from a range of disciplines including health economics and health psychology. Advances in genomic tech-nologies have been mirrored by methodological innovation in the evaluation of precision medicine initiatives andmuch has been learned. To capitalize on these lessons, a fundamental shift in approach is now required so that newframeworks can be developed. In doing so, the true value of genomic information will be realized within healthcaresystems both despite and because of inherent complexity.Financial & competing interests disclosureM Eden has completed a National Institute for Health Research (NIHR) Doctoral Research Fellowship. The views expressed are thoseof the author and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. The author has noother relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflictwith the subject matter or materials discussed in the manuscript apart from those disclosed.No writing assistance was utilized in the production of this manuscript.Open accessThis work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 Unported License. To view a copy of this license,visit http://creativecommons.org/licenses/by-nc-nd/4.0/References1. The EuroQol Group. EuroQol (2019). http://www.euroqol.org/home.html2. Garrison L, Mestre-Ferrandiz J, Zamora B. The value of knowing and knowing the value: improving the health technology assessment ofcomplementary diagnostics. Office for Health Economics, London, UK(2016). http://www.ohe.org/sites/default/files/WP EpemedOHE f inal.pdf3. Lakdawalla DN, Doshi JA, Garrison LP, Phelps CE, Basu A, Danzon PM. Defining elements of value in health care – a healtheconomics approach: an ISPOR Special Task Force report [3]. Value Health 21(2), 131–139 (2018).4. Newman WG, Black GC. Delivery of a clinical genomics service. Genes (Basel) 5(4), 1001–1017 (2014).5. Eden M, Payne K, Jones C et al. Identifying variation in models of care for the genomic-based diagnosis of inherited retinal dystrophiesin the United Kingdom. Eye (Lond.) 30, 966–971 (2016).6. Harrison M, Birch S, Eden M et al. Variation in healthcare services for specialist genetic testing and implications for planning geneticservices: the example of inherited retinal dystrophy in the English NHS. J. Community Genet. 6(2), 157–165 (2015).7. Payne K, McAllister M, Davies LM. Valuing the economic benefits of complex interventions: when maximising health is not sufficient.Health Econ. 22(3), 258–271 (2013).8. Eden M, Payne K, Combs RM, Hall G, McAllister M, Black GCM. Valuing the benefits of genetic testing for retinitis pigmentosa: apilot application of the contingent valuation method. Br. J. Ophthalmol. 97(8), 1051–1056 (2013).9. Eden M, Daker-White G, Black G, Payne K. Developing a taxonomy of non-health value for genomic-based diagnostic information.Value Health 20(5), A6 (2017).10. Hollands GJ, French DP, Griffin SJ et al. The impact of communicating genetic risks of disease on risk-reducing health behaviour:systematic review with meta-analysis. BMJ 352, i1102 (2016).11. Buchanan J, Wordsworth S, Schuh A. Issues surrounding the health economic evaluation of genomic technologies. Pharmacogenomics14(15), 1833–1847 (2013).future science group 10.2217/pme-2021-0023Editorial Eden12. Payne K, Gavan SP, Wright SJ, Thompson AJ. Cost–effectiveness analyses of genetic and genomic diagnostic tests. Nat. Rev. Genet.19(4), 235–246 (2018).13. Rutter H, Savona N, Glonti K et al. The need for a complex systems model of evidence for public health. Lancet 390(10112),2602–2604 (2017).14. Shiell A, Hawe P, Gold L. Complex interventions or complex systems? Implications for health economic evaluation. BMJ 336(7656),1281–1283 (2008).10.2217/pme-2021-0023 Per. Med. (Epub ahead of print) future science group

Developing new frameworks to value genomic information: accounting for complexity

EditorialFor reprint orders, please contact: reprints@futuremedicine.comDeveloping new frameworks to valuegenomic information: accounting forcomplexityMartin Eden*,11Manchester Centre for Health Economics, The University of Manchester, Manchester, UK*Author for correspondence: martin.eden@manchester.ac.ukFirst draft submitted: 21 February 2021; Accepted for publication: 14 April 2021; Published online:11 May 2021Keywords: economics • genomics • methodologyCurrent frameworks & their limitationsDecisions have to be made about how to allocate the finite resources available to healthcare systems. Frameworksexist that can aid decisions about whether a new healthcare intervention should be approved for use in a healthcaresystem. These frameworks are typically underpinned by a formal approach to economic evaluation called cost–effectiveness analysis (CEA) and are primarily driven by the tenet that population health should be maximized.In a CEA, two or more interventions are assessed in terms of their costs and outcomes to determine the relativecost–effectiveness of an intervention compared with its alternatives. To enable comparisons between different typesof healthcare interventions in a CEA, it is preferable to use a standard, common outcome measure: the quality-adjusted life-year (QALY). Mortality effects and quality of life attributable to ‘health’ are captured by the QALYwith ‘health’ being narrowly defined by the scope of recommended survey tools such as the EQ-5D [1].Generally speaking, the frameworks have proved useful for resource allocation decision making and have beensuccessfully adopted in a number of jurisdictions across the world. There are, however, specific situations in whichQALY-based frameworks, as currently applied, are inadequate. Notably, limitations have been identified wheregenomic information forms part of a complex intervention, for example, in precision medicine initiatives [2,3].Interventions which utilize genomic information are complex, in that multiple elements combine to producemultifaceted outcomes. Importantly, these outcomes can extend beyond the confines of ‘health’ as conceptualizedin the QALY.The role & complexity of genomic informationGenomic information can be used for a variety of purposes. In a precision medicine context it can guide treatmentdecisions. The extent to which these decisions are driven by the providers and/or users of healthcare is dependenton the specific application of precision medicine.In other applications, genomic information can confer a diagnosis or provide a likely prognosis of diseaseprogression. The distinction between genetic and genomic information is brought into sharp relief in this respect;over the last decade, new diagnostic technologies have provided a means of identifying the etiological basis of rarediseases for which older genetic tests were ill-equipped [4]. Juxtaposing the ‘precision’ label, it should be noted thata residual uncertainty often accompanies diagnostic and prognostic information because phenotypic traits can varyin how they present in individuals who share a common genomic diagnosis.Uncertainty can also be seen in relation to the provision of individualized risk information. Genomic-baseddata can be used to estimate a person’s likelihood of experiencing a health condition over a given period of time.The inherent uncertainty in this likelihood is further compounded by concerns about how risk information isinterpreted by individuals.Taking a broader system-wide perspective, complexity in genomic medicine also manifests in bureaucracy,infrastructures and dynamic relationships between multiple stakeholders. Jurisdictions differ in their approaches tothe approval and delivery of healthcare. Within healthcare systems, variations in working practices are evident [5] andPer. Med. (2021) 18(4), 329–332 ISSN 1741-0541 32910.2217/pme-2021-0023 C© 2021 Martin EdenEditorial Edenhave resulted in apparent inequities in, for example, the provision of genomic testing [6]. Technological innovation,ostensibly beneficial, has the potential to re-inforce existent negative elements or introduce new system-wideproblems.Addressing challengesLimitations with decision-making frameworks ascribable to these complexities could be addressed by refining ap-proaches to the economic evaluation of interventions reliant on genomic information. A White Paper from 2016 [2]highlighted challenges with a particular focus on issues to consider, relating to the evaluation of ‘complementarydiagnostic’ technology. The report describes how existing frameworks fail to account for the true comprehen-sive value of diagnostic technologies and, consequently, do not provide adequate incentives – from an industryperspective – to foster technological innovation [2].The focus, in CEA, on a narrowly defined conception of health maximization means that current frameworksmay not adequately account for the true value of diagnostic technology as perceived by individuals [2,7]. For example,genomic test information per se can confer value irrespective of any associated treatments; it has been demonstratedthat people attach value to genomic-based diagnostic information in relation to untreatable familial conditions [8].Qualitative and quantitative approaches have been combined to define and measure how people value genomic-based diagnostic information. It is considered useful in aiding informed decision-making and is also valued in itsability to reduce uncertainty about a family’s condition. Importantly, for inherited conditions, individuals valuegenomic test information because it may have implications for others as well as themselves [9]. These types of valueattributable to genomic information, using a QALY-based framework, would not inform approval decisions.As well as offering gains in perceived value, genomic information has the potential to impact upon individu-als’ behavior. For example, clinicians from an increasing range of disciplines are being confronted with new waysof working as more and more treatment-guiding genomic tests become available. In dedicated clinics, geneticcounselors use genomic information to tailor their intervention aimed at helping people come to terms with adiagnosis, understand its implications and to help them make plans for the future. In principle, effects of suchbehavior change could be predicted, measured and incorporated into economic evaluation but, again, the extentto which the true value of genomic information is captured rests on approaches to outcomes measurement.Algorithms to predict risk of preventable disease can be augmented with genomic information. The expectationhere is that, armed with knowledge, individuals might alter their behavior to maximize health outcomes. However,current evidence does not support this expectation; the provision of genetic risk information in isolation does notappear to effectively change behavior [10]. More work is needed to understand and, if appropriate, quantify theimpact on individuals’ behavior of genomic information as a complex intervention component.Genomic information is used for different purposes in different settings, can be used as a complement to otherinterventional components and can produce varied outcomes. More broadly, resource allocation decision-makingin this area is further complicated by various factors including the involvement of multiple stakeholders withdistinct agendas. As alluded to earlier, one key factor of complexity relates to the failure of current frameworksto account for the often synergistic effects of interventions attributable to genomic information. This has servedto stifle technological innovation and – alongside the inadequate measurement of value from the individual’sperspective – has undoubtedly hindered the development and timely approval of novel diagnostics. A frameworkwhich accounted for the true value of genomic information and which was fairly set up to reward innovation wouldaccelerate the diffusion of benefits deriving from novel technologies across the healthcare system.New approachesProgress has been made. It has followed from the identification of certain areas of concern [2,11,12]. But the keyissue, running through each of these individual concerns, has yet to be addressed. Complexity is also manifestthroughout the overarching systems in which decisions are made and where healthcare is provided. As indicated inthe White Paper [2], it is possible to address each area of concern by measuring additional elements of value but thereal challenge is to combine evidence in order to construct a new fit-for-purpose framework which is cognizant ofsystem-wide complexity.The characterization of each area of concern as a distinct problem to fix before a new framework can be developed,might serve to compound the current unacceptable situation. It does not promote a holistic approach which wouldallow the targeting of multiple issues simultaneously. Such an approach could address areas of complexity which arecurrently unknown or difficult to target. For example, dynamic relationships between industry-based stakeholders,330 Per. Med. (2021) 18(4) future science groupValuing complex genomic interventions Editorialapproval bodies and individuals would represent a useful focus of enquiry. A recognition that the implementationof new interventions within healthcare systems might apparently produce only slight changes in the short termwill be required. An understanding of the value and impact of genomic information from multiple perspectivescould also play a role. Complex systems approaches have been used in the field of public health research to considerthe interconnectedness of system elements and how interventions have the potential to effect change across thesesystems over extended periods of time [13].The specific implications of systems theory approaches for economic evaluation have been explicitly consid-ered [14]. Primarily, there is a need to recognize when traditional economic evaluation methods will suffice (i.e., inthe case of complex interventions applied in a noncomplex system) and when characteristics of a complex systemdemand that alternative approaches be enlisted [14]. The scope for interventions to redefine and shift values heldwithin a system is a characteristic to consider in the development of frameworks seeking to incentivize innovationin diagnostic technologies used to synergistic effect.Using a complex systems theory approach to framework development will require multidisciplinary working,employing mixed-methods techniques. Where progress has been made to date, it has arisen with methodologicalinput from a range of disciplines including health economics and health psychology. Advances in genomic tech-nologies have been mirrored by methodological innovation in the evaluation of precision medicine initiatives andmuch has been learned. To capitalize on these lessons, a fundamental shift in approach is now required so that newframeworks can be developed. In doing so, the true value of genomic information will be realized within healthcaresystems both despite and because of inherent complexity.Financial & competing interests disclosureM Eden has completed a National Institute for Health Research (NIHR) Doctoral Research Fellowship. The views expressed are thoseof the author and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. The author has noother relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflictwith the subject matter or materials discussed in the manuscript apart from those disclosed.No writing assistance was utilized in the production of this manuscript.Open accessThis work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 Unported License. To view a copy of this license,visit http://creativecommons.org/licenses/by-nc-nd/4.0/References1. The EuroQol Group. EuroQol (2019). http://www.euroqol.org/home.html2. Garrison L, Mestre-Ferrandiz J, Zamora B. The value of knowing and knowing the value: improving the health technology assessment ofcomplementary diagnostics. Office for Health Economics, London, UK (2016).http://www.ohe.org/sites/default/files/WP EpemedOHE f inal.pdf3. Lakdawalla DN, Doshi JA, Garrison LP, Phelps CE, Basu A, Danzon PM. Defining elements of value in health care – a healtheconomics approach: an ISPOR Special Task Force report [3]. Value Health 21(2), 131–139 (2018).4. Newman WG, Black GC. Delivery of a clinical genomics service. Genes (Basel) 5(4), 1001–1017 (2014).5. Eden M, Payne K, Jones C et al. Identifying variation in models of care for the genomic-based diagnosis of inherited retinal dystrophiesin the United Kingdom. Eye (Lond.) 30, 966–971 (2016).6. Harrison M, Birch S, Eden M et al. Variation in healthcare services for specialist genetic testing and implications for planning geneticservices: the example of inherited retinal dystrophy in the English NHS. J. Community Genet. 6(2), 157–165 (2015).7. Payne K, McAllister M, Davies LM. Valuing the economic benefits of complex interventions: when maximising health is not sufficient.Health Econ. 22(3), 258–271 (2013).8. Eden M, Payne K, Combs RM, Hall G, McAllister M, Black GCM. Valuing the benefits of genetic testing for retinitis pigmentosa: apilot application of the contingent valuation method. Br. J. Ophthalmol. 97(8), 1051–1056 (2013).9. Eden M, Daker-White G, Black G, Payne K. Developing a taxonomy of non-health value for genomic-based diagnostic information.Value Health 20(5), A6 (2017).10. Hollands GJ, French DP, Griffin SJ et al. The impact of communicating genetic risks of disease on risk-reducing health behaviour:systematic review with meta-analysis. BMJ 352, i1102 (2016).11. Buchanan J, Wordsworth S, Schuh A. Issues surrounding the health economic evaluation of genomic technologies. Pharmacogenomics14(15), 1833–1847 (2013).future science group www.futuremedicine.com 331Editorial Eden12. Payne K, Gavan SP, Wright SJ, Thompson AJ. Cost–effectiveness analyses of genetic and genomic diagnostic tests. Nat. Rev. Genet.19(4), 235–246 (2018).13. Rutter H, Savona N, Glonti K et al. The need for a complex systems model of evidence for public health. Lancet 390(10112),2602–2604 (2017).14. Shiell A, Hawe P, Gold L. Complex interventions or complex systems? Implications for health economic evaluation. BMJ 336(7656),1281–1283 (2008).332 Per. Med. (2021) 18(4) future science group

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Developing new frameworks to value genomic information: accounting for complexity

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