TY - JOUR
T1 - Cost-effectiveness frameworks for comparing genome and exome sequencing versus conventional diagnostic pathways
T2 - A scoping review and recommended methods
AU - Ferket, Bart S.
AU - Baldwin, Zach
AU - Murali, Priyanka
AU - Pai, Akila
AU - Mittendorf, Kathleen F.
AU - Russell, Heidi V.
AU - Chen, Flavia
AU - Lynch, Frances L.
AU - Lich, Kristen Hassmiller
AU - Hindorff, Lucia A.
AU - Savich, Renate
AU - Slavotinek, Anne
AU - Smith, Hadley Stevens
AU - Gelb, Bruce D.
AU - Veenstra, David L.
N1 - Funding Information:
The Clinical Sequencing Evidence-Generating Research consortium is funded by the National Human Genome Research Institute ( NHGRI ) with cofunding from the National Institute on Minority Health and Health Disparities and the National Cancer Institute , supported by U01 HG006485, U01 HG007301, U01 HG007292, U01 HG006487, U01 HG009610, U01 HG009599, and U24HG007307. The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health . K.F.M. was funded by a diversity supplement to U01HG007292 ( NHGRI ). H.S.S. is supported by K99HG011491 ( NHGRI ). D.L.V. is supported by 1R01HG009694-01 ( NHGRI ). The authors thank the members of the Clinical Utility, Health Economics and Policy (CUHEP) working group of the Clinical Sequencing Evidence-Generating Research consortium for their input on this work. The authors gratefully acknowledge Renata Gallagher ( University of California San Francisco), for providing clinical expertise and insights for the scenarios of screening of healthy populations.
Funding Information:
K.F.M. has received institutional support from GE Healthcare. D.L.V has received institutional support from Foundation Medicine. All other authors declare no conflicts of interest.
Funding Information:
For further research, some innovative modeling techniques and analysis types may be tested as potential solutions to the challenges for conducting economic evaluation of GS/ES strategies. For example, when cohorts linked by their pedigree should be modeled, agent-based modeling approaches may overcome some of the limitations of using the conventional state-transition models. In addition, value of information analysis can be used to determine and prioritize research agendas for future empirical studies, better informing model parameters that are considered essential for making more definitive conclusions about cost-effectiveness. Finally, the conduct of equity-informative CEA has been suggested to inform decision-makers about trade-offs between cost-effectiveness and health disparities.43 Most genomic research databases used to verify variants are based on participants of European ancestry and do not capture the global diversity in human genomic variation. As such, determining cost-effectiveness of GS/ES technologies across all genomic backgrounds or ancestral populations remains a challenge, potentially leading to decisions that are less favorable for underserved and/or disadvantaged populations. In this context, the CSER consortium, now in its second funding cycle,8 is expected to provide useful information by providing data about the integration of GS and ES in clinical care across over 6100 participants including diverse and medically underserved individuals in a variety of health care settings and disease states.The Clinical Sequencing Evidence-Generating Research consortium is funded by the National Human Genome Research Institute (NHGRI) with co-funding from the National Institute on Minority Health and Health Disparities and the National Cancer Institute, supported by U01 HG006485, U01 HG007301, U01 HG007292, U01 HG006487, U01 HG009610, U01 HG009599, and U24HG007307. The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health. K.F.M. was funded by a diversity supplement to U01HG007292 (NHGRI). H.S.S. is supported by K99HG011491 (NHGRI). D.L.V. is supported by 1R01HG009694-01 (NHGRI). The authors thank the members of the Clinical Utility, Health Economics and Policy (CUHEP) working group of the Clinical Sequencing Evidence-Generating Research consortium for their input on this work. The authors gratefully acknowledge Renata Gallagher (University of California San Francisco), for providing clinical expertise and insights for the scenarios of screening of healthy populations. Conceptualization: B.S.F. Z.B. P.M. A.P. K.F.M. H.V.R. F.C. F.L.L. K.H.L. L.A.H. R.S. A.S. H.S.S. B.D.G. D.L.V.; Data Curation: B.S.F. Z.B.; Methodology: B.S.F. Z.B. P.M. A.P. K.F.M. H.V.R. F.C. F.L.L. K.H.L. L.A.H. R.S. A.S. H.S.S. B.D.G. D.L.V.; Writing-original draft: B.S.F.; Writing-review and editing: B.S.F. Z.B. P.M. A.P. K.F.M. H.V.R. F.C. F.L.L. K.H.L. L.A.H. R.S. A.S. H.S.S. B.D.G. D.L.V. This study did not include human subjects or animal research.
Publisher Copyright:
© 2022 American College of Medical Genetics and Genomics
PY - 2022/10
Y1 - 2022/10
N2 - Purpose: Methodological challenges have limited economic evaluations of genome sequencing (GS) and exome sequencing (ES). Our objective was to develop conceptual frameworks for model-based cost-effectiveness analyses (CEAs) of diagnostic GS/ES. Methods: We conducted a scoping review of economic analyses to develop and iterate with experts a set of conceptual CEA frameworks for GS/ES for prenatal testing, early diagnosis in pediatrics, diagnosis of delayed-onset disorders in pediatrics, genetic testing in cancer, screening of newborns, and general population screening. Results: Reflecting on 57 studies meeting inclusion criteria, we recommend the following considerations for each clinical scenario. For prenatal testing, performing comparative analyses of costs of ES strategies and postpartum care, as well as genetic diagnoses and pregnancy outcomes. For early diagnosis in pediatrics, modeling quality-adjusted life years (QALYs) and costs over ≥20 years for rapid turnaround GS/ES. For hereditary cancer syndrome testing, modeling cumulative costs and QALYs for the individual tested and first/second/third-degree relatives. For tumor profiling, not restricting to treatment uptake or response and including QALYs and costs of downstream outcomes. For screening, modeling lifetime costs and QALYs and considering consequences of low penetrance and GS/ES reanalysis. Conclusion: Our frameworks can guide the design of model-based CEAs and ultimately foster robust evidence for the economic value of GS/ES.
AB - Purpose: Methodological challenges have limited economic evaluations of genome sequencing (GS) and exome sequencing (ES). Our objective was to develop conceptual frameworks for model-based cost-effectiveness analyses (CEAs) of diagnostic GS/ES. Methods: We conducted a scoping review of economic analyses to develop and iterate with experts a set of conceptual CEA frameworks for GS/ES for prenatal testing, early diagnosis in pediatrics, diagnosis of delayed-onset disorders in pediatrics, genetic testing in cancer, screening of newborns, and general population screening. Results: Reflecting on 57 studies meeting inclusion criteria, we recommend the following considerations for each clinical scenario. For prenatal testing, performing comparative analyses of costs of ES strategies and postpartum care, as well as genetic diagnoses and pregnancy outcomes. For early diagnosis in pediatrics, modeling quality-adjusted life years (QALYs) and costs over ≥20 years for rapid turnaround GS/ES. For hereditary cancer syndrome testing, modeling cumulative costs and QALYs for the individual tested and first/second/third-degree relatives. For tumor profiling, not restricting to treatment uptake or response and including QALYs and costs of downstream outcomes. For screening, modeling lifetime costs and QALYs and considering consequences of low penetrance and GS/ES reanalysis. Conclusion: Our frameworks can guide the design of model-based CEAs and ultimately foster robust evidence for the economic value of GS/ES.
KW - Cost-effectiveness analysis
KW - Decision modeling
KW - Economic evaluation
KW - Exome sequencing
KW - Genome sequencing
UR - http://www.scopus.com/inward/record.url?scp=85134755262&partnerID=8YFLogxK
U2 - 10.1016/j.gim.2022.06.004
DO - 10.1016/j.gim.2022.06.004
M3 - Article
C2 - 35833928
AN - SCOPUS:85134755262
SN - 1098-3600
VL - 24
SP - 2014
EP - 2027
JO - Genetics in Medicine
JF - Genetics in Medicine
IS - 10
ER -