This study aims to determine research participants’ preferences for receiving genetic risk information when participating in a scientific study that uses genome sequencing.
A discrete choice experiment questionnaire was sent to 650 research participants (response rate 60.5%). Four attributes were selected for the questionnaire: type of disease, disease penetrance probability, preventive opportunity, and effectiveness of the preventive measure. Panel mixed logit models were used to determine attribute level estimates and the heterogeneity in preferences. Relative importance of the attribute and the predicted uptake for different information scenarios were calculated from the estimates. In addition, this study estimates predicted uptake for receiving genetic risk information in different scenarios.
All characteristics influenced research participants’ willingness to receive genetic risk information. The most important characteristic was the effectiveness of the preventive opportunity. Predicted uptake ranged between 28% and 98% depending on what preventive opportunities and levels of effectiveness were presented.
Information about an effective preventive measure was most important for participants. They valued that attribute twice as much as the other attributes. Therefore, when there is an effective preventive measure, risk communication can be less concerned with the magnitude of the probability of developing disease.
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Meulenkamp TM, Gevers SK, Bovenberg JA, Koppelman GH, van Hylckama Vlieg A, Smets EM. Communication of biobanks’ research results: what do (potential) participants want? Am J Med Genet A. 2010;152A:2482–2492.
Jelsig AM, Qvist N, Brusgaard K, Ousager LB. Research participants in NGS studies want to know about incidental findings. Eur J Hum Genet. 2015;23:1423–1426.
Middleton A, Morley KI, Bragin E, et al. Attitudes of nearly 7000 health professionals, genomic researchers and publics toward the return of incidental results from sequencing research. Eur J Hum Genet. 2016;24:21–29.
Allen NL, Karlson EW, Malspeis S, Lu B, Seidman CE, Lehmann LS. Biobank participants’ preferences for disclosure of genetic research results: perspectives from the OurGenes, OurHealth, OurCommunity project. Mayo Clin Proc. 2014;89:738–746.
Bollinger JM, Scott J, Dvoskin R, Kaufman D. Public preferences regarding the return of individual genetic research results: findings from a qualitative focus group study. Genet Med. 2012;14:451–457.
Finkler K, Skrzynia C, Evans JP. The new genetics and its consequences for family, kinship, medicine and medical genetics. Soc Sci Med. 2003;57:403–412.
Glanz K, Grove J, Lerman C, Gotay C, Le Marchand L. Correlates of intentions to obtain genetic counseling and colorectal cancer gene testing among at-risk relatives from three ethnic groups. Cancer Epidemiol Biomarkers Prev. 1999;8:329–336.
Hens K, Nys H, Cassiman JJ, Dierickx K. The return of individual research findings in paediatric genetic research. J Med Ethics. 2011;37:179–183.
Bredenoord AL, Onland-Moret NC, Van Delden JJM. Feedback of individual genetic results to research participants: in favor of a qualified disclosure policy. Hum Mutat. 2011;32:861–867.
Kaufman D, Murphy J, Scott J, Hudson K. Subjects matter: a survey of public opinions about a large genetic cohort study. Genet Med. 2008;10:831–839.
Murphy J, Scott J, Kaufman D, Geller G, LeRoy L, Hudson K. Public expectations for return of results from large-cohort genetic research. Am J Bioeth. 2008;8:36–43.
Hensher DA, Rose JM, Greene WH. Applied choice analysis: a primer. New York: Cambridge University Press; 2005.
Jackson L, Goldsmith L, O’Connor A, Skirton H. Incidental findings in genetic research and clinical diagnostic tests: a systematic review. Am J Med Genet A. 2012;158A:3159–3167.
Daack-Hirsch S, Driessnack M, Hanish A, et al. “Information is information”: a public perspective on incidental findings in clinical and research genome-based testing. Clin Genet. 2013;84:11–18.
Haukkala A, Kujala E, Alha P, et al. The return of unexpected research results in a biobank study and referral to health care for heritable long QT syndrome. Public Health Genomics. 2013;16:241–250.
Regier DA, Peacock SJ, Pataky R, et al. Societal preferences for the return of incidental findings from clinical genomic sequencing: a discrete-choice experiment. Can Med Assoc J. 2015;187:E190–E197.
Bollinger JM, Bridges JFP, Mohamed A, Kaufman D. Public preferences for the return of research results in genetic research: a conjoint analysis. Genet Med. 2014;16:932–939.
Viberg Johansson J, Segerdahl P, Ugander UH, Hansson MG, Langenskiold S. Making sense of genetic risk: a qualitative focus-group study of healthy participants in genomic research. Patient Educ Couns. 2018;101:422–427.
Hiligsmann M, van Durme C, Geusens P, et al. Nominal group technique to select attributes for discrete choice experiments: an example for drug treatment choice in osteoporosis. Patient Prefer Adherence. 2013;7:133–139.
Timmermans DRM. What clinicians can offer: assessing and communicating probabilities for individual patient decision making. Horm Res. 1999;51:58–66.
Piercey MD. Motivated reasoning and verbal vs. numerical probability assessment: evidence from an accounting context. Organ Behav Hum Dec. 2009;108:330–341.
Harrison M, Rigby D, Vass C, Flynn T, Louviere J, Payne K. Risk as an attribute in discrete choice experiments: a systematic review of the literature. Patient. 2014;7:151–170.
Bridges JFP, Hauber AB, Marshall D, et al. Conjoint analysis applications in health—a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. Value Health. 2011;14:403–413.
Ryan M, Watson V, Entwistle V. Rationalising the "Irrational”: a think aloud study of discrete choice experiment responses. Health Econ. 2009;18:321–336.
Cheraghi-Sohi S, Bower P, Mead N, McDonald R, Whalley D, Roland M. Making sense of patient priorities: applying discrete choice methods in primary care using "think aloud” technique. Fam Pract. 2007;24:276–282.
Wangdahl JM, Martensson LI. The communicative and critical health literacy scale—Swedish version. Scand J Public Health. 2014;42:25–31.
Bergstrom G, Berglund G, Blomberg A, et al. The Swedish CArdioPulmonary BioImage Study: objectives and design. J Intern Med. 2015;278:645–659.
Wangdahl J, Lytsy P, Martensson L, Westerling R Health literacy among refugees in Sweden—a cross-sectional study. BMC Public Health. 2014;14:1030.
Bech M, Gyrd-Hansen D. Effects coding in discrete choice experiments. Health Econ. 2005;14:1079–1083.
de Bekker-Grob EW, Ryan M, Gerard K. Discrete choice experiments in health economics: a review of the literature. Health Econ. 2012;21:145–172.
Louviere JJ, Hensher DA, Swait JD. Stated choice methods: analysis and application. Cambridge: Cambridge University Press; 2000.
Ryan M, Gerard K, Amaya-Amaya M. Using discrete choice experiments to value health and health care. Dordrecht: Springer; 2008.
Ploug T, Holm S. Clinical genome sequencing and population preferences for information about “incidental” findings D From medically actionable genes (MAGs) to patient actionable genes (PAGs). PLoS ONE 2017;12:e0179935.
Sanderson SC, Linderman MD, Suckiel SA, et al. Motivations, concerns and preferences of personal genome sequencing research participants: baseline findings from the HealthSeq project. Eur J Hum Genet. 2016;24:153–153.
Wynn J, Martinez J, Duong J, et al. Research participants’ preferences for hypothetical secondary results from genomic research. J Genet Couns. 2017;26:841–851.
Zimmern RL, Kroese M. The evaluation of genetic tests. J Public Health (Oxf.). 2007;29:246–250.
Kalia SS, Adelman K, Bale SJ, et al. Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SFv2.0): a policy statement of the American College of Medical Genetics and Genomics. Genet Med. 2017;19:484–485.
This work has been supported by a grant to the project Mind the Risk from the Swedish Foundation for Humanities and Social Sciences (grant number PR2013–0123); the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement number 305444 (RD-Connect); the Innovative Medicines Initiative project BTCure (grant agreement number 115142–1); the BioBanking and Molecular Resource Infrastructure of Sweden project, BBMRI.se (financed by the Swedish Research Council); Euro-TEAM; BiobankCloud; and Biobanking and Biomolecular Resources Research Infrastructure (BBMRI) LPC (313010). We acknowledge support from SCAPIS funded by the Swedish Heart–Lung Foundation for providing access to their cohort.
The authors declare no conflicts of interest.
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