PURPOSE: In 2006, the Department of Veterans Affairs launched the Genomic Medicine Program with the goal of using genomic information to personalize and improve health care for veterans. A step toward this goal is the Million Veteran Program, which aims to enroll a million veterans in a longitudinal cohort study and establish a database with genomic, lifestyle, military-exposure, and health information. Before the launch of the Million Veteran Program, a survey of Department of Veterans Affairs patients was conducted to measure preferences for opt-in and opt-out models of enrollment and consent. METHODS: An online survey was conducted with a random sample of 451 veterans. The survey described the proposed Million Veteran Program database and asked respondents about the acceptability of opt-in and opt-out models of enrollment. The study examined differences in responses among demographic groups and relationships between beliefs about each model and willingness to participate. RESULTS: Most respondents were willing to participate under both opt-in (80%) and opt-out (69%) models. Nearly 80% said they would be comfortable providing access to residual clinical samples for research. At least half of respondents did not strongly favor one model over the other; of those who expressed a preference, significantly more people said they would participate in a study using opt-in methods. Stronger preferences for the opt-in approach were expressed among younger patients and Hispanic patients. CONCLUSION: Support for the study and willingness to participate were high for both enrollment models. The use of an opt-out model could impede recruitment of certain demographic groups, including Hispanic patients and patients under the age of 55 years.
PURPOSE: In 2006, the Department of Veterans Affairs launched the Genomic Medicine Program with the goal of using genomic information to personalize and improve health care for veterans. A step toward this goal is the Million Veteran Program, which aims to enroll a million veterans in a longitudinal cohort study and establish a database with genomic, lifestyle, military-exposure, and health information. Before the launch of the Million Veteran Program, a survey of Department of Veterans Affairs patients was conducted to measure preferences for opt-in and opt-out models of enrollment and consent. METHODS: An online survey was conducted with a random sample of 451 veterans. The survey described the proposed Million Veteran Program database and asked respondents about the acceptability of opt-in and opt-out models of enrollment. The study examined differences in responses among demographic groups and relationships between beliefs about each model and willingness to participate. RESULTS: Most respondents were willing to participate under both opt-in (80%) and opt-out (69%) models. Nearly 80% said they would be comfortable providing access to residual clinical samples for research. At least half of respondents did not strongly favor one model over the other; of those who expressed a preference, significantly more people said they would participate in a study using opt-in methods. Stronger preferences for the opt-in approach were expressed among younger patients and Hispanic patients. CONCLUSION: Support for the study and willingness to participate were high for both enrollment models. The use of an opt-out model could impede recruitment of certain demographic groups, including Hispanic patients and patients under the age of 55 years.
Authors: S Trent Rosenbloom; Jennifer L Madison; Kyle B Brothers; Erica A Bowton; Ellen Wright Clayton; Bradley A Malin; Dan M Roden; Jill Pulley Journal: J Am Med Inform Assoc Date: 2013-07-25 Impact factor: 4.497
Authors: Jeffrey R Botkin; Rebecca Anderson; Tom Murray; Laura M Beskow; Karen Maschke; Leona Cuttler Journal: Am J Med Genet A Date: 2014-01-23 Impact factor: 2.802
Authors: Claudia A Kozinetz; Kathryn Royse; Sarah C Graham; Xiaoying Yu; Jack Moye; Beatrice J Selwyn; Michele R Forman; Chantal Caviness Journal: J Community Genet Date: 2016-02-11
Authors: Katherine M Brown; Bettina F Drake; Sarah Gehlert; Leslie E Wolf; James DuBois; Joann Seo; Krista Woodward; Hannah Perkins; Melody S Goodman; Kimberly A Kaphingst Journal: J Community Genet Date: 2015-08-25
Authors: Muhammad Naveed; Erman Ayday; Ellen W Clayton; Jacques Fellay; Carl A Gunter; Jean-Pierre Hubaux; Bradley A Malin; Xiaofeng Wang Journal: ACM Comput Surv Date: 2015-09 Impact factor: 10.282