Data from a national survey of United States primary care physicians on genetic risk scores for common disease prevention
Charles A. Brunette,
Elizabeth J. Harris,
Ashley A. Antwi,
Amy A. Lemke,
Benjamin J. Kerman,
Jason L. Vassy
Affiliations
Charles A. Brunette
Veterans Affairs Boston Healthcare System, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Corresponding author at: VA Boston Healthcare System, 150 S Huntington Ave, Boston, MA 02130, USA.
Elizabeth J. Harris
Veterans Affairs Boston Healthcare System, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
Ashley A. Antwi
Veterans Affairs Boston Healthcare System, Boston, MA, USA
Amy A. Lemke
Norton Children's Research Institute, University of Louisville School of Medicine, Louisville, KY, USA
Benjamin J. Kerman
Department of Medicine, Harvard Medical School, Boston, MA, USA; Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA
Jason L. Vassy
Veterans Affairs Boston Healthcare System, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA; Precision Population Health, Ariadne Labs, Boston, MA, USA
Genetic risk scores (GRS) are an emerging and rapidly evolving genomic medicine innovation that may contribute to more precise risk stratification for disease prevention. Inclusion of GRS in routine medical care is imminent, and understanding how physicians perceive and intend to utilize GRS in practice is an important first step in facilitating uptake. This dataset was derived from an electronic survey and comprises one of the first, largest, and broadest samples of United States primary care physician perceptions on the clinical decision-making, benefits, barriers, and utility of GRS to date. The dataset is nearly complete (<1% missing data) and contains responses from 369 PCPs spanning 58 column variables. The public repository includes minimally filtered, de-identified data, all underlying survey versions and items, a data dictionary, and associated analytic files.
