Citations 3
Enrichment sampling for a multi-site patient survey using electronic health records and census data
Mercaldo, Nathaniel D. , Brothers, Kyle B. , Connolly, John J. , Horowitz, Carol R. , Jarvik, Gail P. , Kitchner, Terrie, Li, Rongling, McCarty, Catherine A. , McManus, Valerie, Myers, Melanie F. , Pankratz, Joshua J., Shrubsole, Martha J. , Smith, Maureen E. , Stallings, Sarah , Williams, Janet L. , Schildcrout, Jonathan S. , McCormick, Jennifer B, Mercaldo, Nathaniel D , Myers, Melanie F , Shrubsole, Martha J , Schildcrout, Jonathan S , Jarvik, Gail P , Connolly, John J , Carrell, David , Clayton, Ellen Wright , Holm, Ingrid A. , McCormick, Jennifer Blair
Journal of the American Medical Informatics Association | November 16, 2018 | Volume: 26 | Issue: 3 | Pages: 219-227
Abstract Objective We describe a stratified sampling design that combines electronic health records (EHRs) and United States Census (USC) data to construct the sampling frame and an algorithm to enrich the sample with individuals belonging to rarer strata. Materials and Methods This design was developed for a multi-site survey that sought to examine patient concerns about and barriers to participating in research studies, especially among under-studied populations (eg, minorities, low educational attainment). We defined sampling strata by cross-tabulating several socio-demographic variables obtained from EHR and augmented with census-block-level USC data. We oversampled rarer and historically underrepresented subpopulations. Results The sampling strategy, which included USC-supplemented EHR data, led to a far more diverse sample than would have been expected under random sampling (eg, 3-, 8-, 7-, and 12-fold increase in African Americans, Asians, Hispanics and those with less than a high school degree, respectively). We observed that our EHR data tended to misclassify minority races more often than majority races, and that non-majority races, Latino ethnicity, younger adult age, lower education, and urban/suburban living were each associated with lower response rates to the mailed surveys. Discussion We observed substantial enrichment from rarer subpopulations. The magnitude of the enrichment depends on the accuracy of the variables that define the sampling strata and the overall response rate. Conclusion EHR and USC data may be used to define sampling strata that in turn may be used to enrich the final study sample. This design may be of particular interest for studies of rarer and understudied populations.
Parents’ attitudes toward consent and data sharing in biobanks: A multisite experimental survey
Antommaria, Armand H. Matheny , Brothers, Kyle B. , Myers, John , Feygin, Yana B., Aufox, Sharon , Brilliant, Murray H. , Conway, Pat , Fullerton, Stephanie M. , Garrison, Nanibaa’ A. , Horowitz, Carol R. , Jarvik, Gail P. , Li, Rongling, Ludman, Evette, McCarty, Catherine A. , Mercaldo, Nathaniel D. , Myers, Melanie F. , Sanderson, Saskia C. , Shrubsole, Martha J. , Williams, Janet L. , Smith, Maureen E. , Aufox, Sharon A , McCormick, Jennifer Blair , Mercaldo, Nathaniel D , Myers, Melanie F , Shrubsole, Martha J , Schildcrout, Jonathan S , Williams, Janet L , Smith, Maureen E , Clayton, Ellen Wright , Holm, Ingrid A , Jarvik, Gail P , Schildcrout, Jonathan S. , Clayton, Ellen Wright , Holm, Ingrid A.
AJOB Empirical Bioethics | September 21, 2018 | Volume: 9 | Issue: 3 | Pages: 128-142
The Pharmacogenomics Research Network Translational Pharmacogenetics Program: Outcomes and Metrics of Pharmacogenetic Implementations Across Diverse Healthcare Systems
Luzum, Jasmine A. , Pakyz, R E, Elsey, AR, Haidar, Cyrine E. , Peterson, JF, Whirl‐Carrillo, Michelle , Handelman, SK, Palmer, Kathleen , Pulley, JM, Beller, Michael, Schildcrout, JS, Field, JR, Weitzel, KW, Cooper‐DeHoff, RM, Cavallari, Larisa H. , O'Donnell, PH, Altman, Russ B. , Pereira, Naveen L. , Ratain, MJ, Roden, DM, Embi, PJ, Klein, Teri E. , Johnson, JA, Relling, MV, Wang, L, Weinshilboum, RM, Freimuth, Robert R. , Johnson, Julie A. , Peterson, Josh F. , Schildcrout, Jonathan S. , Elsey, Amanda R., Peterson, JF, Weitzel, KW, Cooper-DeHoff, RM, Ratain, Mark J. , Roden, DM, Sadée, Wolfgang , Relling, Mary V. , Shuldiner, Alan R. , Schildcrout, Jonathan S , Whirl-Carrillo, Michelle , Freimuth, Robert R , Peterson, Josh F , Cavallari, Larisa H , Luzum, Jasmine A , Klein, Teri E
Clinical Pharmacology & Therapeutics | January 16, 2017 | Volume: 102 | Issue: 3 | Pages: 502-510
Numerous pharmacogenetic clinical guidelines and recommendations have been published, but barriers have hindered the clinical implementation of pharmacogenetics. The Translational Pharmacogenetics Program (TPP) of the National Institutes of Health (NIH) Pharmacogenomics Research Network was established in 2011 to catalog and contribute to the development of pharmacogenetic implementations at eight US healthcare systems, with the goal to disseminate real‐world solutions for the barriers to clinical pharmacogenetic implementation. The TPP collected and normalized pharmacogenetic implementation metrics through June 2015, including gene–drug pairs implemented, interpretations of alleles and diplotypes, numbers of tests performed and actionable results, and workflow diagrams. TPP participant institutions developed diverse solutions to overcome many barriers, but the use of Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines provided some consistency among the institutions. The TPP also collected some pharmacogenetic implementation outcomes (scientific, educational, financial, and informatics), which may inform healthcare systems seeking to implement their own pharmacogenetic testing programs.