scholarly journals Care and Precision Medicine Research in the Time of COVID-19

2021 ◽  
Vol 31 (3) ◽  
pp. 407-410
Author(s):  
Juliana M. Ison ◽  
Elizabeth W. Karlson ◽  
Jonathan D. Jackson ◽  
Alexander Hille ◽  
Cheryl McCloud ◽  
...  

Purpose: Enhancing the bidirectional benefit of precision medicine research in­frastructure may advance equity in research participation for diverse groups. This study explores the use of research infrastructure to provide human-centered COVID-19 resources to participants as a part of their research participation.Design: The All of Us New England (AoUNE) consortium research team devel­oped standardized check-in telephone calls to ask participants about their well-being and share COVID-19 resources.Participants: A total of 20,559 partici­pants in the AoUNE consortium received a COVID-19 check-in call.Methods: Research assistants called partici­pants during March-April 2020, distributed COVID-19 resources to interested partici­pants, and subsequently rated call tone.Results: Of the total cohort participants called, 8,512 (41%) spoke with a research team member. The majority of calls were rated as positive or neutral; only 3% rated as negative. African American and Black as well as Hispanic populations requested COVID-19 resources at higher rates than other groups.Conclusion: Calls made to AoUNE participants were received positively by diverse groups. These findings may have implications for participant-centered en­gagement strategies in precision medicine research.Ethn Dis. 2021;31(3):407-410; doi:10.18865/ed.31.3.407

Author(s):  
Diana C. Garofalo ◽  
Howard A. Rosenblum ◽  
Yuan Zhang ◽  
Ying Chen ◽  
Paul S. Appelbaum ◽  
...  

2020 ◽  
Vol 30 (Suppl 1) ◽  
pp. 217-228 ◽  
Author(s):  
Sanjay Basu ◽  
James H. Faghmous ◽  
Patrick Doupe

  Precision medicine research designed to reduce health disparities often involves studying multi-level datasets to understand how diseases manifest disproportionately in one group over another, and how scarce health care resources can be directed precisely to those most at risk for disease. In this article, we provide a structured tutorial for medical and public health research­ers on the application of machine learning methods to conduct precision medicine research designed to reduce health dispari­ties. We review key terms and concepts for understanding machine learning papers, including supervised and unsupervised learning, regularization, cross-validation, bagging, and boosting. Metrics are reviewed for evaluating machine learners and major families of learning approaches, including tree-based learning, deep learning, and ensemble learning. We highlight the advan­tages and disadvantages of different learning approaches, describe strategies for interpret­ing “black box” models, and demonstrate the application of common methods in an example dataset with open-source statistical code in R.Ethn Dis. 2020;30(Suppl 1):217-228; doi:10.18865/ed.30.S1.217


2019 ◽  
Vol 21 (10) ◽  
pp. 2319-2327 ◽  
Author(s):  
Maya Sabatello ◽  
Ying Chen ◽  
Yuan Zhang ◽  
Paul S. Appelbaum

PLoS ONE ◽  
2016 ◽  
Vol 11 (7) ◽  
pp. e0154850 ◽  
Author(s):  
Chanita Hughes Halbert ◽  
Jasmine McDonald ◽  
Susan Vadaparampil ◽  
LaShanta Rice ◽  
Melanie Jefferson

2020 ◽  
Vol 17 (5) ◽  
pp. 345-359
Author(s):  
Kelsey Moriarty ◽  
Susan M Wolf ◽  
Patricia M Veach ◽  
Bonnie LeRoy ◽  
Ian M MacFarlane ◽  
...  

Aim: Precision medicine research recruitment poses challenges. To better understand factors impacting recruitment, this study assessed hypothetical willingness, public opinions of and familiarity with precision medicine research. Materials & methods: Adult attendees (n = 942) at the 2017 Minnesota State Fair completed an electronic survey. Results: Few respondents had heard of ‘precision medicine’ (18%), and familiarity came mostly from media (43%). Fifty-six percent expressed hypothetical willingness to participate in precision medicine research. Significant predictors of willingness were: comfort with unconditional research; perceiving precision medicine research as beneficial, trustworthy and confidential; having a graduate degree; comfort with self- but not family-participation; and familiarity with precision/personalized medicine. Conclusion: This study identified predictors of hypothetical willingness to participate in precision medicine research. Alternative recruitment strategies are needed.


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