Underlying Ambiguities in Genetic Privacy Legislation

1999 ◽  
Vol 3 (4) ◽  
pp. 341-345 ◽  
Author(s):  
MARY TERRELL WHITE
1995 ◽  
Vol 23 (4) ◽  
pp. 367-370 ◽  
Author(s):  
Neil A. Holtzman

The Genetic Privacy Act (GPA) is a comprehensive effort to protect individuals from unauthorized analysis of their DNA and from unauthorized disclosure of information resulting from genetic analysis. Irrespective of merit, every bill must survive legislative scrutiny. This is a considerable challenge, particularly for a bill as complex and far-reaching as the GPA. To illustrate my point, I describe the fate of two bills introduced into the Maryland Senate in 1995 by Senator Jennie Forehand. The first, also entitled the Genetic Privacy Act (S. 645), was a slightly modified version of the model legislation prepared by Annas, Glantz, and Roche. After a hearing, the bill received a 9-2 unfavorable vote from the Economic and Environmental Affairs Committee. The second was a much shorter bill, DNA Testing – Informed Consent and Confidentiality (S. 707), which simply stated that “DNA analysis may only be performed with the informed consent of the person being analyzed” and that the results of such analysis “are the exclusive property of the person tested, are confidential, and may not be disclosed without the consent of the person being tested.” This bill had a hearing but was never put to a vote by the Judicial Proceedings Committee. My principal aim is to examine the testimony on these bills. I will conclude with some suggestions about accomplishing the goals of genetic privacy legislation.


1998 ◽  
Vol 2 (1) ◽  
pp. 37-41 ◽  
Author(s):  
JANICE G. EDWARDS ◽  
S. ROBERT YOUNG ◽  
KAREN A. BROOKS ◽  
JANE H. AIKEN ◽  
ELIZABETH D. PATTERSON ◽  
...  

1997 ◽  
Vol 2 (1) ◽  
pp. 83-87 ◽  
Author(s):  
D HFARKAS ◽  
H GOERL ◽  
R HYER

2000 ◽  
Vol 4 (1) ◽  
pp. 31-42 ◽  
Author(s):  
Ami S. Jaeger ◽  
William F. Mulholland

2021 ◽  
Vol 11 (6) ◽  
pp. 543
Author(s):  
Anna DiNucci ◽  
Nora B. Henrikson ◽  
M. Cabell Jonas ◽  
Sundeep Basra ◽  
Paula Blasi ◽  
...  

Ovarian cancer (OVCA) patients may carry genes conferring cancer risk to biological family; however, fewer than one-quarter of patients receive genetic testing. “Traceback” cascade testing —outreach to potential probands and relatives—is a possible solution. This paper outlines a funded study (U01 CA240747-01A1) seeking to determine a Traceback program’s feasibility, acceptability, effectiveness, and costs. This is a multisite prospective observational feasibility study across three integrated health systems. Informed by the Conceptual Model for Implementation Research, we will outline, implement, and evaluate the outcomes of an OVCA Traceback program. We will use standard legal research methodology to review genetic privacy statutes; engage key stakeholders in qualitative interviews to design communication strategies; employ descriptive statistics and regression analyses to evaluate the site differences in genetic testing and the OVCA Traceback testing; and assess program outcomes at the proband, family member, provider, system, and population levels. This study aims to determine a Traceback program’s feasibility and acceptability in a real-world context. It will account for the myriad factors affecting implementation, including legal issues, organizational- and individual-level barriers and facilitators, communication issues, and program costs. Project results will inform how health care providers and systems can develop effective, practical, and sustainable Traceback programs.


Nature ◽  
2021 ◽  
Author(s):  
Stefanie Warnat-Herresthal ◽  
◽  
Hartmut Schultze ◽  
Krishnaprasad Lingadahalli Shastry ◽  
Sathyanarayanan Manamohan ◽  
...  

AbstractFast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.


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