A Risk Management Framework for Identifiability in Genomics Research

基因组学研究中可识别性的风险管理框架

• 批准号: 8548389
• 负责人: Bradley A. Malin
• 金额: $ 33.43万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-21 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8548389
• 关键词: Access to Information; Address; Adopted; Adoption; Agreement; Clinical; Clinical Data; Commit; Computers; Costs and Benefits; DNA Sequence Databases; Data; Data Protection; Data Sources; Databases; Effectiveness; Electronic Health Record; Engineering; Equilibrium; Ethics; Evaluation; Evolution; Foundations; Genome; Genomics; Genotype; Goals; Guidelines; Health Insurance Portability and Accountability Act; Healthcare; Human Genome Project; Hybrids; Individual; Label; Legal; Literature; Marketing; Measurement; Measures; Medicine; Methods; Modeling; Modification; Motivation; Nature; Outcome; Perception; Plant Roots; Policies; Policy Maker; Population; Price; Privacy; Protocols documentation; Provider; Regulation; Relative (related person); Research; Research Project Grants; Resources; Risk; Risk Assessment; Risk Management; Safety; Simulate; Solutions; Staging; Structure; Time; Work; base; common rule; computerized data processing; computerized tools; cost; court; data sharing; design; firewall; health care delivery; improved; population based; public health relevance; social; sound; statistics; tool; usability

DESCRIPTION (provided by applicant): When the Human Genome Project was completed almost ten years ago it cost millions of dollars to sequence an individual's genome. Yet, the evolution of high-throughput sequencing and computational tools has been swift and it will soon be possible to genotype anyone for a nominal price. The ability to generate genomic data coincides with the adoption of electronic health records, setting the stage for large-scale personalized medicine research, the results of which can improve the efficiency, effectiveness, and safety of healthcare delivery. To ease barriers to population-based research, genomic and clinical data are often made available via a de- identified designation by various policies and regulations. However, there is a growing perception that de- identification is a fallacy and that biomedical data can be re-identified with relative ease. This argument, which is partially based on our own studies, forms the core of calls for legislative and regulatory modifications in the literature and court cases. Most notably, a recent Advanced Notice of Proposed Rule Making (ANPRM) inquires if biospecimens, as well as derived genomic data, should be redefined as inherently identifiable. Such labeling would require changes to the Common Rule and HIPAA Privacy Rule and could influence the availability of genomic data for research. It is clear that only a small amount of genomic data is necessary to uniquely distinguish an individual, even in the context of aggregated statistics. However, at the same time, it must be recognized that "distinguishable" is not equivalent to "identifiable" and though re-identification is possible it des not imply it is probable. Identifiability concerns should not be trivialized, but there is currentl no sound basis for reasoning about such risks, limiting the ability to make informed policy decisions. There are many factors associated with identifiability, including the information shared with genomic data (e.g., clinical, demographic), with whom it is shared, what other sources of data exist, and the relevant legal landscape. A limiting factor of prior studies in genomic identifiability is their consideration of these factors in isolation, which provides an incomplete picture. To fill this void, the overarching objective of our research is to engineer a foundation, rooted in ethical, legal, and computational formalisms, that provides a basis for reasoning about, and managing, genomic data identifiability risks. This foundation will be realized through specific aims: (1) build a protocol for modeling the extent to which sharing genomic data can substantiate re-identification concerns, (2) design and evaluate practical measures of genomic identifiability for risk assessment protocols, (3) develop a strategy that supplies options to mitigate genomic data identification risks. We envision several notable outcomes from this project. First, this work will yield guidelines and risk assessment strategies that can be employed by genomic data managers and policy makers to inform their decisions regarding identifiability. Second, we will perform an evaluation of our framework with a real, large de-identified database of clinical and genomic data to provide tangible and pragmatic results.

描述（由申请人提供）：当人类基因组项目大约十年前完成时，它花费了数百万美元才能对个人的基因组进行测序。但是，高通量测序和计算工具的演变迅速，很快就有可能以标称价格进行基因型。产生基因组数据的能力与采用电子健康记录的采用，为大规模个性化医学研究奠定了基础，其结果可以提高医疗保健提供的效率，有效性和安全性。为了简化基于人群的研究的障碍，通常可以通过各种政策和法规来确定的指定基因组和临床数据。但是，人们越来越了解识别是谬论，并且可以相对轻松地重新识别生物医学数据。该论点部分基于我们自己的研究，构成了文献和法院案件中立法和法规修改的呼吁的核心。最值得注意的是，最近提出的规则制定（ANPRM）的高级通知询问生物测量以及衍生的基因组数据是否应重新定义为固有的可识别。这种标签将需要更改共同规则和HIPAA隐私规则，并可能影响基因组数据的可用性。显然，即使在汇总统计数据的背景下，也只有少量的基因组数据才能唯一区分个体。但是，与此同时，必须认识到，“可区分”并不等于“可识别”，尽管可以重新识别，但并不暗示它可能是可能的。可识别性问题不应被琐碎，但是当前没有关于这种风险推理的合理基础，从而限制了做出明智的政策决策的能力。与可识别性相关的因素很多，包括与基因组数据共享的信息（例如，临床，人口统计），与之共享的信息，其他数据源的存在以及相关的法律景观。先前研究基因组可识别性研究的限制因素是它们孤立地考虑这些因素，这提供了不完整的情况。为了填补这一空白，我们的研究的总体目标是设计一个植根于道德，法律和计算形式主义的基础，该基础为有关和管理基因组数据可识别性风险的推理提供了基础。该基础将通过特定的目的来实现：（1）建立一个协议，以建模共享基因组数据可以证实重新识别问题的程度，（2）设计和评估对风险评估方案的基因组识别性的实践测量，（3）开发一种策略，以提供减轻基因组数据识别风险的策略。我们设想了该项目的几个值得注意的结果。首先，这项工作将产生基因组数据经理和政策制定者可以采用的指南和风险评估策略，以告知他们有关可识别性的决定。其次，我们将使用临床和基因组数据的真实，大型识别数据库对框架进行评估，以提供有形和务实的结果。