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: 8915734
• 负责人: Bradley A. Malin
• 金额: $ 0.88万
• 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 2016-04-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8915734
• 关键词: Access to Information; Address; Adopted; Adoption; Agreement; Clinical; Clinical Data; 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; Health Insurance Portability and Accountability Act; Healthcare; High-Throughput Nucleotide Sequencing; Human Genome Project; Hybrids; Individual; Label; Legal; Literature; Marketing; Measurement; Measures; 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; Solutions; Staging; Structure; Time; Work; base; common rule; computerized data processing; computerized tools; cost; court; data sharing; design; firewall; health care delivery; improved; personalized medicine; population based; 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）：当人类基因组计划在大约十年前完成时，它花费了数百万美元来测序个人的基因组。然而，高通量测序和计算工具的发展非常迅速，很快就可以以象征性的价格对任何人进行基因分型。生成基因组数据的能力与电子健康记录的采用相吻合，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服务的效率，有效性和安全性。为了减少基于人群的研究的障碍，基因组和临床数据通常通过各种政策和法规的去识别化指定来提供。然而，越来越多的人认为去识别是一种谬论，生物医学数据可以相对容易地重新识别。这一论点，这是部分基于我们自己的研究，形成了在文献和法院案件的立法和监管修改的呼吁的核心。最值得注意的是，最近提出的规则制定的预先通知（ANPRM）询问生物标本以及衍生的基因组数据是否应该重新定义为固有可识别的。这样的标签将需要修改通用规则和HIPAA隐私规则，并可能影响研究基因组数据的可用性。很明显，即使在汇总统计的背景下，也只需要少量的基因组数据就可以独特地区分个体。然而，与此同时，必须认识到，“可区分”并不等同于“可识别”，尽管重新识别是可能的，但这并不意味着它是可能的。不应轻视可识别性问题，但目前没有合理的基础来推理这种风险，从而限制了作出知情政策决定的能力。有许多因素与可识别性相关，包括与基因组数据共享的信息（例如，临床、人口统计学）、与谁共享、存在哪些其他数据源以及相关的法律的环境。先前基因组可识别性研究的一个限制因素是他们孤立地考虑这些因素，这提供了一个不完整的画面。为了填补这一空白，我们研究的首要目标是设计一个基础，植根于伦理，法律的和计算形式主义，为推理和管理基因组数据可识别性风险提供基础。这一基础将通过具体目标实现：（1）建立一个协议，用于建模共享基因组数据可以证实重新识别问题的程度，（2）设计和评估风险评估协议的基因组可识别性的实用措施，（3）开发一种策略，提供减轻基因组数据识别风险的选项。我们设想了这个项目的几个显著成果。首先，这项工作将产生指导方针和风险评估策略，可供基因组数据管理者和政策制定者使用，以告知他们关于可识别性的决定。其次，我们将使用一个真实的、大型的去识别化临床和基因组数据数据库对我们的框架进行评估，以提供切实可行的结果。