Multiple modes of data sharing can facilitate secondary use of sensitive health data for research.
Multiple modes of data sharing can facilitate secondary use of sensitive health data for research.
复制标题
DOI：
10.1136/bmjgh-2023-013092
复制
发表时间：
2023-10
影响因子：
8.1
通讯作者：
Tiffin, Nicki
中科院分区：
文献类型：
--
作者：
Tamuhla, Tsaone;Lulamba, Eddie T.;Mutemaringa, Themba;Tiffin, Nicki
基于证据的医疗保健依赖于来自不同来源的健康数据，为不同领域的决策提供信息，包括疾病预防、病因学、诊断、治疗和预后。随着越来越多的人认识到健康数据是高度敏感的，进一步的研究使用可能会给提供数据的个人带来隐私问题，越来越多的高度细粒度数据为利用证据基础提供了机会。对未经明确知情同意的数据用于二次研究的担忧加剧。此外，研究人员——尤其是来自资源不足的环境和全球南方的研究人员——可能希望参与对他们收集的资源的后续分析，或保留对后续使用的监督，以确保遵守伦理约束。根据数据敏感性和二次使用限制，可以采用不同的数据共享方式，超越传统的数据从生成器向二次用户单向传输的开放获取模式。我们描述了协作数据共享，通过结合数据集和涉及合作伙伴的元分析来促进研究；联合数据分析，合作伙伴对各自独立的数据集进行同步、协调的分析，然后将结果合并到一份共同撰写的报告中；以及可信的研究环境，在受控的环境中分析数据，只输出汇总结果。我们回顾了包括数据扰动在内的去识别和匿名化方法如何能够降低与卫生数据二次使用具体相关的风险。此外，我们提出了一种创新的模块化方法来构建数据共享协议，其中包含了一种更细致的数据共享方法来保护隐私，并为每个数据共享场景提供了构建协议的框架。
Evidence-based healthcare relies on health data from diverse sources to inform decision-making across different domains, including disease prevention, aetiology, diagnostics, therapeutics and prognosis. Increasing volumes of highly granular data provide opportunities to leverage the evidence base, with growing recognition that health data are highly sensitive and onward research use may create privacy issues for individuals providing data. Concerns are heightened for data without explicit informed consent for secondary research use. Additionally, researchers—especially from under-resourced environments and the global South—may wish to participate in onward analysis of resources they collected or retain oversight of onward use to ensure ethical constraints are respected. Different data-sharing approaches may be adopted according to data sensitivity and secondary use restrictions, moving beyond the traditional Open Access model of unidirectional data transfer from generator to secondary user. We describe collaborative data sharing, facilitating research by combining datasets and undertaking meta-analysis involving collaborating partners; federated data analysis, where partners undertake synchronous, harmonised analyses on their independent datasets and then combine their results in a coauthored report, and trusted research environments where data are analysed in a controlled environment and only aggregate results are exported. We review how deidentification and anonymisation methods, including data perturbation, can reduce risks specifically associated with health data secondary use. In addition, we present an innovative modularised approach for building data sharing agreements incorporating a more nuanced approach to data sharing to protect privacy, and provide a framework for building the agreements for each of these data-sharing scenarios.
登录
查看更多内容
影响因子：
56.9
作者：
Gymrek, Melissa;McGuire, Amy L.;Erlich, Yaniv
通讯作者：
Erlich, Yaniv
DOI：
10.1001/jama.1992.03490170092032
发表时间：
1992-11-04
影响因子：
120.7
作者：
GUYATT, G
通讯作者：
GUYATT, G
影响因子：
4.5
作者：
Ganzinger, Matthias;Blumenstock, Max;Dugas, Martin
通讯作者：
Dugas, Martin
影响因子：
--
作者：
Boulle, A;Heekes, A;Vallabhjee, K
通讯作者：
Vallabhjee, K
影响因子：
5.7
作者：
Jochems, Arthur;Deist, Timo M.;Dekker, Andre
通讯作者：
Dekker, Andre