Kassam-Adams Nancy, Thompson Kristi, Sijbrandij Marit, Dyb Grete
Center for Injury Research & Prevention, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Western Libraries, Western University, London, Canada.
Eur J Psychotraumatol. 2025 Dec;16(1):2499296. doi: 10.1080/20008066.2025.2499296. Epub 2025 May 19.
FAIR Data practices support data sharing and re-use and are essential for advancing science and practice to benefit individuals, families, and communities affected by trauma. In traumatic stress research, as in other health and social science research, ethical, legal, and regulatory frameworks require careful attention to data privacy. Most traumatic stress researchers are aware of basic methods for de-identifying/anonymising datasets that are to be shared. But our field has not generally made use of systematic, data analytic approaches to reduce the risk of re-identification of study participants or disclosure of personal or sensitive information. To facilitate safe and ethical data sharing by better preparing traumatic stress researchers to systematically assess and reduce re-identification risk using contemporary data analytic methods. In two case studies using publicly available trauma research datasets from international, multi-language projects, we applied a systematic approach guided by the Checklist for Reducing Re-Identification Risk in Traumatic Stress Research Data. For each case study dataset, we identified specific recommended actions to further reduce the risk of re-identification, and we then communicated these recommendations to the original investigators. After implementing the recommended changes, each dataset is judged to be at very low re-identification risk. The particular nature of traumatic stress research, i.e. its content, data, and study designs, can influence the likelihood and potential impact of re-identification or disclosure. The two worked case examples in this paper demonstrate the utility of applying a systematic approach to assess and further mitigate re-identification risk in shared datasets. At each stage of the research data lifecycle, there are research practices and choices relevant to reducing re-identification risk. This paper presents practical tips for research teams to facilitate FAIR data practices while attending to data privacy.
公平数据实践支持数据共享和再利用，对于推动科学发展和实践以造福受创伤影响的个人、家庭和社区至关重要。在创伤应激研究中，与其他健康和社会科学研究一样，伦理、法律和监管框架要求仔细关注数据隐私。大多数创伤应激研究人员都知道对要共享的数据集进行去识别/匿名化的基本方法。但我们这个领域一般没有采用系统的数据分析方法来降低研究参与者被重新识别或个人或敏感信息被披露的风险。为了通过更好地让创伤应激研究人员做好准备，以便使用当代数据分析方法系统地评估和降低重新识别风险，从而促进安全和符合伦理的数据共享。在两项使用来自国际多语言项目的公开可用创伤研究数据集的案例研究中，我们应用了一种以《降低创伤应激研究数据中重新识别风险清单》为指导的系统方法。对于每个案例研究数据集，我们确定了进一步降低重新识别风险的具体建议行动，然后将这些建议传达给原始研究人员。在实施建议的更改后，每个数据集被判定为重新识别风险非常低。创伤应激研究的特殊性质，即其内容、数据和研究设计，会影响重新识别或披露的可能性和潜在影响。本文中的两个实际工作案例展示了应用系统方法评估和进一步降低共享数据集中重新识别风险的效用。在研究数据生命周期的每个阶段，都有与降低重新识别风险相关的研究实践和选择。本文为研究团队提供了实用技巧，以便在关注数据隐私的同时促进公平数据实践。
