Sharing traumatic stress research data: assessing and reducing the risk of re-identification

Background: 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.Objective: 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.Method: 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.Results: 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.Discussion: 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.