A co-produced framework for assessing common law basis of consent to enable record linkage for public good research.

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Jacqui Oakley
Andy Boyd
Abigail Harrison
Emma Turner

Abstract

UK-wide Longitudinal Population Studies (LPS) have been brought together and linked to NHS, socioeconomic records and environmental data within a Trusted Research Environment. To ensure compatibility with data processing, access and to enable public good research we needed to develop a way to assess the legal basis of LPS. With NHS England, we co-developed key criteria for assessing common law duty of confidentiality. A framework was developed for assessing information provided to participants. The framework focused on compatibility with our research purpose (for public good), data processing, and data access. Template documents were produced to capture communications and key statements providing evidence relating to the criteria. A risk-based approach was adopted and a Confidentiality Due Diligence Panel recruited and trained University Data Protection Officer, Governance leads, cross-disciplinary academic representation and members of the public recruited to the Panel). The framework was formalised and documented, guidance, risk-assessment approach and outputs agreed with NHS England. 27 UK-LPS have completed the assessment. NHS England is sharing the framework as a model of best practice, and we will scale this approach through an open UK-wide call for new partner LPS in 2026. Co-development and implementation of a review framework for common law basis enabled robust and reproducible assessment of multiple LPS consent, information and communications materials. This allowed identification of LPS which have set reasonable expectations or advise on alternative legal permissions, and communications to enable record linkage in-line with participants wishes.

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How to Cite
Oakley, J., Boyd, A., Harrison, A. and Turner, E. (2026) “A co-produced framework for assessing common law basis of consent to enable record linkage for public good research”., International Journal of Population Data Science, 11(5). doi: 10.23889/ijpds.v11i5.3510.