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Term of the Day

Natural history study

A natural history study is a preplanned observational study intended to track the course of a disease over time, identifying demographic, genetic, environmental and other variables that correlate with its development and outcomes in the absence of intervention, or under standard of care. Designs may be retrospective (chart review of existing records) or prospective (longitudinal follow-up of a cohort or registry).

Natural history data is particularly important in rare and paediatric diseases, where randomised placebo-controlled trials may be infeasible or unethical. The FDA (guidance on rare disease natural history studies, 2019) and the EMA accept well-designed natural history studies to define endpoints and biomarkers, identify patient subgroups, estimate sample sizes and, in some cases, serve as external or historical control arms for single-arm trials supporting orphan products.

Because they are non-interventional, natural history studies fall outside the CTR and are governed by national law (for example France's MR-003 or MR-004 reference methodologies) and by the GDPR. They typically involve secondary use of medical records, long-term follow-up, genetic data and small populations in which anonymisation is rarely achievable, so pseudonymisation, a DPIA and a robust research legal basis under Art. 9(2)(j) are essential. Registries maintained by patient organisations or academic consortia raise additional questions of joint controllership and data access governance.

P

Privacy-enhancing technologies (PETs)

Privacy-enhancing technologies (PETs) are software, hardware and methodological solutions that protect personal data by minimising identifiability, limiting disclosure or enabling computation without revealing underlying data, while preserving the utility of the data for legitimate purposes. The EDPB, ENISA, the OECD and the UK ICO have each published PET taxonomies and guidance, and the GDPR refers to several PETs as examples of data protection by design and security measures (Art. 25 and 32).

The main families are: pseudonymisation and tokenisation; encryption at rest, in transit and in use (homomorphic encryption, trusted execution environments); anonymisation techniques such as k-anonymity, generalisation and differential privacy; federated learning and secure multi-party computation, which allow models or statistics to be computed across institutions without pooling raw data; synthetic data generation; and zero-knowledge proofs for authentication without disclosure.

PETs are not a legal category and do not by themselves take data outside the GDPR: pseudonymised and encrypted data remain personal data, and whether a synthetic or differentially private dataset is anonymous must be assessed case by case. Their value lies in reducing risk, which lowers the DPIA risk score, supports supplementary measures for international transfers, and enables secondary use of health data through health data warehouses and the European Health Data Space. Regulators expect their use to be documented and their limits understood.