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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.

F

Fairness and transparency

Fairness and transparency are, together with lawfulness, the first principle of data protection in Art. 5(1)(a) GDPR: personal data must be processed lawfully, fairly and in a transparent manner in relation to the data subject.

Transparency is concretised in Art. 12 to 14: the controller must provide concise, intelligible, easily accessible information in clear and plain language about who is processing the data, why, on what legal basis, for how long, with whom it is shared, whether it leaves the EEA and what rights the individual has (see right of information and privacy notice). The EDPB Guidelines on transparency (WP260) stress layered notices and the need to inform individuals about processing they would not reasonably expect.

Fairness is less codified but no less enforceable. It requires that processing does not exploit, deceive or disadvantage individuals, that expectations set at collection are honoured, and that power imbalances (employer and employee, sponsor and patient) are not abused. The EDPB Guidelines 03/2022 on deceptive design patterns treat "dark patterns" in interfaces as breaches of fairness. In clinical research, fairness and transparency mean that informed consent forms and information notices describe all data recipients, including CROs, vendors and non-EU affiliates, and that re-use of data for purposes not disclosed at enrolment is either compatible under Art. 6(4) or subject to fresh information. These principles are also the anchor of the AI Act's transparency obligations for AI systems interacting with people.

Does a detailed privacy notice prove that processing is fair?

No. Transparency explains the processing; fairness also concerns how it affects people and whether it misleads or takes advantage of them. A long notice cannot cure an unfair practice. For a patient-facing service, check that the actual data use matches the explanation and that important consequences are understandable before the person engages.