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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.

D

Differential privacy

Differential privacy is a formal, mathematical definition of privacy protection for statistical analyses and machine-learning models. An algorithm is differentially private if its output changes only negligibly, within a bound set by the privacy parameter epsilon (and, in the relaxed version, delta), whether or not any single individual's record is included in the input dataset. This is achieved by adding carefully calibrated random noise to queries, statistics or model gradients, so that an observer cannot confidently infer anything about a specific person from the released result. The framework was introduced by Cynthia Dwork and colleagues in 2006 and has been deployed by the US Census Bureau, Apple, Google and others.

Differential privacy differs fundamentally from syntactic models such as k-anonymity: it does not modify the dataset itself but controls what can be learned from analyses of it, offers a quantifiable and composable guarantee that holds regardless of what background knowledge an attacker has, and degrades gracefully as more queries are answered, through a "privacy budget" that must be managed. Its cost is reduced accuracy, which is significant for small populations, rare events and detailed subgroups, precisely the situations that matter most in clinical and rare-disease research. Local differential privacy applies noise on the user's device before collection; central differential privacy applies it to the aggregated result.

In health data, differential privacy is used to publish aggregate statistics from registries and health data warehouses, to protect model updates in federated learning, to release synthetic data with provable guarantees, and to answer exploratory queries in secure processing environments such as those envisaged by the European Health Data Space. The EDPB and national authorities recognise it as a strong privacy-enhancing technology that supports anonymisation claims, but the underlying processing of personal data remains subject to the GDPR, and whether a differentially private output is anonymous depends on the chosen epsilon and on documentation of the parameters, which should be recorded in the DPIA and in any data-sharing agreement.