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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.

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Anonymisation

Anonymisation is the process of rendering personal data anonymous in such a way that the data subject is not, or is no longer, identifiable by any means reasonably likely to be used (Recital 26 GDPR). Truly anonymous data falls outside the scope of the GDPR entirely.

The threshold is high. The former Article 29 Working Party (Opinion 05/2014) and the EDPB assess anonymisation against three risks: singling out an individual, linking records relating to the same individual, and inferring information about an individual. If any of these remains reasonably possible, taking into account cost, time and available technology, the data is at most pseudonymised and remains personal data.

In clinical research, coded trial data held by a sponsor is almost never anonymous, because the investigator holds the key. Genuine anonymisation is generally achievable only for aggregated results or for datasets processed with techniques such as k-anonymity, generalisation or differential privacy, and even then the assessment should be documented. Anonymisation is itself a processing operation requiring a legal basis. Compare de-identification under HIPAA, which uses a different, prescriptive standard.

What should a sponsor check before calling a dataset anonymous?

Check whether people remain identifiable through the dataset itself or information reasonably likely to be combined with it. Removing names alone is insufficient. For a trial dataset, examine unusual diagnoses, dates, small groups and available linkage information, then document the assessment and release controls. Reassess the conclusion if the surrounding identification risks change.