Rare diseases, an emerging global public health priority, require an evidence-based estimate of the global point prevalence to inform public policy. We used the publicly available epidemiological data in the Orphanet database to calculate such a prevalence estimate. Overall, Orphanet contains information on 6172 unique rare diseases; 71.9% of which are genetic and 69.9% which are exclusively pediatric onset. Global point prevalence was calculated using rare disease prevalence data for predefined geographic regions from the ‘Orphanet Epidemiological file’ (http://www.orphadata.org/cgi-bin/epidemio.html). Of the 5304 diseases defined by point prevalence, 84.5% of those analysed have a point prevalence of <1/1 000 000. However 77.3–80.7% of the population burden of rare diseases is attributable to the 4.2% (n = 149) diseases in the most common prevalence range (1–5 per 10 000). Consequently national definitions of ‘Rare Diseases’ (ranging from prevalence of 5 to 80 per 100 000) represent a variable number of rare disease patients despite sharing the majority of rare disease in their scope. Our analysis yields a conservative, evidence-based estimate for the population prevalence of rare diseases of 3.5–5.9%, which equates to 263–446 million persons affected globally at any point in time. This figure is derived from data from 67.6% of the prevalent rare diseases; using the European definition of 5 per 10 000; and excluding rare cancers, infectious diseases, and poisonings. Future registry research and the implementation of rare disease codification in healthcare systems will further refine the estimates.

The Human Phenotype Ontology (HPO)—a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases—is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO’s interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

Rare disorders are scarcely represented in international classifications and therefore invisible in information systems. One of the major needs in health information systems and for research is to share and/or to integrate data coming from heterogeneous sources with diverse reference terminologies. ORPHANET (www.orpha.net) is a multilingual information portal on rare diseases and orphan drugs. Orphanet information system is supported by a relational database built around the concept of rare disorders. Representation of rare diseases in Orphanet encompasses levels of increasing complexity: lexical (multilingual terminology), nosological (multihierarchical classifications), relational (annotations—epidemiological data—and classes of objects—genes, manifestations, and orphan drugs—integrated in a relational database), and interoperational (semantic interoperability). Rare disorders are mapped to International Classification of Diseases (10th version), SNOMED CT, MeSH, MedDRA, and UMLS. Genes are cross‐referenced with HGNC, UniProt, OMIM, and Genatlas. A suite of tools allow for extraction of massive datasets giving different views that can be used in bioinformatics to answer complex questions, intended to serve the needs of researchers and the pharmaceutical industry in developing medicinal products for rare diseases. An ontology is under development. The Orphanet nomenclature is at the crossroads of scientific data repositories and of clinical terminology standards, and is suitable to be used as a standard terminology. Hum Mutat 33:803–808, 2012.

Randomised clinical trials are key to advancing medical knowledge and to enhancing patient care, but major barriers to their conduct exist. The present paper presents some of these barriers. We performed systematic literature searches and internal European Clinical Research Infrastructure Network (ECRIN) communications during face-to-face meetings and telephone conferences from 2013 to 2017 within the context of the ECRIN Integrating Activity (ECRIN-IA) project. The following barriers to randomised clinical trials were identified: inadequate knowledge of clinical research and trial methodology; lack of funding; excessive monitoring; restrictive privacy law and lack of transparency; complex regulatory requirements; and inadequate infrastructures. There is a need for more pragmatic randomised clinical trials conducted with low risks of systematic and random errors, and multinational cooperation is essential. The present paper presents major barriers to randomised clinical trials. It also underlines the value of using a pan-European-distributed infrastructure to help investigators overcome barriers for multi-country trials in any disease area.

Because of their individual rarity, genetic diseases and other types of rare diseases are under-represented in healthcare coding systems; this contributes to a lack of ascertainment and recognition of their importance for healthcare planning and resource allocation, and prevents clinical research from being performed. Orphanet was given the task to develop an inventory of rare diseases and a classification system which could serve as a template to update International terminologies. When the World Health Organization (WHO) launched the revision process of the International Classification of Diseases (ICD), a Topic Advisory Group for rare diseases was established, managed by Orphanet and funded by the European Commission. So far 5,400 rare diseases listed in the Orphanet database have an endorsed representation in the foundation layer of ICD-11, and are thus provided with a unique identifier in the Beta version of ICD-11, which is 10 times more than in ICD10. A rare disease linearization is also planned. The current beta version is open for public consultation and comments, and to be used for field testing. The adoption by the World Health Assembly is planned for 2017. The overall revision process was carried out with very limited means considering its scope, ambition and strategic significance, and experienced significant hurdles and setbacks. The lack of funding impacted the level of professionalism that could be attained. The contrast between the initially declared goals and the currently foreseen final product is disappointing. In the context of uncertainty around the outcome of the field testing and the potential willingness of countries to adopt this new version, the European Commission Expert Group on Rare Diseases adopted in November 2014 a recommendation for health care coding systems to consider using ORPHA codes in addition to ICD10 codes for rare diseases having no specific ICD10 codes. The Orphanet terminology, classifications and mappings with other terminologies are freely available at www.orphadata.org .