The Brazilian Policy of Comprehensive Care for People with Rare Diseases (BPCCPRD) was established by the Ministry of Health to reduce morbidity and mortality and improve the quality of life of people with rare diseases (RD). Several laboratory tests, most using molecular genetic technologies, have been incorporated by the Brazilian Public Health System, and 18 specialised centres have so far been established at university hospitals (UH) in the capitals of the Southern, Southeastern and Northeastern regions. However, whether the available human and technological resources in these services are appropriate and sufcient to achieve the goals of care established by the BPCCPRD is unknown. Despite great advances in diagnosis, especially due to new technologies and the recent structuring of clinical assessment of RD in Brazil, epidemiological data are lacking and when available, restricted to specifc disorders. This position paper summarises the performance of a nationally representative survey on epidemiology, clinical status, and diagnostic and therapeutic resources employed for individuals with genetic and non-genetic RD in Brazil. The Brazilian Rare Disease Network (BRDN) is under development, comprising 40 institutions, including 18 UH, 17 Rare Diseases Reference Services and fve Newborn Screening Reference Services. A retrospective study will be initially conducted, followed by a prospective study. The data collection instrument will use a standard protocol with sociodemographic data and clinical and diagnostic aspects according to international ontology. This great collaborative network is the frst initiative of a large epidemiological data collection of RD in Latin America, and the results will increase the knowledge of RD in Brazil and help health managers to improve national public policy on RD in Brazil.

Abstract Background: A rare disease is a medical condition with low prevalence in the general population, but these can collectively affect up to 10% of the population. Thus, rare diseases have a significant impact on the health care system, and health professionals must be familiar with their diagnosis, management, and treatment. Objective: This paper aims to provide health indicators regarding the rare diseases in Brazil and to create a network of reference centers with health professionals from different regions of the country. RARASnet proposes to map, analyze, and communicate all the data regarding the infrastructure of the centers and the patients' progress or needs. The focus of the proposed study is to provide all the technical infrastructure and analysis, following the World Health Organization and the Brazilian Ministry of Health guidelines. Methods: To build this digitized system, we will provide a security framework to assure the privacy and protection of each patient when collecting data. Systems development life cycle methodologies will also be applied to align software development, infrastructure operation, and quality assurance. After data collection of all information designed by the specialists, the computational analysis, modeling, and results will be communicated in scientific research papers and a digital health observatory. Results: The project has several activities, and it is in an initial stage. Initially, a survey was given to all health care centers to understand the technical aspects of each network member, such as the existence of computers, technical support staff, and digitized systems. In this survey, we detected that 59% (23/39) of participating health units have electronic medical records, while 41% (16/39) have paper records. Therefore, we will have different strategies to access the data from each center in the data collection phase. Later, we will standardize and analyze the clinical and epidemiological data and use these data to develop a national network for monitoring rare diseases and a digital health observatory to make the information available. The project had its financing approved in December 2019. Retrospective data collection started in October 2020, and we expect to finish in January 2021. During the third quarter of 2020, we enrolled 40 health institutions from all regions of Brazil. Conclusions: The nature of rare disease diagnosis is complex and diverse, and many problems will be faced in the evolution of the project. However, decisions based on data analysis are the best option for the improvement of the rare disease network in Brazil. The creation of RARASnet, along with all the digitized infrastructure, can improve the accessibility of information and standardization of rare diseases in the country.

Introdução: As Doenças Raras (DR) acometem até 65 pessoas a cada 100.000 habitantes, segundo a Organização Mundial de Saúde. Apesar de individualmente raras, integram aproximadamente sete mil doenças. No Brasil, são cerca de 13 milhões de pacientes que precisam de atenção e tratamento adequados, em geral providos pelo Sistema Único de Saúde. Desde a instituição da Política Nacional de Atenção Integral às Pessoas com DR (portaria 199/2014), o Ministério da Saúde (MS) tem investido em políticas que uniformizam o atendimento, monitoramento e tratamento destes pacientes. De acordo com esta portaria, as DR são classificadas de acordo com o CID-10 (Classificação Internacional de Doenças). A Rede Nacional de Doenças Raras identificou que a classificação utilizada agrupa muitas doenças no mesmo código (CID), dificultando a classificação e o registro de DR. Objetivos: Comparar o uso do CID-10 ao código ORPHA, uma ontologia desenvolvida para a classificação de DR. Metodologia: Utilizou-se a lista de CID-10 referentes a DR determinada pelo MS e foi exportado do Orphanet a relação de doenças com CID-10 relacionados. Por meio de um cruzamento de dados via linguagem Python, determinou-se a relação de ORPHA e CID-10. Resultados: A lista de DR fornecida pelo MS conta com 270 CID-10, sendo que 113 (41,85%) não apresentam código ORPHA e apenas 40 (14,81%) estão associados a este código. Com o cruzamento de dados, observa-se que, apesar das classificações serem feitas pelo CID-10 no MS, o número ORPHA classifica as doenças com mais detalhes e de forma mais compartimentada, já que 117 DR (43,33%) estão associadas a 2600 DR no código ORPHA. Como exemplo, o CID Q87.8 [outras síndromes com malformações congênitas (MC) não especificadas] está relacionado a 422 doenças no código ORPHA; e o CID Q87.0 (síndromes com MC afetando predominantemente o aspecto da face) está associado a 131 códigos ORPHA. Conclusões: O CID é um identificador generalista devido a sua natureza mono-hierárquica. Desta forma, a classificação utilizada pelo MS para DR dificulta o reconhecimento correto das mesmas, já que muitas ficam agrupadas em um único código. É importante que as DR recebam uma classificação adequada, pois tais indicadores poderão contribuir para o reconhecimento da epidemiologia e determinar a alocação de recursos humanos, terapêuticos e diagnósticos no campo das DR.