National Network for Rare Diseases in Brazil: The Computational Infrastructure and Preliminary Results

Yamada, Diego Bettiol; Bernardi, Filipe Andrade; Filho, Márcio Eloi Colombo; Neiva, Mariane Barros; Lima, Vinícius; Vinci, André Luiz Teixeira; Oliveira, Bibiana Mello de; Félix, Têmis Maria; Alves, Domingos

doi:10.1007/978-3-031-08757-8_4

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…In Brazil, the Brazilian Policy for Comprehensive Care for People with Rare Diseases of the Ministry of Health of Brazil established reference services in RD that offer preventive, diagnostic, and therapeutic actions for people with rare conditions [13]. There is a need for human, technological, and infrastructure resources in the Brazilian Unified Health System, which leads to management and communication problems such as difficulties in transmitting information between the services that comprise the health network, leading to different processes and costs across the public system [14].…”

Section: Introductionmentioning

confidence: 99%

The Minimum Data Set for Rare Diseases: Systematic Review

Bernardi¹,

Oliveira²,

Yamada³

et al. 2023

J Med Internet Res

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Background The minimum data set (MDS) is a collection of data elements to be grouped using a standard approach to allow the use of data for clinical and research purposes. Health data are typically voluminous, complex, and sometimes too ambiguous to generate indicators that can provide knowledge and information on health. This complexity extends further to the rare disease (RD) domain. MDSs are essential for health surveillance as they help provide services and generate recommended population indicators. There is a bottleneck in international literature that reveals a global problem with data collection, recording, and structuring in RD. Objective This study aimed to identify and analyze the MDSs used for RD in health care networks worldwide and compare them with World Health Organization (WHO) guidelines. Methods The population, concept, and context methodology proposed by the Joanna Briggs Institute was used to define the research question of this systematic review. A total of 4 databases were reviewed, and all the processes were reported using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. The data elements were analyzed, extracted, and organized into 10 categories according to WHO digital health guidelines. The quality assessment used the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist. Results We included 20 studies in our review, 70% (n=14) of which focused on a specific health domain and 30% (n=6) of which referred to RD in general. WHO recommends that health systems and networks use standard terminology to exchange data, information, knowledge, and intelligence in health. However, there was a lack of terminological standardization of the concepts in MDSs. Moreover, the selected studies did not follow the same standard structure for classifying the data from their MDSs. All studies presented MDSs with limitations or restrictions because they covered only a specific RD, or their scope of application was restricted to a specific context or geographic region. Data science methods and clinical experience were used to design, structure, and recommend a fundamental global MDS for RD patient records in health care networks. Conclusions Our study highlights the difficulties in standardizing and categorizing findings from MDSs for RD because of the varying structures used in different studies. The fundamental RD MDS designed in this study comprehensively covers the data needs in the clinical and management sectors. These results can help public policy makers support other aspects of their policies. We highlight the potential of our results to help strategic decisions related to RD. Trial Registration PROSPERO CRD42021221593; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221593 International Registered Report Identifier (IRRID) RR2-10.1016/j.procs.2021.12.034

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Section: Introductionmentioning

confidence: 99%

The Minimum Data Set for Rare Diseases: Systematic Review

Bernardi¹,

Oliveira²,

Yamada³

et al. 2023

J Med Internet Res

Self Cite

View full text Add to dashboard Cite

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Supervised Machine Learning Techniques Applied to Medical Records Toward the Diagnosis of Rare Autoimmune Diseases

Martins

Filho

Mioto

et al. 2023

Lecture Notes in Computer Science

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Epidemiological characterization of rare diseases in Brazil: A retrospective study of the Brazilian Rare Diseases Network

de Oliveira,

Bernardi,

Baiochi

et al. 2024

Orphanet J Rare Dis

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Background The Brazilian Policy for Comprehensive Care for People with Rare Diseases was implemented in 2014; however, national epidemiological data on rare diseases (RDs) are scarce and mainly focused on specific disorders. To address this gap, University Hospitals, Reference Services for Neonatal Screening, and Reference Services for Rare Diseases, all of which are public health institutions, established the Brazilian Rare Diseases Network (RARAS) in 2020. The objective of this study was to perform a comprehensive nationwide epidemiological investigation of individuals with RDs in Brazil. This retrospective survey collected data from patients receiving care in 34 healthcare facilities affiliated with RARAS in 2018 and 2019. Results The survey included 12,530 participants with a median age of 15.0 years, with women representing 50.5% of the cohort. Classification according to skin color demonstrated that 5044 (47.4%) participants were admixed. Most had a confirmed diagnosis (63.2%), with a predominance of phenylketonuria (PKU), cystic fibrosis (CF), and acromegaly. Common clinical manifestations included global developmental delay and seizures. The average duration of the diagnostic odyssey was 5.4 years (± 7.9 years). Among the confirmed diagnoses, 52.2% were etiological (biochemical: 42.5%; molecular: 30.9%), while 47.8% were clinical. Prenatal diagnoses accounted for 1.2%. Familial recurrence and consanguinity rates were 21.6% and 6.4%, respectively. Mainstay treatments included drug therapy (55.0%) and rehabilitation (15.6%). The Public Health System funded most diagnoses (84.2%) and treatments (86.7%). Hospitalizations were reported in 44.5% of cases, and the mortality rate was 1.5%, primarily due to motor neuron disease and CF. Conclusion This study marks a pioneering national-level data collection effort for rare diseases in Brazil, offering novel insights to advance the understanding, management, and resource allocation for RDs. It unveils an average diagnostic odyssey of 5.4 years and a higher prevalence of PKU and CF, possibly associated with the specialized services network, which included newborn screening services.

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National Network for Rare Diseases in Brazil: The Computational Infrastructure and Preliminary Results

Cited by 4 publications

References 9 publications

The Minimum Data Set for Rare Diseases: Systematic Review

The Minimum Data Set for Rare Diseases: Systematic Review

Supervised Machine Learning Techniques Applied to Medical Records Toward the Diagnosis of Rare Autoimmune Diseases

Epidemiological characterization of rare diseases in Brazil: A retrospective study of the Brazilian Rare Diseases Network

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