Building the National Database of Health Centred on the Individual: Adminis-trative and Epidemiological Record Linkage - Brazil, 2000-2015

• Autor(a) principal: Augusto Afonso Guerra Júnior
• Data de Publicação: 2018
• Outros Autores: Ramon Gonçalves Pereira, Eli Iola Gurgel Andrade, Mariangela Leal Cherchiglia, Leonardo Vinícius Dias da Silva, Juliano Ávila, Núbia Santos, Afonso Reis, Francisco de Assis Acurcio, Wagner Meira Junior
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFMG
• Texto Completo: https://doi.org/10.23889/ijpds.v3i1.446; http://hdl.handle.net/1843/50902; https://orcid.org/0000-0001-5256-0577; https://orcid.org/0000-0002-0206-2462; https://orcid.org/0000-0001-5622-567X; https://orcid.org/0000-0003-1956-5100; https://orcid.org/0000-0002-5880-5261; https://orcid.org/0000-0002-2614-2723
• topic: Data linkage; Record linkage; Brazilian health database; SUS deduplication; Sistema Único de Saúde; Banco de dados - Saúde; Medicina - Processamento de dados

Objective: To describe the methods and results of parameter setting that are needed to execute the probabilistic deduplication of large administrative and epidemiological databases in Brazil and to create a National Health Database Centred on the individual. Methods: This paper shows the results of a record linkage model to integrate data from SIH, SIA, SIM, and SINAN, which have different formats and attributes between them and over time. These data consistof 1.3 billion records from 2000-2015. Probabilistic and deterministic record linkages were used to deduplicate these data. The Kappa statistic and clerical review were used to ensure the quality ofthe linkage. The graph algorithm and depth-first search were used to generate the identifiers. Results: The deterministic deduplication process resulted in a database with 403,113,527 possible unique individuals. After the probabilistic deduplication process of the former database was performed,159,703,805 unique individuals were identified. This result had an estimated a false positive error rate of 3.3%, and the false negative error was estimated at 12.3%. Conclusions: The National Health Database centred on the individual was generated and will allow researchers to use real-world evidence to conduct clinical, epidemiological, economic and other studies. This database represents a significant cohort, spanning 15 years of historical data and preserving patient privacy. The success of the process described will allow repeating and appending the data for future years and enable important studies to promote SUS efficiency and provide better treatments for patients.