Accuracy of a probabilistic record-linkage methodology used to track blood donors in the Mortality Information System database / Acurácia da metodologia de relacionamento probabilístico de registros na identificação de doadores de sangue no banco de dados do Sistema de Informações sobre Mortalidade / La exactitud del método probabilístico de relación para identificar a donantes de sangre en la base de datos del Sistema de Información sobre Mortalidad

The probabilistic record linkage (PRL) is based on a likelihood score that measures the degree of similarity of several matching variables. Screening test results for different diseases are available for the blood donor population. In this paper, we describe the accuracy of a PRL process used to track blood donors from the Fundação Pró-Sangue (FPS) in the Mortality Information System (SIM), in order that future studies might determine the blood donor’s cause of death. The databases used for linkage were SIM and the database made up of individuals that were living (200 blood donors in 2007) and dead (196 from the Hospital das Clinicas de São Paulo that died in 2001-2005). The method consists of cleaning and linking the databases using three blocking steps comparing the variables “Name/Mother’s Name/ Date of Birth” to determine a cut-off score. For a cut-off score of 7.06, the sensitivity and specificity of the method is 94.4% (95%CI: 90.0-97.0) and 100% (95%CI: 98.0-100.0), respectively. This method can be used in studies that aim to track blood donors from the FPS database in SIM.

O relacionamento probabilístico se baseia em um escore que é calculado levando em consideração a similaridade do pareamento de diversas variáveis. Dados de resultados de testes de triagem para diferentes doenças estão disponíveis para a população de doadores de sangue. Neste artigo descrevemos a acurácia de um processo de relacionamento probabilístico para identificar doadores de sangue da Fundação Pró-Sangue (FPS) no Sistema de Informações sobre Mortalidade (SIM). Os bancos utilizados para o relacionamento foram o SIM e o banco formado por indivíduos vivos (200 doadores de sangue em 2007) e mortos (196 pacientes do Hospital das Clínicas de São Paulo que morreram entre 2001-2005). O método consistiu em limpar e relacionar probabilísticamente os bancos em três passos de blocagem comparando as variáveis “Nome/Nome Mãe /Data de Nascimento” para determinar um escore de corte. Para um escore de corte de 7,06 a sensibilidade e especificidade do método é de 94,4% (IC95%: 90-97) e 100% (IC95%: 98-100), respectivamente. Este método pode ser utilizado em estudos para identificar pacientes da FPS no SIM.

La relación probabilística (RP) se basa en una puntuación que se calcula en función de la similitud entre variables de emparejamiento. Los resultados de los tests sobre diferentes enfermedades están a disposición de la población de donantes de sangre. En el presente artículo se describe la precisión de un proceso de RP para identificar a donantes de sangre de la Fundação Pró-Sangue (FPS) en el Sistema de Información de Mortalidad (SIM). Se llevó a cabo la RP del SIM y de un banco compuesto por individuos vivos (200 donantes de sangre en 2007) y muertos (196 pacientes del Hospital de Clínicas de São Paulo, que murieron entre 2001 y 2005). El método consistió en depurar los bancos de datos y RP en tres etapas de bloqueo, comparando las variables nombre, nombre de la madre y fecha de nacimiento para determinar un punto de corte. Para el punto de corte 7:06, la especificidad y sensibilidad del método fue de un 94,4% (IC95%: 90,0-97,0) y 100% (IC95%: 98,0-100,0), respectivamente. Este método puede ser utilizado en más estudios con el fin de identificar a los pacientes FPS en el SIM.

Contribution of the Retrovirus Epidemiology Donor Study (REDS) to research on blood transfusion safety in Brazil

The Retrovirus Epidemiology Donor Study (REDS) program was established in the United States in 1989 with the purpose of increasing blood transfusion safety in the context of the HIV/AIDS and human T-lymphotropic virus epidemics. REDS and its successor, REDS-II were at first conducted in the US, then expanded in 2006 to include international partnerships with Brazil and China. In 2011, a third wave of REDS renamed the Recipient Epidemiology and Donor Evaluation Study-III (REDS-III) was launched. This seven-year research program focuses on both blood banking and transfusion medicine research in the United States of America, Brazil, China, and South Africa. The main goal of the international programs is to reduce and prevent the transmission of HIV/AIDS and other known and emerging infectious agents through transfusion, and to address research questions aimed at understanding global issues related to the availability of safe blood. This article describes the contribution of REDS-II to transfusion safety in Brazil. Articles published from 2010 to 2013 are summarized, including database analyses to characterize blood donors, deferral rates, and prevalence, incidence and residual risk of the main blood-borne infections. Specific studies were developed to understand donor motivation, the impact of the deferral questions, risk factors and molecular surveillance among HIV-positive donors, and the natural history of Chagas disease. The purpose of this review is to disseminate the acquired knowledge and briefly summarize the findings of the REDS-II studies conducted in Brazil as well as to introduce the scope of the REDS-III program that is now in progress and will continue through 2018.

GenFlow: Generic flow for integration, management and analysis of molecular biology data

A large number of DNA sequencing projects all over the world have yielded a fantastic amount of data, whose analysis is, currently, a big challenge for computational biology. The limiting step in this task in the integration of large volumes of data stored in highly heterogeneous repositories of genomic and cDNA sequences, as well as gene expression results. Solving this problem requires automated analytical tools to optimize operations and efficiently generate knowledge. This paper presents an generic flow model, called GenFlow, that can tackle this analytical task.