ABSTRACT OBJECTIVE To investigate the HCV cascade of care and to identify the factors associated with loss or absence to follow-up of patients identified as infected with hepatitis C through blood donation. METHODS Blood donors from 1994 to 2012, identified with positive anti- HCV by enzyme immunoassay and immunoblot tests were invited to participate in the study, through letters or phone calls. Patients who agreed to participate were interviewed and their blood samples were collected for further testing. The following variables were investigated: demographic data, data on comorbidities and history concerning monitoring of hepatitis C. Multiple regression analysis by Poisson regression model was used to investigate the factors associated with non-referral for consultation or loss of follow-up. RESULTS Of the 2,952 HCV-infected blood donors, 22.8% agreed to participate: 394 (58.2%) male, median age 48 years old and 364 (53.8%) Caucasian. Of the 676 participants, 39.7% did not receive proper follow-up or treatment after diagnosis: 45 patients referred not to be aware they were infected, 61 did not seek medical attention and 163 started a follow-up program, but were non-adherent. The main reasons for inadequate follow-up were not understanding the need for medical care (71%) and health care access difficulties (14%). The variables showing a significant association with inadequate follow-up after multiple regression analysis were male gender (PR = 1.40; 95%CI 1.15–1.71), age under or equal to 50 years (PR = 1.36; 95%CI 1.12–1.65) and non-Caucasians (PR = 1.53; 95%CI 1.27–1.84). CONCLUSIONS About 40.0% of patients did not receive appropriate follow-up. These data reinforce the need to establish strong links between primary care and reference centers and the need to improve access to specialists and treatments.

ABSTRACT OBJECTIVE To investigate the HCV cascade of care and to identify the factors associated with loss or absence to follow-up of patients identified as infected with hepatitis C through blood donation. METHODS Blood donors from 1994 to 2012, identified with positive anti- HCV by enzyme immunoassay and immunoblot tests were invited to participate in the study, through letters or phone calls. Patients who agreed to participate were interviewed and their blood samples were collected for further testing. The following variables were investigated: demographic data, data on comorbidities and history concerning monitoring of hepatitis C. Multiple regression analysis by Poisson regression model was used to investigate the factors associated with non-referral for consultation or loss of follow-up. RESULTS Of the 2,952 HCV-infected blood donors, 22.8% agreed to participate: 394 (58.2%) male, median age 48 years old and 364 (53.8%) Caucasian. Of the 676 participants, 39.7% did not receive proper follow-up or treatment after diagnosis: 45 patients referred not to be aware they were infected, 61 did not seek medical attention and 163 started a follow-up program, but were non-adherent. The main reasons for inadequate follow-up were not understanding the need for medical care (71%) and health care access difficulties (14%). The variables showing a significant association with inadequate follow-up after multiple regression analysis were male gender (PR = 1.40; 95%CI 1.15–1.71), age under or equal to 50 years (PR = 1.36; 95%CI 1.12–1.65) and non-Caucasians (PR = 1.53; 95%CI 1.27–1.84). CONCLUSIONS About 40.0% of patients did not receive appropriate follow-up. These data reinforce the need to establish strong links between primary care and reference centers and the need to improve access to specialists and treatments.

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.

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.

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.

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.

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.

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.