Logistic model for determining factors associated with HIV infection among blood donor candidates at the Fundação HEMOPE

• Autor(a) principal: Queiroz,Niedja Maristone Barreto
• Data de Publicação: 2012
• Outros Autores: Sampaio,Divaldo de Almeida, Santos,Eufrázio de Souza, Bezerra,Ana Cristina de Souza
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista brasileira de hematologia e hemoterapia (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-84842012000300014
• topic: HIV Infections; Blood donation; Risk factors; Logistic models

OBJECTIVE: To determine factors associated with HIV infection in blood donor candidates in Recife, Brazil. METHODS:A transversal study was performed of 106,203 blood donor candidates found eligible by the routine clinical screening process in the blood bank in Recife of the Fundação de Hematologia e Hemoterapia de Pernambuco (HEMOPE) in the period from January 1998 to November 2003. Additional indirect immune and western blot confirmation tests for HIV infection were performed and the candidates were classified as HIV positive or negative. The Chi-squared test and stepwise multiple logistic regression were conducted to examine any association between HIV infection and age, gender, place of residence, schooling, number of donations and serological tests for core hepatitis B antigen virus (anti-HBc), the hepatitis C antivirus (anti-HCV), human T-type antivirus lymph cells (anti-HTLV 1 and 2), serological tests for syphilis (VDRL) and the hepatitis B virus surface antigen (HBsAg). RESULTS: In the observed sample, 0.204% of blood donor candidates were found to be HIV positive. Among the studied variables, the age, education level, residency, donation type, and serologic status for anti-HBc and VDRL tests were found to be associated with HIV infection. CONCLUSION: The younger, spontaneous donor candidates living in the Recife metropolitan area with a low level of education and positive for anti-HBc and VDRL have an higher risk of HIV infection than the other candidates. Data such as these are useful to understand the dynamics of infection and to guide healthcare policies.