XVIII International AIDS Conference


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Multidimensional risk factors model to improve precision to identify HIV in antenatal care and emergency settings in a reference hospital in Guatemala

B. Hernandez1, C. Mejia2, G. Villatoro2, J.L. De Leon3, P. Rivera Scott4

1UNAIDS, Guatemala, Guatemala, 2Hospital Roosevelt, Clinica de Enfermedades Infecciosas, Guatemala, Guatemala, 3Fundacion HOREB, Guatemala, Guatemala, 4UNAIDS, M&E, Guatemala, Guatemala

Mother-to-child transmission is a pivotal strategy in many countries since 1998. Cost-benefit of this interventions is not questionable when there are sufficient funds for the AIDS response. However, when resources are limited, particularly in low and mid-income countries with concentrated epidemics and limited access to health services, strategies to optimize the use of available resources are critical.
The Guatemalan Universal Access report states that in 2008, 103,000 (26%) of 400,000 pregnant women were tested, of whom 516 were identified as HIV positive (7% of the 6,592 pregnant women estimated to be HIV positive).
To increase identification of HIV positive pregnant women, this retrospective, corelational study was carried out to develop a multi-dimensional model that would identify the common variables associated with HIV, thus providing inputs to better target this population at VCT service delivery points.
Information was collected from data bases of women that received VCT during 2002 - 2009 in ambulatory services and emergency of the Roosevelt Hospital. Confidentiality was taken into consideration by eliminating all personal information. A bi-variated , co-linear and logistic regression analysis was made to select associated variables.
The multivariate proposed risk model for pregnant women identifies the following tracking variables: age older than 22 years old, race LADINO, single, with no schooling, no religion and no pre-natal care. The possibility to be HIV positive when these variables are present increases in 67%.
Active search of pregnant women with this characteristics would increase targeting capacity to identify HIV positive pregnant women and thus, make better use of available resources.

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