Literature DB >> 15737969

HIV-1 subtype E progression among northern Thai couples: traditional and non-traditional predictors of survival.

C Costello1, K E Nelson, V Suriyanon, S Sennun, S Tovanabutra, C M Heilig, S Shiboski, D J Jamieson, V Robison, K Rungruenthanakit, A Duerr.   

Abstract

BACKGROUND: In the continuing effort to introduce antiretroviral therapy in resource-limited settings, there is a need to understand differences between natural history of HIV in different populations and to identify feasible clinical measures predictive of survival.
METHODS: We examined predictors of survival among 836 heterosexuals who were infected with HIV subtype CRF01_AE in Thailand.
RESULTS: From 1993 to 1999, 269 (49.4%) men and 65 (25.7%) women died. The median time from the estimated seroconversion to death was 7.8 years (95% confidence interval 7.0-9.1). Men and women with enrolment CD4 counts <200 cells/microl had about 2 and 11 times greater risk of death than those with CD4 counts of 200-500 and >500, respectively. Measurements available in resource-limited settings, including total lymphocyte count (TLC), anaemia, and low body mass index (BMI), also predicted survival. Men with two or more of these predictors had a median survival of 0.8 (0.5-1.8) years, compared with 2.7 (1.9-3.3) years for one predictor and 4.9 (4.1-5.2) years for no predictors.
CONCLUSIONS: The time from HIV infection to death appears shorter among this Thai population than among antiretroviral naive Western populations. CD4 count and viral load (VL) were strong, independent predictors of survival. When CD4 count and VL are unavailable, individuals at high risk for shortened HIV survival may be identified by a combination of low TLC, anaemia, and low BMI. This combination of accessible clinical measures of the disease stage may be useful for medical management in resource-limited settings.

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Year:  2005        PMID: 15737969     DOI: 10.1093/ije/dyi023

Source DB:  PubMed          Journal:  Int J Epidemiol        ISSN: 0300-5771            Impact factor:   7.196


  15 in total

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10.  Estimating 'net' HIV-related mortality and the importance of background mortality rates.

Authors:  Milly Marston; Jim Todd; Judith R Glynn; Kenrad E Nelson; Ram Rangsin; Tom Lutalo; Mark Urassa; Sam Biraro; Lieve Van der Paal; Pam Sonnenberg; Basia Zaba
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