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OBJECTIVES: To test and characterize the dependence of viral load on gender in different countries and racial groups as a function of CD4 T-cell count. METHODS: Plasma viral load data were analysed for > 30,000 HIV-infected patients attending clinics in the USA [HIV Insight (Cerner Corporation, Vienna, VA, USA) and Plum Data Mining LLC (East Meadow, NY, USA) databases] and the Netherlands (Athena database; HIV Monitoring Foundation, Amsterdam, Netherlands). Log-normal regression models were used to test for an effect of gender on viral load while adjusting for covariates and allowing the effect to depend on CD4 T-cell count. Sensitivity analyses were performed to test the robustness of conclusions to assumptions regarding viral loads below the lower limit of quantification (LLOQ). RESULTS: After adjusting for covariates, women had (nonsignificantly) lower viral loads than men (HIV Insight: -0.053 log(10) HIV-1 RNA copies/mL, P = 0.202; Athena: -0.005 log(10) copies/mL, P = 0.667; Plum: -0.072 log(10) copies/mL, P = 0.273). However, further investigation revealed that the gender effect depended on CD4 T-cell count. Women had consistently higher viral loads than men when CD4 T-cell counts were at most 50 cells/microL, and consistently lower viral loads than men when CD4 T-cell counts were greater than 350 cells/microL. These effects were remarkably consistent when estimated independently for the racial groups with sufficient data available in the HIV Insight and Plum databases. CONCLUSIONS: The consistent relationship between gender-related differences in viral load and CD4 T-cell count demonstrated here explains the diverse findings previously published.

Original publication

DOI

10.1111/j.1468-1293.2005.00285.x

Type

Journal article

Journal

HIV medicine

Publication Date

05/2005

Volume

6

Pages

170 - 178

Addresses

Department of Infectious Disease Epidemiology, Faculty of Medicine, Imperial College London, London, UK. c.donnelly@imperial.ac.uk

Keywords

Humans, HIV-1, HIV Infections, CD4 Lymphocyte Count, Viral Load, Data Collection, Regression Analysis, Statistics, Nonparametric, Sex Distribution, Databases, Factual, Adult, United States, Female, Male