Skip Navigation LinksHome > August 15, 2013 - Volume 63 - Issue 5 > HIV-1 Disease Progression During Highly Active Antiretrovira...
JAIDS Journal of Acquired Immune Deficiency Syndromes:
doi: 10.1097/QAI.0b013e3182976891
Epidemiology and Prevention

HIV-1 Disease Progression During Highly Active Antiretroviral Therapy: An Application Using Population-Level Data in British Columbia: 1996–2011

Nosyk, Bohdan PhD*,†; Min, Jeong MSc*; Lima, Viviane D. PhD*,‡; Yip, Benita BSc*; Hogg, Robert S. PhD*,†; Montaner, Julio S. G. MD, FRCPC*,‡; on behalf of the STOP HIVAIDS Study Group

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Background: Accurately estimating rates of disease progression is of central importance in developing mathematical models used to project outcomes and guide resource allocation decisions. Our objective was to specify a multivariate regression model to estimate changes in disease progression among individuals on highly active antiretroviral treatment in British Columbia, Canada, 1996–2011.

Methods: We used population-level data on disease progression and antiretroviral treatment utilization from the BC HIV Drug Treatment Program. Disease progression was captured using longitudinal CD4 and plasma viral load testing data, linked with data on antiretroviral treatment. The study outcome was categorized into (CD4 count ≥ 500, 500–350, 350–200, <200 cells/mm3, and mortality). A 5-state continuous-time Markov model was used to estimate covariate-specific probabilities of CD4 progression, focusing on temporal changes during the study period.

Results: A total of 210,083 CD4 measurements among 7421 individuals with HIV/AIDS were included in the study. Results of the multivariate model suggested that current highly active antiretroviral treatment at baseline, lower baseline CD4 (<200 cells/mm3), and extended durations of elevated plasma viral load were each associated with accelerated progression. Immunological improvement was accelerated significantly from 2004 onward, with 23% and 46% increases in the probability of CD4 improvement from the fourth CD4 stratum (CD4 < 200) in 2004–2008 and 2008–2011, respectively.

Conclusion: Our results demonstrate the impact of innovations in antiretroviral treatment and treatment delivery at the population level. These results can be used to estimate a transition probability matrix flexible to changes in the observed mix of clients in different clinical stages and treatment regimens over time.

© 2013 by Lippincott Williams & Wilkins


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