Viral load monitoring of antiretroviral therapy, cohort viral load and HIV transmission in Southern Africa: a mathematical modelling analysis

Abstract

Objectives: In low-income settings, treatment failure is often identified using CD4 cell count monitoring. Consequently, patients remain on a failing regimen, resulting in a higher risk of transmission. We investigated the benefit of routine viral load monitoring for reducing HIV transmission.

Design: Mathematical model.

Methods: We developed a stochastic mathematical model representing the course of individual viral load, immunological response and survival in a cohort of 1000 HIV-infected patients receiving antiretroviral therapy (ART) in southern Africa. We calculated cohort viral load (CVL; sum of individual viral loads) and used a mathematical relationship between individual viral load values and transmission probability to estimate the number of new HIV infections. Our model was parameterized with data from the International epidemiologic Databases to Evaluate AIDS Southern African collaboration. Sensitivity analyses were performed to assess the validity of the results in a universal 'test and treat' scenario, wherein patients start ART earlier after HIV infection.

Results: If CD4 cell count alone was regularly monitored, the CVL was 2.6 × 10 copies/ml and the treated patients transmitted on average 6.3 infections each year. With routine viral load monitoring, both CVL and transmissions were reduced by 31% to 1.7 × 10 copies/ml and 4.3 transmissions, respectively. The relative reduction of 31% between monitoring strategies remained similar for different scenarios.

Conclusion: Although routine viral load monitoring enhances the preventive effect of ART, the provision of ART to everyone in need should remain the highest priority.

Figure 1. Flow chart showing possible transitions between risk stages after start of antiretroviral therapy (ART)

In each stage, the patient is exposed to specific risk of death and loss to follow up. Transitions between stages are assigned randomly according to the failure and switching rates observed in the Gugulethu and Khayelitsha ART programmes in Cape Town, South Africa. The flow chart is simplified: the nature of immunological failure (with or without preceding virological failure) will influence outcome of second-line ART. As treatment failures are rare, most patients stay on successful first-line ART during the entire follow-up period.

Figure 2. Number of patients in different viral load categories (A)*, cohort viral load (B) and expected number of new infections (C)

rVL, routine viral load; CVL, cohort viral load; ART, antiretroviral therapy. *) The same individuals were followed up through the entire 5 years, and because of mortality the total number of patients decreases over time.