Assessing the reliability of the CD4 depletion model in the presence of Ending the HIV Epidemic initiatives

Abstract

Background: Accurate estimates of HIV incidence are necessary to monitor progress towards Ending the HIV Epidemic (EHE) initiative targets (90% decline by 2030). U.S. incidence estimates are derived from a CD4 depletion model (CD4 model). We performed simulation-based analyses to investigate the ability of this model to estimate HIV incidence when implementing EHE interventions that have the potential to shorten the duration between HIV infection and diagnosis (diagnosis delay).

Methods: Our simulation study evaluates the impact of three parameters on the accuracy of incidence estimates derived from the CD4 model: rate of HIV incidence decline, length of diagnosis delay, and sensitivity of using CD4 + cell counts to identify new infections (recency error). We model HIV incidence and diagnoses after the implementation of a theoretical prevention intervention and compare HIV incidence estimates derived from the CD4 model to simulated incidence.

Results: Theoretical interventions that shortened the diagnosis delay (10-50%) result in overestimation of HIV incidence by the CD4 model (10-92%) in the first year and by more than 10% for the first 6 years after implementation of the intervention. Changes in the rate of HIV incidence decline and the presence of recency error had minimal impact on the accuracy of incidence estimates derived from the CD4 model.

Conclusion: In the setting of EHE interventions to identify persons with HIV earlier during infection, the CD4 model overestimates HIV incidence. Alternative methods to estimate incidence based on objective measures of incidence are needed to assess and monitor EHE interventions.

Figure 1:

Simulated HIV incidence, 2014–2028. The vertical line separates the pre-intervention (years 2014–2020) and intervention (years 2021–2028) periods. The blue line simulates no change in the annual HIV incidence in the intervention period. Simulations illustrate theoretical Ending the HIV Epidemic prevention interventions representing a stable (5% per year) decline in red and a rapid (24% per year) decline in green in annual HIV incidence in the intervention phase.

Figure 2:

HIV incidence estimates using CD4 model compared with simulated incidence. Panel A demonstrates scenarios where there is a 5% annual decline in simulated incidence. Panel B demonstrates scenarios where there is a 0% annual decline in simulated incidence. Panel C demonstrates scenarios where there is a 24% annual decline in simulated incidence. Lines are labeled according to their delay between infection and diagnosis – [DD(0), blue] for no change and [DD(50), purple] for 50% decrease compared to baseline (purple) – and inclusion of recency error (RE, dashed lines) that results from using CD4 depletion to estimate the duration of infection at diagnosis.

Figure 3:

Simulated incidence by decreasing diagnosis delay. There is an incremental shortening of the diagnosis delay (DD) by 0% [DD(0), blue], 10% [DD(10), green], 25% [DD(25), red] and 50% [DD(50), purple]. All simulations have a 5% annual decline in simulated HIV incidence.