Modeling the HIV Cascade of Care Using Routinely Collected Clinical Data to Guide Programmatic Interventions and Policy Decisions

Abstract

Background: The HIV care cascade is a framework to examine effectiveness of HIV programs and progress toward global targets to end the epidemic but has been conceptualized as a unidirectional process that ignores cyclical care patterns. We present a dynamic cascade that accounts for patient "churn" and apply novel analytic techniques to readily available clinical data to robustly estimate program outcomes and efficiently assess progress toward global targets.

Methods: Data were assessed for 35,649 people living with HIV and receiving care at 78 clinics in East Africa between 2014 and 2020. Patients were aged ≥15 years and had ≥1 viral load measurements. We used multi-state models to estimate the probability of being in 1 of 5 states of a dynamic HIV cascade: (1) in HIV care but not on antiretroviral therapy (ART), (2) on ART, (3) virally suppressed, (4) in a gap-in-care, and (5) deceased and compared these among subgroups. To assess progress toward global targets, we summed those probabilities across patients and generated population-level proportions of patients on ART and virally suppressed in mid-2020.

Results: One year after enrollment, 2.8% of patients had not initiated ART, 86.7% were receiving ART, 57.4% were virally suppressed, 10.2% were disengaged from care, and 0.3% had died. At 5 years, the proportion on ART remained steady but viral suppression increased to 77.2%. Of those aged 15-25, >20% had disengaged from care and <60% were virally suppressed. In mid-2020, 90.1% of the cohort was on ART, 90.7% of whom had suppressed virus.

Conclusions: Novel analytic approaches can characterize patient movement through a dynamic HIV cascade and, importantly, by capitalizing on readily available data from clinical cohorts, offer an efficient approach to estimate population-level proportions of patients on ART and virally suppressed. Significant progress toward global targets was observed in our cohort but challenges remain among younger patients.

Figure 1.

Multi-state model of the HIV care cascade.

Figure 2.

State-occupation probabilities for a dynamic HIV care cascade, n=35,649

Figure 3.

Probability of being virally suppressed over time since enrollment, among those alive and in care, by sex (top left), age (top right), CD4 count at enrollment (bottom left), and year of enrollment (bottom right).

Figure 4.

Probability of being in a gap-in-care over time since enrollment by sex (top left), age (top right), CD4 at enrollment (bottom left), and year of enrollment (bottom right).

Figure 5.

Proportion of patients not known to have died who were on ART and virally suppressed as of May 30, 2020.