Beyond Risk Compensation: Clusters of Antiretroviral Treatment (ART) Users in Sexual Networks Can Modify the Impact of ART on HIV Incidence

Abstract

Introduction: Concerns about risk compensation-increased risk behaviours in response to a perception of reduced HIV transmission risk-after the initiation of ART have largely been dispelled in empirical studies, but other changes in sexual networking patterns may still modify the effects of ART on HIV incidence.

Methods: We developed an exploratory mathematical model of HIV transmission that incorporates the possibility of ART clusters, i.e. subsets of the sexual network in which the density of ART patients is much higher than in the rest of the network. Such clusters may emerge as a result of ART homophily-a tendency for ART patients to preferentially form and maintain relationships with other ART patients. We assessed whether ART clusters may affect the impact of ART on HIV incidence, and how the influence of this effect-modifying variable depends on contextual variables such as HIV prevalence, HIV serosorting, coverage of HIV testing and ART, and adherence to ART.

Results: ART homophily can modify the impact of ART on HIV incidence in both directions. In concentrated epidemics and generalized epidemics with moderate HIV prevalence (≈ 10%), ART clusters can enhance the impact of ART on HIV incidence, especially when adherence to ART is poor. In hyperendemic settings (≈ 35% HIV prevalence), ART clusters can reduce the impact of ART on HIV incidence when adherence to ART is high but few people living with HIV (PLWH) have been diagnosed. In all contexts, the effects of ART clusters on HIV epidemic dynamics are distinct from those of HIV serosorting.

Conclusions: Depending on the programmatic and epidemiological context, ART clusters may enhance or reduce the impact of ART on HIV incidence, in contrast to serosorting, which always leads to a lower impact of ART on HIV incidence. ART homophily and the emergence of ART clusters should be measured empirically and incorporated into more refined models used to plan and evaluate ART programmes.

Fig 1. Causal pathways linking the availability of ART to HIV incidence within a population.

Notes: wide arrows represent direct pathways, whereas solid narrow arrows represent feedback loops that have been considered in the literature on treatment-as-prevention. The dotted arrow represents ART homophily, another causal feedback loop that has not been considered in the literature on treatment-as-prevention.

Fig 2. Illustration of the effects of ART homophily on HIV exposure in a population.

HIV-negative individuals appear in empty circles, while people living with HIV (PLWH) are represented by triangles. ART patients appear in blue triangles, whereas other (untreated) PLWH appear in red triangles. In panel A) the network is formed at random, in panel B) the network is formed based on ART homophily: all HIV-negative individuals are connected only to the one PLWH not on ART, and the 3 ART patients are connected together in an ART cluster.

Fig 3. Population-level impact of ART on HIV incidence, as a function of the model parameters (one-way analysis).

Notes: The impact of ART on HIV incidence is defined as the relative change in the HIV incidence rate, associated with ART (I/I_noART − 1). An ART impact of 0 thus indicates no effects of ART on HIV incidence, whereas an ART impact of -1 indicates that ART eliminates HIV incidence.

Fig 4. Effects of model parameters on the modification factor of the impact of ART associated with ART homophily.

Notes: The modification factor is calculated by dividing the estimated impact of ART in a context with perfect ART homophily (m = 1) by the estimated impact of ART in a context without any ART homophily (m = 0).

Fig 5. The effect of ART homophily and serosorting on the population-level impact of ART on HIV incidence, by levels of HIV prevalence (h), fraction of PLWH who are aware of their HIV status (d) and intra-couple effectiveness of ART (r).

Contour lines indicate the impact of ART on HIV incidence in the absence of ART clusters (m = 0). Color-coding indicates the modification factor of ART clusters: the factor by which the ART impact on HIV incidence increases (> 1 in blue) or decreases (< 1 in red) when comparing the case of m = 1 to the case of m = 0. For example, in the darkest blue areas, the impact of ART on HIV incidence is 50% greater in the presence of perfect ART homophily (m = 1) than it would have been if networks were formed without any ART homophily (m = 0). The uptake of ART among diagnosed PLWH (a) was fixed at 50% in all model scenarios shown.