Effect of population viral load on prospective HIV incidence in a hyperendemic rural African community

Abstract

Monitoring HIV population viral load (PVL) has been advocated as an important means of inferring HIV transmission potential and predicting the future rate of new HIV infections (HIV incidence) in a particular community. However, the relationship between PVL measures and directly measured HIV incidence has not been quantified in any setting and, most importantly, in a hyperendemic sub-Saharan African setting. We assessed this relationship using one of Africa's largest population-based prospective population cohorts in rural KwaZulu-Natal, South Africa in which we followed 8732 HIV-uninfected participants between 2011 and 2015. Despite clear evidence of spatial clustering of high viral loads in some communities, our results demonstrate that PVL metrics derived from aggregation of viral load data only from the HIV-positive members of a particular community did not predict HIV incidence in this typical hyperendemic, rural African population. Only once we used modified PVL measures, which combined viral load information with the underlying spatial variation in the proportion of the population infected (HIV prevalence), did we find a consistently strong relationship with future risk of HIV acquisition. For example, every 1% increase in the overall proportion of a population having detectable virus (PDV _P ) was independently associated with a 6.3% increase in an individual's risk of HIV acquisition (P = 0.001). In hyperendemic African populations, these modified PVL indices could play a key role in targeting and monitoring interventions in the most vulnerable communities where the future rate of new HIV infections is likely to be highest.

Fig. 1.. Age-sex differences in viral load patterns in the 2011 population-based survey.

Geometric mean viral load (A) and proportion of viral loads (>50,000 copies/ml) (B). Estimates are shown with 95% confidence intervals.

Fig. 2.. Geographical variations in the population viral load (PVL) indices derived from the 2011 population-based viral load survey.

The PVL indices are derived from HIV-positive participants only (A to C) and both HIV-positive and HIV-negative participants (D to F). The PVL maps were obtained using a moving two-dimensional standard Gaussian kernel of a 3-km radius. The Kulldorff spatial clusters of high viral loads are shown as black circles. Further details of the Kulldorff spatial clustering results are given in table S9. VL, viral load.

Fig. 3.. Graphs showing the ecological relationship between HIV incidence (2011 to 2015) and PVL quartiles derived from the 2011 population-based viral load survey (quartile 1, communities with lowest PVL values).

The PVL indices are derived from HIV-positive participants only (A to C) and HIV-positive and HIV-negative participants (D to F). Incidence estimates are shown with 95% confidence intervals.