Can mother-to-child transmission of HIV be eliminated without addressing the issue of stigma? Modeling the case for a setting in South Africa.

backgroundStigma and discrimination ontinue to undermine the effectiveness of the HIV response. Despite a growing body of evidence of the negative relationship between stigma and HIV outcomes, there is a paucity of data available on the prevalence of stigma and its impact. We present a probabilistic cascade model to estimate the magnitude of impact stigma has on mother-to-child-transmission (MTCT).

methodsThe model was parameterized using 2010 data from Johannesburg, South Africa, from which loss-to-care at each stage of the antenatal cascade were available. Three scenarios were compared to assess the individual contributions of stigma, non-stigma related barriers, and drug ineffectiveness on the overall number of infant infections. Uncertainty analysis was used to estimate plausible ranges. The model follows the guidelines in place in 2010 when the data were extracted (WHO Option A), and compares this with model results had Option B+ been implemented at the time.

resultsThe model estimated under Option A, 35% of infant infections being attributed to stigma. This compares to 51% of total infections had Option B+ been implemented in 2010. Under Option B+, the model estimated fewer infections than Option A, due to the availability of more effective drugs. Only 8% (Option A) and 9% (Option B+) of infant infections were attributed to drug ineffectiveness, with the trade-off in the proportion of infections being between stigma and non-stigma-related barriers.

conclusionsThe model demonstrates that while the effect of stigma on retention of women at any given stage along the cascade can be relatively small, the cumulative effect can be large. Reducing stigma may be critical in reaching MTCT elimination targets, because as countries improve supply-side factors, the relative impact of stigma becomes greater. The cumulative nature of the PMTCT cascade results in stigma having a large effect, this feature may be harnessed for efficiency in investment by prioritizing interventions that can affect multiple stages of the cascade simultaneously.