A novel modelling framework to simulate the effects of HIV stigma on HIV transmission dynamics

IntroductionHIV remains a global public health challenge, with social determinants such as stigma influencing transmission dynamics, access to testing, and treatment. HIV stigma shapes both individual behaviour and community responses to HIV. However, modelling approaches have rarely represented the complex role of stigma in HIV epidemics. Our study introduces an innovative modelling framework designed to capture the interplay between stigma and HIV transmission dynamics. MethodsWe modelled effects of anticipated, internalised, and experienced HIV stigma on HIV testing, antiretroviral treatment initiation, and treatment adherence. We built an individual-based model representing the HIV epidemic (HIV-IBM) in a USA-like population of 3 million individuals that accounts for community demography, behaviour, and healthcare access. Stigma parameters were based on a scoping review focused on the prevalence and effects of stigma in people living with and without HIV. HIV-IBM was used to assess effects of interventions targeting different types of stigma. We tested reductions of stigma by 50% and 100% across the simulated population and performed a sensitivity analysis to identify effects of each type of stigma on the simulated HIV epidemic. ResultsWithout reduced stigma, the HIV-IBM had an annual incidence rate of 12.6 (95% credible interval [CI]: 11.4-13.5) new cases per 100,000 people. Reducing the overall level of stigma in the population by 50% resulted in an annual incidence rate of 9.6 (95% CI: 8.6-10.3) per 100,000, and a 100% reduction in stigma resulted in an annual incidence rate of just 6.8 (95% CI: 6.1-7.3) per 100,000. In addition to reducing HIV incidence, reducing stigma resulted in a substantial increase of viral suppression among people living with HIV (50% stigma reduction: +10.5%; 100% stigma reduction: +16.4%). Sensitivity analysis showed that outcomes resulting from interventions targeting each type of stigma were highly heterogeneous. ConclusionSimulation results suggest that reducing HIV stigma could have a large effect on HIV incidence and viral suppression. Our model framework provides a dynamic approach to understanding the role of stigma in HIV outcomes that facilitates exploration of stigma reduction strategies and offers insights to inform evidence-based policies and interventions for reducing stigma and curtailing HIV.