Introduction Eswatini achieved a 44% decrease in new HIV infections from 2014 to 2019 through substantial scale-up of testing and treatment. However, it still has one of the highest rates of HIV incidence in the world, with 14 infections per 1,000 adults 15-49 years estimated for 2017. The Government of Eswatini has called for an 85% reduction in new infections by 2023 over 2017 levels. To make further progress towards this target and to achieve maximum health gains, this study aims to model optimized investments of available HIV resources. Methods The Optima HIV model was applied to estimate the impact of efficiency strategies to accelerate prevention of HIV infections and HIV-related deaths. We estimated the number of infections and deaths that could be prevented by optimizing HIV investments. We optimize across HIV programs, then across service delivery modalities for voluntary medical male circumcision (VMMC), HIV testing, and antiretroviral refill, as well as switching to a lower cost antiretroviral regimen. Findings Under an optimized budget, prioritising HIV testing for the general population followed by key preventative interventions may result in approximately 1,000 more new infections (2% more) being averted by 2023. More infections could be averted with further optimization between service delivery modalities across the HIV cascade. Scaling-up index and self-testing could lead to 100,000 more people getting tested for HIV (25% more tests) with the same budget. By prioritizing Fast-Track, community-based, and facility-based antiretroviral refill options, an estimated 30,000 more people could receive treatment, 17% more than
Background Estimating the distribution of new HIV infections according to identifiable characteristics is a priority for programmatic planning in HIV prevention. We propose a mathematical modelling approach that uses robust data sources to estimate the distribution of new infections acquired in the generalised epidemics of sub-Saharan Africa and validate it against cohort data. Methods We developed a predictive model that represents the population according to factors powerfully associated with risk: gender, marital status, geographic location, key risk behaviours (sex-work, injecting drug-use, male-to-male sex), sero-discordancy within couples, circumcision and ART status. Incidence inference methods are applied to estimate the short-term distribution of new infections by group. The model is applied within a Bayesian framework whereby regional demographic and epidemiological prior information is updated, where possible, with local data. We validated and trained the model against cohort data from Manicaland (Zimbabwe), Kisesa (Tanzania) and Rakai (Uganda). Building on the results from the acquisition model we infer likely sources of transmission. The model was applied to six countries in the region to investigate potential differences in incidence patterns. Results Without training using the site-specific data, the model was able to predict the pattern of new infections with reasonable accuracy: 95% credible intervals were substantially overlapping and the rank ordering of groups with new infections was consistent. With training using group-specific data on new infections, the accuracy of predictions for subsequent rounds of data improved further and credible intervals narrowed. When applied to the six countries in the region the model showed variation in the distribution of infections between and within countries consistent with the data on prevalence. Conclusions It is possible to accurately predict, the distribution of new HIV infections acquired using data routinely available in many countries in the Sub-Saharan African region. This validated tool can complement additional analyses on resource allocation and data collection priorities.
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