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. 2020 May;7(5):e348-e358.
doi: 10.1016/S2352-3018(19)30436-9. Epub 2020 Feb 13.

The effect of 90-90-90 on HIV-1 incidence and mortality in eSwatini: a mathematical modelling study

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The effect of 90-90-90 on HIV-1 incidence and mortality in eSwatini: a mathematical modelling study

Adam Akullian et al. Lancet HIV. 2020 May.

Abstract

Background: The rapid scale-up of antiretroviral therapy (ART) towards the UNAIDS 90-90-90 goals over the last decade has sparked considerable debate as to whether universal test and treat can end the HIV-1 epidemic in sub-Saharan Africa. We aimed to develop a network transmission model, calibrated to capture age-specific and sex-specific gaps in the scale-up of ART, to estimate the historical and future effect of attaining and surpassing the UNAIDS 90-90-90 treatment targets on HIV-1 incidence and mortality, and to assess whether these interventions will be enough to achieve epidemic control (incidence of 1 infection per 1000 person-years) by 2030.

Methods: We used eSwatini (formerly Swaziland) as a case study to develop our model. We used data on HIV prevalence by 5-year age bins, sex, and year from the 2007 Swaziland Demographic Health Survey (SDHS), the 2011 Swaziland HIV Incidence Measurement Survey, and the 2016 Swaziland Population Health Impact Assessment (PHIA) survey. We estimated the point prevalence of ART coverage among all HIV-infected individuals by age, sex, and year. Age-specific data on the prevalence of male circumcision from the SDHS and PHIA surveys were used as model inputs for traditional male circumcision and scale-up of voluntary medical male circumcision (VMMC). We calibrated our model using publicly available data on demographics; HIV prevalence by 5-year age bins, sex, and year; and ART coverage by age, sex, and year. We modelled the effects of five scenarios (historical scale-up of ART and VMMC [status quo], no ART or VMMC, no ART, age-targeted 90-90-90, and 100% ART initiation) to quantify the contribution of ART scale-up to declines in HIV incidence and mortality in individuals aged 15-49 by 2016, 2030, and 2050.

Findings: Between 2010 and 2016, status-quo ART scale-up among adults (aged 15-49 years) in eSwatini (from 34·0% in 2010 to 74·1% in 2016) reduced HIV incidence by 43·57% (95% credible interval 39·71 to 46·36) and HIV mortality by 56·17% (54·06 to 58·92) among individuals aged 15-49 years, with larger reductions in incidence among men and mortality among women. Holding 2016 ART coverage levels by age and sex into the future, by 2030 adult HIV incidence would fall to 1·09 (0·87 to 1·29) per 100 person-years, 1·42 (1·13 to 1·71) per 100 person-years among women and 0·79 (0·63 to 0·94) per 100 person-years among men. Achieving the 90-90-90 targets evenly by age and sex would further reduce incidence beyond status-quo ART, primarily among individuals aged 15-24 years (an additional 17·37% [7·33 to 26·12] reduction between 2016 and 2030), with only modest additional incidence reductions in adults aged 35-49 years (1·99% [-5·09 to 7·74]). Achieving 100% ART initiation among all people living with HIV within an average of 6 months from infection-an upper bound of plausible treatment effect-would reduce adult HIV incidence to 0·73 infections (0·55 to 0·92) per 100 person-years by 2030 and 0·46 (0·33 to 0·59) per 100 person-years by 2050.

Interpretation: Scale-up of ART over the last decade has already contributed to substantial reductions in HIV-1 incidence and mortality in eSwatini. Focused ART targeting would further reduce incidence, especially in younger individuals, but even the most aggressive treatment campaigns would be insufficient to end the epidemic in high-burden settings without a renewed focus on expanding preventive measures.

Funding: Global Good Fund and the Bill & Melinda Gates Foundation.

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Figures

Figure 1
Figure 1
Best fitting incidence simulations (n=250) and LOESS curves for individuals aged 15–49 years by sex Data points are incidence point estimates (95% CI) from two population-based surveys (Swaziland HIV Incidence Measurement Survey, 2011, and Swaziland Population Health Impact Assessment, 2016). LOESS=locally weighted smoothing.
Figure 2
Figure 2
Incidence and mortality trajectories by scenarios 1–5 The curves are LOESS curves of 250 modelled simulations. The epidemic control threshold is an incidence of 0·1 infections per 100 person-years. ART=antiretroviral therapy. LOESS=locally weighted smoothing.
Figure 3
Figure 3
Modelled scale-up of ART by age group, sex, and scenario over time Data are point estimates (95% CI) from 2 survey years (2011 and 2016) overlaid., ART=antiretroviral therapy.
Figure 4
Figure 4
Incidence trajectories by ART scale-up scenario, age group, and sex from 2015 to 2050 The curves are LOESS curves of 250 modelled simulations. ART=antiretroviral therapy. LOESS=locally weighted smoothing.
Figure 5
Figure 5
Reduction in cumulative HIV incidence for scenarios 4 and 5 relative to scenario 1 (status quo), by age and sex, during 2016–30 and 2016–50 Data are reduction (95% credible interval). Negative values indicate an increase in cumulative incidence. ART=antiretroviral therapy.

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