The impact of antiretroviral treatment on the age composition of the HIV epidemic in sub-Saharan Africa

Abstract

Introduction: Antiretroviral treatment (ART) coverage is rapidly expanding in sub-Saharan Africa (SSA). Based on the effect of ART on survival of HIV-infected people and HIV transmission, the age composition of the HIV epidemic in the region is expected to change in the coming decades. We quantify the change in the age composition of HIV-infected people in all countries in SSA.

Methods: We used STDSIM, a stochastic microsimulation model, and developed an approach to represent HIV prevalence and treatment coverage in 43 countries in SSA, using publicly available data. We predict future trends in HIV prevalence and total number of HIV-infected people aged 15-49 years and 50 years or older for different ART coverage levels.

Results: We show that, if treatment coverage continues to increase at present rates, the total number of HIV-infected people aged 50 years or older will nearly triple over the coming years: from 3.1 million in 2011 to 9.1 million in 2040, dramatically changing the age composition of the HIV epidemic in SSA. In 2011, about one in seven HIV-infected people was aged 50 years or older; in 2040, this ratio will be larger than one in four.

Conclusion: The HIV epidemic in SSA is rapidly ageing, implying changing needs and demands in many social sectors, including health, social care, and old-age pension systems. Health policymakers need to anticipate the impact of the changing HIV age composition in their planning for future capacity in these systems.

Figure 1. Geographical distribution of sexual-behaviour profiles

The colour of each country represents the best fitting sexual-behaviour profile given country-specific circumcision levels (table S2) and ART roll out (figure 2A). A detailed description of the profiles is given in table S1.

Figure 2. Model fit compared to data

A. Predicted ART coverage of those eligible at ≤200 cells/μL in the model compared to WHO data over the period 2004-2009. The dashed line represents a perfect fit (ie predicted coverage in model = WHO data). B. Predicted HIV prevalence for low and medium endemic countries in the model compared to UNAIDS prevalence estimates over the period 2004-2009. The dashed line represents a perfect fit (eg predicted prevalence in the model = UNAIDS data), the dotted line represents a 10% difference between model predictions and data. C. Predicted HIV prevalence for high endemic countries in the model compared to UNAIDS prevalence estimates over the period 2004-2009. The dashed line represents a perfect fit (eg predicted prevalence in the model = UNAIDS data), the dotted line represents a 10% difference between model predictions and data. Full country-specific parameter settings are given in table S2.

Figure 3. HIV prevalence in the population age 15-49 and 50+ in sub-Saharan Africa for the years 2011, 2025 and 2040, under continuous scale up of antiretroviral therapy

N/A = Not Applicable

Figure 4. Projected trends of total number of infections and in sub-Saharan Africa over the period 2010-2040 under continuous scale up of antiretroviral therapy

The change is relative to the total number of HIV infected patients per age category in 2011.

Figure 5. Predicted age composition of the HIV-infected population by ART scale-up scenario

Baseline = baseline scenario of continued scale-up of ART coverage; decline = scenario in which health-system capacity to deliver ART is reduced by 20% in 2012; no further scale-up = scenario in which health-system capacity to deliver ART remains at the same level as in 2011; Rapid scale-up = scenario in which health-system capacity to deliver ART is scaled-up to 100% for all countries by 2015.