Brooke E Nichols1, Rob Baltussen, Janneke H van Dijk, Phil E Thuma, Jan L Nouwen, Charles A B Boucher, David A M C van de Vijver. 1. *Department of Viroscience, Erasmus Medical Centre, Rotterdam, the Netherlands; †Department of Primary and Community Care, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands; ‡Macha Mission Hospital and Macha Research Trust, Macha, Zambia; and §Department of Medical Microbiology and Infectious Diseases, Erasmus Medical Centre, Rotterdam, the Netherlands.
Abstract
BACKGROUND: Earlier antiretroviral therapy initiation and pre-exposure prophylaxis (PrEP) prevent HIV, although at a substantial cost. We use mathematical modeling to compare the cost-effectiveness and economic affordability of antiretroviral-based prevention strategies in rural Macha, Zambia. METHODS: We compare the epidemiological impact and cost-effectiveness over 40 years of a baseline scenario (treatment initiation at CD4 <350 cells/μL) with treatment initiation at CD4 <500 cells per microliter, and PrEP (prioritized to the most sexually active, or nonprioritized). A strategy is cost effective when the incremental cost-effectiveness ratio (ICER) is <$3480 (<3 times Zambian per capita GDP). Stochastic league tables then predict the optimal intervention per budget level. RESULTS: All scenarios will reduce the prevalence from 6.2% (interquartile range, 5.8%-6.6%) in 2014 to about 1% after 40 years. Compared with the baseline, 16% of infections will be averted with prioritized PrEP plus treatment at CD4 <350, 34% with treatment at CD4 <500, and 59% with nonprioritized PrEP plus treatment at CD4 <500. Only treating at CD4 <500 is cost effective: ICER of $62 ($46-$75). Nonprioritized PrEP plus treating at CD4 <500 is borderline cost effective: ICER of $5861 ($3959-$8483). Initiating treatment at CD4 <500 requires a budget increase from $20 million to $25 million over 40 years, with a 96.7% probability of being the optimal intervention. PrEP should only be considered when the budget exceeds $180 million. CONCLUSIONS: Treatment initiation at CD4 <500 is a cost-effective HIV prevention approach that will require a modest increase in budget. Although adding PrEP will avert more infections, it is not economically feasible, as it requires a 10-fold increase in budget.
BACKGROUND: Earlier antiretroviral therapy initiation and pre-exposure prophylaxis (PrEP) prevent HIV, although at a substantial cost. We use mathematical modeling to compare the cost-effectiveness and economic affordability of antiretroviral-based prevention strategies in rural Macha, Zambia. METHODS: We compare the epidemiological impact and cost-effectiveness over 40 years of a baseline scenario (treatment initiation at CD4 <350 cells/μL) with treatment initiation at CD4 <500 cells per microliter, and PrEP (prioritized to the most sexually active, or nonprioritized). A strategy is cost effective when the incremental cost-effectiveness ratio (ICER) is <$3480 (<3 times Zambian per capita GDP). Stochastic league tables then predict the optimal intervention per budget level. RESULTS: All scenarios will reduce the prevalence from 6.2% (interquartile range, 5.8%-6.6%) in 2014 to about 1% after 40 years. Compared with the baseline, 16% of infections will be averted with prioritized PrEP plus treatment at CD4 <350, 34% with treatment at CD4 <500, and 59% with nonprioritized PrEP plus treatment at CD4 <500. Only treating at CD4 <500 is cost effective: ICER of $62 ($46-$75). Nonprioritized PrEP plus treating at CD4 <500 is borderline cost effective: ICER of $5861 ($3959-$8483). Initiating treatment at CD4 <500 requires a budget increase from $20 million to $25 million over 40 years, with a 96.7% probability of being the optimal intervention. PrEP should only be considered when the budget exceeds $180 million. CONCLUSIONS: Treatment initiation at CD4 <500 is a cost-effective HIV prevention approach that will require a modest increase in budget. Although adding PrEP will avert more infections, it is not economically feasible, as it requires a 10-fold increase in budget.
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