Table 3.
PrEP in the context of resource optimization and combination prevention
| PrEP strategies in sub‐Saharan Africa | Modelling | Location | Impact | Cost | Cost‐effectiveness |
|---|---|---|---|---|---|
| Resource optimization, combination prevention and PrEP | |||||
| Optimization of HIV resources and combination prevention in SSA | McGillen 43 | SSA | 3×impact targeting by risk + geo; greater marginal impact by risk versus geo | ||
| McGillen 42 | SSA | Marginal impact of PrEP at high budget availability | $6 B+ (15 years) | Key priorities: MC, behavioural change communication for high risk, early ART | |
| McGillen 42 | SSA | 14% reduction in new infections (15 years) | $1T (15 years) | ||
| Optimization of HIV resources and combination prevention in South Africa | Smith 50 | South Africa | Optimal: MC, ART for all with outreach testing | ||
| Long 41 | South Africa | 62% infections averted, 31 M QALYs (testing+ART+MC+microbicides+PrEP) | $10,000 to 30,000/QALY gained | ||
| Optimization of HIV resources and combination prevention in Kenya | Anderson 26 | Kenya | Optimal: 40% reduction in new infections, +14% targeting by risk + geo | $600 M (15 years) | Optimal (uniform): Behavioural change communication, early ART, then PrEP |
| Alsallaq 25 | Nyanza, Kenya | 11,000 infections averted, 0.5 M DALYs (20 years) | $31.8 M (20 years) | Optimal (youth): HIV testing, TasP, condoms, MC, PrEP | |
| Cremin 33 | Nairobi, Kenya | Optimal: Condom promotion for MSM & MSW, ART retention, earlier ART, MC, then PrEP | |||
| Optimization of HIV resources and combination prevention in Zambia | Nichols 46 | Macha, Zambia | Optimal: ART expansion. PrEP economical only at very high budget availability | ||
| Optimization of combination prevention for serodiscordant | Mitchell 45 | Nigeria | Optimal: ART scale‐up, condoms, TasP, then PrEP | ||
| Optimization of fixed amount of antiretrovirals in the context of 90‐90‐90 and PrEP | Akudibillah 23 | South Africa | Optimal: Use of all antiretrovirals for ART | ||
| PrEP for populations where HIV incidence is >3 per 100 person‐years | McGillen 42 | SSA | Loss of impact | Low budget | At low budgets threshold PrEP takes away from more cost‐effective interventions |
| McGillen 42 | SSA | Reduced impact: 7% of incidence reduction lost compared to optimization | High budget | At higher budgets threshold PrEP limits optimal impact to lower incidence pops | |
Impact in italics indicates combined PrEP impact across multiple interventions. ART, antiretroviral treatment; B, billion; geo, geography; HIV, human immunodeficiency virus; M, million; MC, male circumcision; MSM, men who have sex with men; MSW, male sex worker; PrEP, pre‐exposure prophylaxis; QALY, quality adjusted life year; SSA, sub‐Saharan Africa; T, trillion; TasP, treatment as prevention.