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AbstractIntroductionDue to the nature of funding, national planners and international donors typically balance budgets over short time periods when designing HIV programmes (˜5‐year funding cycles). We aim to explicitly quantify the cost of short‐term funding arrangements on the success of future HIV prevention programmes.MethodsUsing mathematical models of HIV transmission in Kenya, we compare the impact of optimized combination prevention strategies under different constraints on investment over time. Each scenario has the same total budget for the 30‐year intervention period but the pattern of spending over time is allowed to vary. We look at the impact of programmes with decreasing, increasing or constant spending across 5‐year funding cycles for a 30‐year period. Interventions are optimized within each funding cycle such that strategies take a short‐term view of the epidemic. We compare these with two strategies with no spending pattern constraints: one with static intervention choices and another flexible strategy with interventions changed in year ten.Results and DiscussionFor the same total 30‐year budget, greatest impact is achieved if larger initial prevention spending is offset by later treatment savings which leads to accumulating benefits in reduced infections. The impact under funding cycle constraints is determined by the extent to which greater initial spending is permitted. Short‐term funding constraints and funds held back to later years may reduce impact by up to 18% relative to the flexible long‐term strategy.ConclusionsEnsuring that funding arrangements are in place to support long‐term prevention strategies will make spending most impactful. Greater prevention spending now will bring considerable returns through reductions in new infections, greater population health and reductions in the burden on health services in the future.

AbstractIntroductionA strategic approach to the application of HIV prevention interventions is a core component of the UNAIDS Fast Track strategy to end the HIV epidemic by 2030. Central to these plans is a focus on high‐prevalence geographies, in a bid to target resources to those in greatest need and maximize the reduction in new infections. Whilst this idea of geographical prioritization has the potential to improve efficiency, it is unclear how it should be implemented in practice. There are a range of prevention interventions which can be applied differentially across risk groups and locations, making allocation decisions complex. Here, we use mathematical modelling to compare the impact (infections averted) of a number of different approaches to the implementation of geographical prioritization of prevention interventions, similar to those emerging in policy and practice, across a range of prevention budgets.MethodsWe use geographically specific mathematical models of the epidemic and response in 48 counties and major cities of Kenya to project the impact of the different geographical prioritization approaches. We compare the geographical allocation strategies with a nationally uniform approach under which the same interventions must be applied across all modelled locations.ResultsWe find that the most extreme geographical prioritization strategy, which focuses resources exclusively to high‐prevalence locations, may substantially restrict impact (41% fewer infections averted) compared to a nationally uniform approach, as opportunities for highly effective interventions for high‐risk populations in lower‐prevalence areas are missed. Other geographical allocation approaches, which intensify efforts in higher‐prevalence areas whilst maintaining a minimum package of cost‐effective interventions everywhere, consistently improve impact at all budget levels. Such strategies balance the need for greater investment in locations with the largest epidemics whilst ensuring higher‐risk groups in lower‐priority locations are provided with cost‐effective interventions.ConclusionsOur findings serve as a warning to not be too selective in the application of prevention strategies. Further research is needed to understand how decision‐makers can find the right balance between the choice of interventions, focus on high‐risk populations, and geographical targeting to ensure the greatest impact of HIV prevention.