Maximising the effect of combination HIV prevention through prioritisation of the people and places in greatest need: a modelling study

Anderson, Sarah-Jane; Cherutich, Peter; Kilonzo, Nduku; Cremin, Íde; Fecht, Daniela; Kimanga, Davies; Harper, Malayah; Masha, Ruth Laibon; Ngongo, Prince Bahati; Maina, William K.; Dybul, Mark; Hallett, Timothy B.

doi:10.1016/s0140-6736(14)61053-9

Cited by 215 publications

(217 citation statements)

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“…We call this the 'semi-focused' strategy as it permits allocation by local epidemiology within the confines of national boundaries. It is a direct extension of the method developed by Anderson et al 17 to multiple countries.…”

Section: Discussionmentioning

confidence: 99%

“…We employ a mathematical model that builds upon our previous work [17][18][19] to describe the dynamic spread of HIV. Equations are available in the Supplemental Appendix of the paper by Anderson et al 17 Here we describe key details of the model structure (appendix p. 1).…”

Section: Model Designmentioning

confidence: 99%

“…Equations are available in the Supplemental Appendix of the paper by Anderson et al 17 Here we describe key details of the model structure (appendix p. 1).…”

Section: Model Designmentioning

confidence: 99%

“…15 The Optimal model 16 has informed national priority setting and programme planning in several African countries, though to date countries tend not to account explicitly for subnational heterogeneity in choosing intervention packages for population groups. A notable exception is Kenya, which changed its strategic plans 1 following a modelling case study by Anderson et al 17 that synthesised subnational transmission modelling and combination prevention optimisation under a constrained national budget for HIV spending.…”

Section: Introductionmentioning

confidence: 99%

“…We seek to determine whether the results of Anderson et al 17 could scale beyond Kenya, and how funds could be optimised across multiple countries under a single analytical framework. Moreover, we aim to unpack the sources of epidemic heterogeneity and examine their roles in the potential cumulative impact of prevention efforts.…”

Section: Introductionmentioning

confidence: 99%

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Optimum resource allocation to reduce HIV incidence across sub-Saharan Africa: a mathematical modelling study

et al. 2016

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Section: Discussionmentioning

confidence: 99%

Section: Model Designmentioning

confidence: 99%

“…Equations are available in the Supplemental Appendix of the paper by Anderson et al 17 Here we describe key details of the model structure (appendix p. 1).…”

Section: Model Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimum resource allocation to reduce HIV incidence across sub-Saharan Africa: a mathematical modelling study

et al. 2016

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2018

Decision Analytics and Optimization in Disease Prevention and Treatment

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HIV prevention where it is needed most: comparison of strategies for the geographical allocation of interventions

et al. 2017

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IntroductionA 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.

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Maximising the effect of combination HIV prevention through prioritisation of the people and places in greatest need: a modelling study

Abstract: The Bill & Melinda Gates Foundation and UNAIDS.

Cited by 215 publications

References 39 publications

Optimum resource allocation to reduce HIV incidence across sub-Saharan Africa: a mathematical modelling study

Optimum resource allocation to reduce HIV incidence across sub-Saharan Africa: a mathematical modelling study

Saving Lives With Operations Research

HIV prevention where it is needed most: comparison of strategies for the geographical allocation of interventions

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