Evidence map›Paper›PMID 26928810›Full record

ArticleJournal of the International AIDS Society2016

In the interests of time: improving HIV allocative efficiency modelling via optimal time-varying allocations.

Andrew J Shattock, Cliff C Kerr, Robyn M Stuart, Emiko Masaki, Nicole Fraser, Clemens Benedikt, Marelize Gorgens, David P Wilson, Richard T Gray

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Article in Journal of the International AIDS Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Andrew J ShattockThe Kirby Institute, University of New South Wales, Sydney, Australia; ashattock@kirby.unsw.edu.au.
Cliff C KerrThe Kirby Institute, University of New South Wales, Sydney, Australia.
Robyn M StuartThe Kirby Institute, University of New South Wales, Sydney, Australia.
Emiko MasakiThe World Bank Group, Washington DC, USA.
Nicole FraserThe World Bank Group, Washington DC, USA.
Clemens BenediktThe World Bank Group, Washington DC, USA.
Marelize GorgensThe World Bank Group, Washington DC, USA.
David P WilsonThe Kirby Institute, University of New South Wales, Sydney, Australia.
Richard T GrayThe Kirby Institute, University of New South Wales, Sydney, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionInternational investment in the response to HIV and AIDS has plateaued and its future level is uncertain. With many countries committed to ending the epidemic, it is essential to allocate available resources efficiently over different response periods to maximize impact. The objective of this study is to propose a technique to determine the optimal allocation of funds over time across a set of HIV programmes to achieve desirable health outcomes.

methodsWe developed a technique to determine the optimal time-varying allocation of funds (1) when the future annual HIV budget is pre-defined and (2) when the total budget over a period is pre-defined, but the year-on-year budget is to be optimally determined. We use this methodology with Optima, an HIV transmission model that uses non-linear relationships between programme spending and associated programmatic outcomes to quantify the expected epidemiological impact of spending. We apply these methods to data collected from Zambia to determine the optimal distribution of resources to fund the right programmes, for the right people, at the right time. RESULTS AND DISCUSSION: Considering realistic implementation and ethical constraints, we estimate that the optimal time-varying redistribution of the 2014 Zambian HIV budget between 2015 and 2025 will lead to a 7.6% (7.3% to 7.8%) decrease in cumulative new HIV infections compared with a baseline scenario where programme allocations remain at 2014 levels. This compares to a 5.1% (4.6% to 5.6%) reduction in new infections using an optimal allocation with constant programme spending that recommends unrealistic programmatic changes. Contrasting priorities for programme funding arise when assessing outcomes for a five-year funding period over 5-, 10- and 20-year time horizons.

conclusionsCountries increasingly face the need to do more with the resources available. The methodology presented here can aid decision-makers in planning as to when to expand or contract programmes and to which coverage levels to maximize impact.

Indexed as

Resource AllocationHIV InfectionsHumansModels, TheoreticalZambiaallocative efficiencyHIV/AIDSmathematical modellingOptimatime-varying optimizationZambia

Identifiers

PMID26928810
PMCPMC4770825

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.