Evidence map›Paper›PMID 28972975›Full record

ArticlePloS one2017

Getting it right when budgets are tight: Using optimal expansion pathways to prioritize responses to concentrated and mixed HIV epidemics.

Robyn M Stuart, Cliff C Kerr, Hassan Haghparast-Bidgoli, Janne Estill, Laura Grobicki, Zofia Baranczuk, Lorena Prieto, Vilma Montañez, Iyanoosh Reporter, Richard T Gray and 12 more

Abstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Observational
  6. Review
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

22 authors.

Robyn M StuartBurnet Institute, Melbourne VIC, Australia.ORCID http://orcid.org/0000-0001-5482-9584
Cliff C KerrBurnet Institute, Melbourne VIC, Australia.
Hassan Haghparast-BidgoliInstitute for Global Health, University College London, London, United Kingdom.
Janne EstillInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Laura GrobickiInstitute for Global Health, University College London, London, United Kingdom.
Zofia BaranczukInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Lorena PrietoUniversidad del Pacífico, Lima, Peru.
Vilma MontañezUniversidad del Pacífico, Lima, Peru.
Iyanoosh ReporterBurnet Institute, Melbourne VIC, Australia.
Richard T GrayThe Kirby Institute, UNSW Sydney, Sydney NSW, Australia.
Jolene Skordis-WorrallInstitute for Global Health, University College London, London, United Kingdom.
Olivia KeiserInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Nejma CheikhThe World Bank Group, Washington DC, United States of America.
Krittayawan BoontoUNAIDS Myanmar, Yangon, Myanmar.
Sutayut OsornprasopThe World Bank Group, Washington DC, United States of America.
Fernando LavadenzThe World Bank Group, Washington DC, United States of America.
Clemens J BenediktThe World Bank Group, Washington DC, United States of America.
Rowan Martin-HughesBurnet Institute, Melbourne VIC, Australia.
S Azfar HussainBurnet Institute, Melbourne VIC, Australia.
Sherrie L KellyBurnet Institute, Melbourne VIC, Australia.
David J KedzioraBurnet Institute, Melbourne VIC, Australia.
David P WilsonBurnet Institute, Melbourne VIC, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrioritizing investments across health interventions is complicated by the nonlinear relationship between intervention coverage and epidemiological outcomes. It can be difficult for countries to know which interventions to prioritize for greatest epidemiological impact, particularly when budgets are uncertain.

methodsWe examined four case studies of HIV epidemics in diverse settings, each with different characteristics. These case studies were based on public data available for Belarus, Peru, Togo, and Myanmar. The Optima HIV model and software package was used to estimate the optimal distribution of resources across interventions associated with a range of budget envelopes. We constructed "investment staircases", a useful tool for understanding investment priorities. These were used to estimate the best attainable cost-effectiveness of the response at each investment level.

findingsWe find that when budgets are very limited, the optimal HIV response consists of a smaller number of 'core' interventions. As budgets increase, those core interventions should first be scaled up, and then new interventions introduced. We estimate that the cost-effectiveness of HIV programming decreases as investment levels increase, but that the overall cost-effectiveness remains below GDP per capita. SIGNIFICANCE: It is important for HIV programming to respond effectively to the overall level of funding availability. The analytic tools presented here can help to guide program planners understand the most cost-effective HIV responses and plan for an uncertain future.

Indexed as

BudgetsHealth PrioritiesCost-Benefit AnalysisHIV InfectionsHumans

Identifiers

PMID28972975
PMCPMC5626425

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.