Evidence map›Paper›PMID 26369347›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2016

HIV Treatment and Prevention: A Simple Model to Determine Optimal Investment.

Jessie L Juusola, Margaret L Brandeau

Abstract read
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

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

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  13. Constrained Optimization for Decision Making in Health Care Using Python: A Tutorial.Medical decision making : an international journal of the Society for Medical Decision Making
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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

2 authors.

Jessie L JuusolaDepartment of Management Science and Engineering, Stanford University, Stanford, CA (JLJ, MLB).
Margaret L BrandeauDepartment of Management Science and Engineering, Stanford University, Stanford, CA (JLJ, MLB).

Funding

Making Better Decisions: Policy Modeling for AIDS and Drug AbuseR01DA015612 · NIDA · STANFORD UNIVERSITY · PI OWENS, DOUGLAS K · 2002 to 2017
$9.4M
Making Better Decisions: Policy Modeling for AIDS and Drug AbuseR37DA015612 · NIDA · STANFORD UNIVERSITY · PI DOUGLAS K OWENS · 2019 to 2026
$6.3M
NIDA NIH HHS R01 DA015612NIDA NIH HHS R01-DA15612NIDA NIH HHS R37 DA015612
6 · The paper itself

Abstract

objectiveTo create a simple model to help public health decision makers determine how to best invest limited resources in HIV treatment scale-up and prevention.

methodA linear model was developed for determining the optimal mix of investment in HIV treatment and prevention, given a fixed budget. The model incorporates estimates of secondary health benefits accruing from HIV treatment and prevention and allows for diseconomies of scale in program costs and subadditive benefits from concurrent program implementation. Data sources were published literature. The target population was individuals infected with HIV or at risk of acquiring it. Illustrative examples of interventions include preexposure prophylaxis (PrEP), community-based education (CBE), and antiretroviral therapy (ART) for men who have sex with men (MSM) in the US. Outcome measures were incremental cost, quality-adjusted life-years gained, and HIV infections averted.

resultsBase case analysis indicated that it is optimal to invest in ART before PrEP and to invest in CBE before scaling up ART. Diseconomies of scale reduced the optimal investment level. Subadditivity of benefits did not affect the optimal allocation for relatively low implementation levels. The sensitivity analysis indicated that investment in ART before PrEP was optimal in all scenarios tested. Investment in ART before CBE became optimal when CBE reduced risky behavior by 4% or less. Limitations of the study are that dynamic effects are approximated with a static model.

conclusionsOur model provides a simple yet accurate means of determining optimal investment in HIV prevention and treatment. For MSM in the US, HIV control funds should be prioritized on inexpensive, effective programs like CBE, then on ART scale-up, with only minimal investment in PrEP.

Indexed as

Cost-Benefit AnalysisInvestmentsModels, StatisticalAnti-HIV AgentsHIV InfectionsHomosexuality, MaleHumansMalePre-Exposure ProphylaxisQuality-Adjusted Life YearsResource AllocationAnti-HIV AgentsAIDScost-effectiveness analysis (CEA)HIVresource allocation

Identifiers

PMID26369347
PMCPMC4775360

What OpenQuestion holds

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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.