Evidence map›Paper›PMID 39369490›Full record

ArticleEpidemics2024

The effects of HIV self-testing on HIV incidence and awareness of status among men who have sex with men in the United States: Insights from a novel compartmental model.

Alex Viguerie, Chaitra Gopalappa, Cynthia M Lyles, Paul G Farnham

Abstract read
In one paragraph

Article in Epidemics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. 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

4 authors.

Alex ViguerieCenters for Disease Control and Prevention (CDC), National Center for HIV, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Division of HIV Prevention (DHP), Quantitative Sciences Branch (QSB), USA. Electronic address: xjm9@cdc.gov.
Chaitra GopalappaCenters for Disease Control and Prevention (CDC), National Center for HIV, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Division of HIV Prevention (DHP), Quantitative Sciences Branch (QSB), USA; Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst, Amherst, MA 01003, USA.
Cynthia M LylesCenters for Disease Control and Prevention (CDC), National Center for HIV, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Division of HIV Prevention (DHP), Quantitative Sciences Branch (QSB), USA.
Paul G FarnhamCenters for Disease Control and Prevention (CDC), National Center for HIV, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Division of HIV Prevention (DHP), Quantitative Sciences Branch (QSB), USA.

Funding

Intramural CDC HHS CC999999
6 · The paper itself

Abstract

backgroundThe OraQuick In-Home HIV self-test represents a fast, inexpensive, and convenient method for users to assess their HIV status. If integrated thoughtfully into existing testing practices, accompanied by efficient pathways to formal diagnosis, self-testing could enhance both HIV awareness and reduce HIV incidence. However, currently available self-tests are less sensitive, particularly for recent infection, when compared to gold-standard laboratory tests. It is important to understand the impact if some portion of standard testing is replaced by self-tests. We used a compartmental model to evaluate the effects of self-testing in diverse scenarios among gay, bisexual and other men who have sex with men (MSM) in the United States for the period 2020-2030, and to understand which scenarios maximize the advantages of self-testing.

methodsWe introduced a novel 4-compartment model for HIV self-testing. We employed the model under different screening rates, self-test proportions, and delays to diagnosis for those identified through self-tests to determine the potential effects of self-testing on HIV incidence and awareness of status when applied to the US MSM population. We studied scenarios in which self-tests supplement laboratory-based tests, with no replacement, and scenarios in which some replacement occurs. We also examined how future improvements in self-test sensitivity may affect our results.

resultsWhen HIV self-tests are supplemental rather than substitutes for laboratory-based testing, self-testing can decrease HIV incidence among MSM in the US by up to 10 % and increase awareness of status among MSM from 85 % to 91 % over a 10-year period, provided linkage to care and formal diagnosis occur promptly following a positive self-test (90 days or less). As self-tests replace a higher percentage laboratory-based testing algorithms, increases in overall testing rates were necessary to ensure reductions in HIV incidence. However, such needed increases were relatively small (under 10 % for prompt engagement in care and moderate levels of replacement). Improvements in self-test sensitivity and/or decreases in the detection period may further reduce any necessary increases in overall testing by up to 40 %.

conclusionsIf properly utilized, self-testing can provide significant long-term reductions to HIV incidence and improve awareness of HIV status. Ensuring that self-testing increases overall testing and that formal diagnosis and engagement in care occur promptly following a positive self-test are necessary to maximize the benefits of self-testing. Future improvements in self-test sensitivity and reductions in the detection period would further reduce HIV incidence and the potential risks associated with replacing laboratory tests with self-tests.

Indexed as

HIV InfectionsHomosexuality, MaleSelf-TestingAdultHIV TestingHumansIncidenceMaleMass ScreeningSexual and Gender MinoritiesUnited StatesHIVHIV/AIDSMathematical modelingSelf-testing

Identifiers

PMID39369490
PMCPMC11912561

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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.