Evidence map›Paper›PMID 24662776›Full record

ArticlePloS one2014

Mathematical modeling of HIV prevention measures including pre-exposure prophylaxis on HIV incidence in South Korea.

Sun Bean Kim, Myoungho Yoon, Nam Su Ku, Min Hyung Kim, Je Eun Song, Jin Young Ahn, Su Jin Jeong, Changsoo Kim, Hee-Dae Kwon, Jeehyun Lee and 2 more

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed, 1 synthesis or guideline 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

12 authors.

Sun Bean KimDivision of Infectious Disease, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea; AIDS Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Myoungho YoonDepartment of Mathematics, Yonsei University, Seoul, South Korea.
Nam Su KuDivision of Infectious Disease, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea; AIDS Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Min Hyung KimDivision of Infectious Disease, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea; AIDS Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Je Eun SongDivision of Infectious Disease, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea; AIDS Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Jin Young AhnDivision of Infectious Disease, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea; AIDS Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Su Jin JeongDivision of Infectious Disease, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea; AIDS Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Changsoo KimDepartment of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea.
Hee-Dae KwonDepartment of Mathematics, Inha University, Incheon, South Korea.
Jeehyun LeeDepartment of Computational Science and Engineering, Yonsei University, Seoul, South Korea.
Davey M SmithDepartment of Medicine, University of California San Diego, La Jolla, California, United States of America; Veterans Affairs San Diego Healthcare System, San Diego, California, United States of America.
Jun Yong ChoiDivision of Infectious Disease, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea; AIDS Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.

Funding

Molecular Epidemiology for HIV Prevention for Drug Users and Other Risk Groups DP1DA034978 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SMITH, DAVID MITCHELL · 2012 to 2016
$4.1M
Mentoring Translational Research in HIVK24AI100665 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SMITH, DAVID MITCHELL · 2012 to 2021
$1.5M
NIAID NIH HHS AI100665NIAID NIH HHS K24 AI100665NIDA NIH HHS DA034978NIDA NIH HHS DP1 DA034978
6 · The paper itself

Abstract

backgroundMultiple prevention measures have the possibility of impacting HIV incidence in South Korea, including early diagnosis, early treatment, and pre-exposure prophylaxis (PrEP). We investigated how each of these interventions could impact the local HIV epidemic, especially among men who have sex with men (MSM), who have become the major risk group in South Korea. A mathematical model was used to estimate the effects of each these interventions on the HIV epidemic in South Korea over the next 40 years, as compared to the current situation.

methodsWe constructed a mathematical model of HIV infection among MSM in South Korea, dividing the MSM population into seven groups, and simulated the effects of early antiretroviral therapy (ART), early diagnosis, PrEP, and combination interventions on the incidence and prevalence of HIV infection, as compared to the current situation that would be expected without any new prevention measures.

resultsOverall, the model suggested that the most effective prevention measure would be PrEP. Even though PrEP effectiveness could be lessened by increased unsafe sex behavior, PrEP use was still more beneficial than the current situation. In the model, early diagnosis of HIV infection was also effectively decreased HIV incidence. However, early ART did not show considerable effectiveness. As expected, it would be most effective if all interventions (PrEP, early diagnosis and early treatment) were implemented together.

conclusionsThis model suggests that PrEP and early diagnosis could be a very effective way to reduce HIV incidence in South Korea among MSM.

Indexed as

Models, StatisticalEarly DiagnosisHIV InfectionsHumansIncidenceMalePre-Exposure ProphylaxisRepublic of KoreaUnsafe Sex

Identifiers

PMID24662776
PMCPMC3963840

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.