Evidence map›Paper›PMID 34583667›Full record

ArticleBMC public health2021

Effectiveness of sexual health influencers identified by an ensemble machine learning model in promoting secondary distribution of HIV self-testing among men who have sex with men in China: study protocol for a quasi-experimental trial.

Ying Lu, Yuxin Ni, Qianyun Wang, Fengshi Jing, Yi Zhou, Xi He, Shanzi Huang, Wencan Dai, Dan Wu, Joseph D Tucker and 3 more

Abstract readClinical Trial Protocol
In one paragraph

Article in BMC public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Optimal resource allocation in HIV self-testing secondary distribution among Chinese MSM: data-driven integer programming models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022
    Article
  8. Article
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

13 authors.

Ying Lu *Dermatology Hospital of South Medical University, Guangzhou, China.
Yuxin Ni *Dermatology Hospital of South Medical University, Guangzhou, China.
Qianyun Wang *Dermatology Hospital of South Medical University, Guangzhou, China.
Fengshi Jing *Institute for Healthcare Artificial Intelligence, Guangdong Second Provincial General Hospital, Guangzhou, China.
Yi Zhou *Zhuhai Center for Diseases Control and Prevention, Zhuhai, China.
Xi HeZhuhai Xutong Voluntary Services Center, Zhuhai, China.
Shanzi HuangZhuhai Center for Diseases Control and Prevention, Zhuhai, China.
Wencan DaiZhuhai Center for Diseases Control and Prevention, Zhuhai, China.
Dan WuUniversity of North Carolina Project-China, Guangzhou, China.
Joseph D TuckerUniversity of North Carolina Project-China, Guangzhou, China.
Hongbo JiangGuangdong Pharmaceutical University, Guangzhou, China.
Liqun HuangZhuhai Center for Diseases Control and Prevention, Zhuhai, China.
Weiming TangDermatology Hospital of South Medical University, Guangzhou, China. weiming_tang@med.unc.edu.ORCID 0000-0002-9026-707X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHIV self-testing (HIVST), especially the secondary distribution of HIVST (SD-HIVST) initiated by sexual health influencers (SHIs), has been recognized as an effective strategy in promoting HIV testing, especially among men who have sex with men (MSM). This quasi-experimental study aimed to evaluate whether SHIs identified through the ensemble machine learning approach can distribute more HIVST than those who identified by the empiricalscale.

methodsWe will recruit eligible adults (≥18 years old) who were assigned male gender at birth, and willing to participate in potential SD-HIVST online. Participants will be assigned randomly to two groups (scale group or machine learning group), followed by a separate process of SHI identification based on the group assignment. After identification, all index participants (defined as identified SHIs who are verbally consented to participate in SD-HIVST or who directly order HIVST kits) will follow the same procedure for SD-HIVST acquisition and distribution. Index participants can order HIVST online and distribute them to members within their social networks (defined as alters) in-person or virtually through a personalized peer referral link. Once a unique alter uploads a photographed test result to the platform, both the alter and the corresponding index participant will receive a fixed incentive of 3 USD. The index MSM can order up to five HIVST in the first three months and ten HIVST in the following three months. Each index participant will need to complete a baseline survey at the first-time ordering and one to two follow-upbased on the times of ordering,, three months after ordering. This trial will be comparing 1) the mean number of alters motivated by each index participant in each group and 2) the mean number of newly-tested alters motivated by each index participant in each group. DISCUSSION: In promoting the efficacy of identifying SHIs for SD-HIVST, our study has the potential to enhance testing coverage, particularly among marginalized individuals and those who are reluctant to for HIV and other sexually transmitted infections.

trial registrationWe registered the study on the Chinese Clinical Trial Registry website on 4th November 2021, with registration number ChiCTR2000039632 .

Indexed as

HIV InfectionsSexual and Gender MinoritiesSexual HealthAdolescentAdultChinaHomosexuality, MaleHumansMachine LearningMaleMiddle AgedOnline Social NetworkingSelf-TestingSocial MediaYoung AdultChinaEnsemble machine learningHIV self-testingMen who have sex with menSecondary distributionSexual health influencers

Identifiers

PMID34583667
PMCPMC8480079

What OpenQuestion holds

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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.