Evidence map›Paper›PMID 29323225›Full record

ArticleScientific reports2018

Frequent implementation of interventions may increase HIV infections among MSM in China.

Xiaodan Sun, Yanni Xiao, Zhihang Peng, Ning Wang

Abstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. History of the HIV Epidemic in China.Current HIV/AIDS reports · 2019
    Review
  7. 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

4 authors.

Xiaodan SunDepartment of Applied Mathematics, Xi'an Jiaotong University, Xi'an, 710049, China.
Yanni XiaoDepartment of Applied Mathematics, Xi'an Jiaotong University, Xi'an, 710049, China. yxiao@mail.xjtu.edu.cn.
Zhihang PengDepartment of Epidemiology and Biostatistics, Nanjing Medical University, Nanjing, 210029, China.
Ning WangNational Center for AIDS/STD Prevention and Control, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intervention measures among men who have sex with men (MSM) are usually designed to reduce the frequency of high risk behaviors (within-community level), but unfortunately may change the contact network and consequently increase the opportunity for them to have sex with new partners (between-community level). A multi-community periodic model on complex network is proposed to study the two-side effects of interventions on HIV transmission among MSM in China, in which the wanning process of the impacts of interventions are modelled. The basic reproduction number for the multi-community periodic system is defined and calculated numerically. Based on the number of annual reported HIV/AIDS cases among MSM in China, the unknown parameters are estimated by using MCMC method and the basic reproduction number is estimated as 3.56 (95%CI [3.556, 3.568]). Our results show that strong randomness of the community-connection networks leads to more new infections and more HIV/AIDS cases. Moreover, main conclusion indicates that implementation of interventions may induce more new infections, depending on relative level of between- and within-community impacts, and the frequency of implementation of interventions. The findings can help to guide the policy maker to choose the appropriate intervention measures, and to implement the interventions with proper frequency.

Indexed as

Homosexuality, MaleRisk-TakingAlgorithmsChinaCommunity NetworksHIV InfectionsHumansMaleModels, Theoretical

Identifiers

PMID29323225
PMCPMC5765000

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.