Evidence map›Paper›PMID 29321039›Full record

ArticleSubstance abuse treatment, prevention, and policy2018

Strategies to control HIV and HCV in methadone maintenance treatment in Guangdong Province, China: a system dynamic modeling study.

Xia Zou, Yong Xu, Wen Chen, Yinghua Xia, Yin Liu, Cheng Gong, Li Ling

Open access · goldAbstract read
In one paragraph

Article in Substance abuse treatment, prevention, and policy, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 2 pooled it
1.9field-weighted citation impact, top 14% of its field
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, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.

  1. Pooled it
  2. A System Dynamics Simulation Applied to Healthcare: A Systematic Review.International journal of environmental research and public health · 2020
    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

7 authors at 1 institution in 1 country.

Xia ZouFaculty of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China.
Yong XuFaculty of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China.
Wen ChenFaculty of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China.
Yinghua XiaFaculty of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China.
Yin LiuFaculty of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China.
Cheng GongFaculty of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China.
Li LingFaculty of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China. lingli@mail.sysu.edu.cn.
Sun Yat-sen University · CN

Funding

China Medical Board (US) 12-111National Natural Science Foundation of China 71173245National Natural Science Foundation of China 81473065
6 · The paper itself

Abstract

backgroundHuman immunodeficiency virus (HIV) and hepatitis C virus (HCV) infections among methadone maintenance treatment (MMT) participants remain high. Optimized HIV and HCV prevention strategies for MMT clinics in resource-limited regions are urgently needed. This study aims to develop an MMT system dynamic model (SDM) to compare and optimize HIV and HCV control strategies in the MMT system.

methodsWe developed an MMT-SDM structure based on literature reviews. Model parameters were estimated from a cohort study, cross-sectional surveys and literature reviews. We further calibrated model outputs to historical data of HIV and HCV prevalence among MMT participants in 13 MMT clinics of Guangdong Province. Lastly, we simulated the impact of integrated interventions on HIV and HCV incidence among MMT participants using the MMT-SDM.

resultsThe MMT-SDM comprises MMT clinics, MMT participants, detoxification centers, and HIV and HCV transmission, testing and treatment systems. We determined that condom promotion was the most effective way to reduce HIV infection (2013-2020: 2.86% to 1.76%) in MMT setting, followed by needle exchange program (2013-2020: 2.86% to 2.56%), psychological counseling (2013-2020: 2.86% to 2.71%) and contingency management (2013-2020: 2.86% to 2.72%). Health education had marginal impact on reducing HIV incidence among MMT participants (2013-2020:2.86% to 2.84%) from 2013 to 2020. By contrast, psychological counseling (2013-2020: 7.54% to 2.42%) and contingency management (2013-2020: 7.54% to 2.96%) had been shown to be the most effective interventions to reduce HCV incidence among MMT participants, followed by needle exchange program (2013-2020: 7.54% to 5.76%), health education (2013-2020: 7.54% to 6.35%), and condom promotion program (2013-2020: 7.54% to 6.40%). Notably, HCV treatment reduced HCV incidence by 0.32% (2013-2020: 7.54% to 7.22%).

conclusionsIn conclusion, we generated a valuable system dynamic model to analyze the Chinese MMT system and to guide the decision-making process to further improve this system. This study underscores the importance of promoting condom use in MMT clinics and integrating psychosocial interventions to reduce HIV and HCV infections in MMT clinics in China.

Indexed as

ChinaCross-Sectional StudiesHepatitis CHIV InfectionsHumansMethadoneModels, BiologicalOpiate Substitution TreatmentPrevalencePrimary PreventionSubstance Abuse, IntravenousMethadoneHepatitis CHuman immunodeficiency virusMethadone maintenance treatmentSystem dynamics

Identifiers

PMID29321039
PMCPMC5763976
OpenAlexW2783794453

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.