Evidence map›Paper›PMID 41174360›Full record

ArticleSubstance use & misuse2026

Evaluation of the Relationship Between Network Centrality and Individual Sociodemographics and Behaviors Among People Who Inject Drugs.

Benjamin Skov, Ashley L Buchanan, Natallia V Katenka, Tasmin Hoque Nadia, Samuel R Friedman, Georgios K Nikolopoulos

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Article in Substance use & misuse, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Benjamin SkovDepartment of Pharmacy Practice and Clinical Research, University of Rhode Island, Kingston, Rhode Island, USA.
Ashley L BuchananDepartment of Pharmacy Practice and Clinical Research, University of Rhode Island, Kingston, Rhode Island, USA.ORCID 0000-0002-4715-3456
Natallia V KatenkaDepartment of Computer Science and Statistics, University of Rhode Island, Kingston, Rhode Island, USA.
Tasmin Hoque NadiaDepartment of Pharmacy Practice and Clinical Research, University of Rhode Island, Kingston, Rhode Island, USA.
Samuel R FriedmanDepartment of Population Health, New York University Grossman School of Medicine, New York, New York, USA.
Georgios K NikolopoulosMedical School, University of Cyprus, Nicosia, Cyprus.

Funding

Transdisciplinary Theoretical Svnthesis and Development CoreP30DA011041 · NIDA · NEW YORK UNIVERSITY · PI Holly Hagan, Courtney McKnight · 1998 to 2026
$38.9M
Preventing HIV Transmission by Recently-Infected Drug UsersDP1DA034989 · NIDA · NATIONAL DEVELOPMENT AND RESEARCH INSTITUTES, INC. · PI FRIEDMAN, SAMUEL R · 2012 to 2016
$3.8M
Causal Inference Methods for HIV Prevention Studies Among Networks of People Who Use DrugsDP2DA046856 · NIDA · UNIVERSITY OF RHODE ISLAND · PI BUCHANAN, ASHLEY LYNN · 2018 to 2019
$2.3M
NIDA NIH HHS DP1 DA034989NIDA NIH HHS DP2 DA046856NIDA NIH HHS P30 DA011041
6 · The paper itself

Abstract

purposeUnderstanding the types of individuals and their position in the network may improve interventions for people who inject drugs (PWID).

methodsFrom the Transmission Reduction Intervention Project (TRIP), which enrolled PWID and their contacts in Athens, Greece, from 2013 to 2015, we extracted the largest connected component of the network (i.e., the largest group of connected individuals) and identified members who were in the top quartile of the distribution for three network centrality measures: closeness, betweenness, and eigenvector. Using logistic regression, we evaluated associations between high centrality measures and individual sociodemographic characteristics and behaviors. We also varied the definition for high centrality.

resultsAmong 231 individuals, 80% were male and between the ages of 25-40 years. Over half of the individuals injected at least once per day, compared to less than daily. Individuals who injected at least once per day were more likely to have high closeness (odds ratio (OR) = 3.36; 95% confidence interval (CI) = 1.57, 8.42), high betweenness (OR = 2.22; 95% CI = 1.06, 4.67), and eigenvector centrality (OR = 4.50; 95% CI = 1.89,10.68). Individuals who engaged in sex without a condom were less likely to have high closeness (OR = 0.18; 95% CI = 0.07, 0.45) or eigenvector (OR = 0.19; 95% CI = 0.07, 0.49) centrality.

conclusionsIndividual characteristics and behaviors were associated with centrality and may impact an individual's position in the network. These associations could be useful in identifying important community members to engage as part of public health initiatives.

Indexed as

Social NetworkingSubstance Abuse, IntravenousAdultFemaleGreeceHumansMaleRisk-TakingSociodemographic FactorsYoung AdultHIV riskHIV risk behaviorsHIV risk networksinjection drug useNetwork centralitypeople who inject drugs

Identifiers

PMID41174360
PMCPMC13023018

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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.