Evidence map›Paper›PMID 41610680›Full record

ArticleThe International journal on drug policy2026

Gender differences in social networks of women and men who inject drugs in Kenya.

Hannah N Manley, Lindsey R Riback, Mercy Nyakowa, Chenshu Zhang, Peter Vickerman, Jack Stone, Josephine G Walker, Rose Wafula, Nazila Ganatra, Matthew J Akiyama

Abstract read
In one paragraph

Article in The International journal on drug policy, 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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2 · The registry

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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

10 authors.

Hannah N ManleyAlbert Einstein College of Medicine, Bronx, NY, United States.
Lindsey R RibackAlbert Einstein College of Medicine, Bronx, NY, United States.
Mercy NyakowaKenya Ministry of Health, National AIDS and STI Control Programme (NASCOP), Nairobi, Kenya.
Chenshu ZhangAlbert Einstein College of Medicine, Bronx, NY, United States.
Peter VickermanUniversity of Bristol, Bristol, United Kingdom.
Jack StoneUniversity of Bristol, Bristol, United Kingdom.
Josephine G WalkerUniversity of Bristol, Bristol, United Kingdom.
Rose WafulaKenya Ministry of Health, National AIDS and STI Control Programme (NASCOP), Nairobi, Kenya.
Nazila GanatraKenya Ministry of Health, National AIDS and STI Control Programme (NASCOP), Nairobi, Kenya.
Matthew J AkiyamaAlbert Einstein College of Medicine, Bronx, NY, United States. Electronic address: matthew.akiyama@einsteinmed.edu.

Funding

Leveraging HCV Phylogenetic Networks to Prevent HIV and Other Blood Borne Infections Among People Who Inject DrugsDP2DA053730 · NIDA · ALBERT EINSTEIN COLLEGE OF MEDICINE · PI AKIYAMA, MATTHEW · 2021 to 2021
$2.5M
NIDA NIH HHS DP2 DA053730Wellcome TrustWellcome Trust WT 226619/Z/22/Z]
6 · The paper itself

Abstract

backgroundWomen who inject drugs (WWID) face heightened HCV and HIV risk compared to men (MWID); yet underlying differences in social and injection networks are not well understood, particularly in resource-limited countries.

methodsWe enrolled 3152 people who inject drugs (PWID) in Kenya using respondent driven sampling. Participants were tested for HIV, HCV, and HBV, and completed a survey with behavioral and network questions. An individual's social network members (NMs) were defined as participants they had used any drugs with and knew by name. Bivariate analyses were conducted for associations between gender, network characteristics, and injection behaviors. Multivariate logistic regressions were conducted for both men and women to examine the associations between network characteristics and disease status.

resultsAbout one-tenth of participants were women (9.7%, N= 306), 19.3% were HCV antibody-positive (N = 610), and 9.7% were HIV-positive (N = 306). Women were significantly more likely to be HCV antibody-positive (26.5% vs. 18.6%, p < 0.001) and HIV-positive (31.1% vs. 7.4%, p < 0.001) than men. Women were significantly more likely to have injected and had sex with people in their networks than men were (3.9% vs. 1.0%, p < 0.001). Knowledge (or lack thereof) of NM's HIV and HCV status had significant associations with HIV and HCV positivity for both women and men.

conclusionWomen face differential HCV- and HIV-related risk in part based upon their relationships to and behaviors with their network members. Further research examining nuances of injection and sexual relationships among women and men who inject drugs is needed. Interventions accounting for gender-based risks should also be considered.

Indexed as

Hepatitis CHIV InfectionsSocial SupportSubstance Abuse, IntravenousAdolescentAdultFemaleHepatitis BHumansKenyaLogistic ModelsMaleMiddle AgedRisk-TakingSex FactorsSocial GroupGenderhepatitis CHIVInjection drug useSocial networks

Identifiers

PMID41610680
PMCPMC13533548

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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.