Injection network drivers of HIV prevention service utilization among people who inject drugs: results of a community-based sociometric network cohort in New Delhi, India.

INTRODUCTION: Injection drug networks may influence their network members' health-seeking behaviours. Using data from a sociometric injecting partner network of people who inject drugs (PWID) in New Delhi, India, we assessed the role of injecting partner (alter) behaviours on individual engagement in HIV prevention services. METHODS: We enumerated injecting partner linkages among 2512 PWID using coupon referrals and biometric data from November 2017 to March 2020. Participants completed interviewer-administered questionnaires and provided information on injection behaviours, injecting partners, HIV/hepatitis C (HCV) testing and service engagement. Multilevel multiple-membership models (MMMM) evaluated individual PWID HIV testing, medication for opioid use disorder (MOUD) and syringe service engagement as a function of alter attributes, accounting for membership across multiple ego-networks. Logistic regression models assessed parallel associations among socially proximal injecting peers, defined as PWID ≤3 path length from ego. RESULTS: Median age was 26 years; 99% were male. PWID had median 2 injecting partners and 8 socially proximal peers; 14% reported HIV testing, 33% accessed MOUD and 13% used syringe services 6 months prior. In MMMM analyses, PWID with ≥1 versus 0 injecting partners who received HIV testing were significantly more likely to report HIV testing (adjusted odds ratio [aOR]: 2.27, 95% confidence interval [CI]: 1.68-3.16), MOUD (aOR: 1.99, 95% CI: 1.60-2.53) and syringe service use (aOR: 1.66, 95% CI: 1.21-2.39). We observed similar findings for individual MOUD and syringe service use. Having ≥1 versus 0 HIV-positive partners was associated with decreased HIV testing and MOUD but increased syringe service use (aOR: 1.54, 95% CI: 1.09-2.17). PWID with ≥1 versus 0 socially proximal peers who used non-sterile injection equipment reported increased HIV testing (aOR: 1.39, 95% CI: 1.01-1.92), MOUD (aOR: 1.40, 95% CI: 1.10-1.77) and syringe service use (aOR: 1.82, 95% CI: 1.23-2.68). CONCLUSIONS: We found differential associative relationships between individual HIV prevention service engagement and the health or risk behaviours of direct and indirect alters. Characterizing network exposure beyond direct injecting partnerships provided important context on possible mechanisms of behavioural influence. Findings could be leveraged to design peer-based interventions that promote network diffusion of health-seeking behaviours.

Network centrality and HIV prevention service use among people who inject drugs: Findings from a sociometric network cohort in New Delhi, India.

BACKGROUND AND AIMS: Network centrality, an indicator of an individual's importance and potential to drive behavioral change, is rarely used to select peer educators. Individual-level predictors of network centrality might be useful to identify people who inject drugs (PWID) for potential roles as peer navigators or change agents in network-based interventions in settings where sociometric data are unavailable. We assessed the relationship between network centrality and HIV prevention service engagement to determine whether centrally-positioned PWID share measurable commonalities. DESIGN: Observational study and survey using baseline data from a sociometric network cohort of PWID, enumerated using network software and biometric data (2017-2020). Network ties corresponded to direct injection partnerships in the prior month. SETTING: New Delhi, India. PARTICIPANTS: A total of 2512 PWID who were ≥18 years, provided written informed consent, and reported illicit injection drug use within the 24 months before study enrollment. MEASUREMENTS: Interviewer-administered questionnaires measured demographics and substance use behaviors. Central versus peripheral network position was categorized using betweenness centrality 75th%ile . Logistic regression was used to estimate adjusted odds ratios (aOR) with 95% confidence intervals (95%CI) between network position and HIV testing, medication for opioid use disorder (MOUD), or syringe service use. Lasso models selected predictors of central network position among 20 covariates detailing demographic, biologic, and substance use information. Predictive accuracy was evaluated using model performance metrics. FINDINGS: Overall, median age was 26 years (interquartile range 22-34); 99% were male; 628 were classified as central. Compared with PWID at the periphery, central PWID were more likely to use MOUD (aOR: 1.59, 95%CI: 1.30-1.94) and syringe services (aOR: 2.91, 95%CI: 2.25, 3.76) in the prior six months. Findings for HIV testing were inconclusive (aOR: 1.30, 95%CI: 1.00-1.69). The lasso variable selector identified several predictors of network centrality: HIV and hepatitis C infection, number of PWID seen in the prior month, injecting heroin and buprenorphine (vs. heroin only) six months prior, sharing injection equipment six months prior, experiencing drug overdose in the past year, and moderate/severe depression (vs. none/mild). Average agreement between model-predicted vs. observed values was 0.75; area under the receiver operator curve was 0.69. CONCLUSIONS: In a socioeconomic network of people who inject drugs (PWID) in New Delhi, India, there are common characteristics among individuals based on their network position (central vs. peripheral) but individual-level predictors have only moderate predictive accuracy. Although central network members appear to be more likely to use HIV prevention services than peripheral network members, their potential as change agents may be limited by other factors that impede their ability to adopt or promote HIV prevention service use.

Deep learning and social network analysis elucidate drivers of HIV transmission in a high-incidence cohort of people who inject drugs.

Globally, people who inject drugs (PWID) experience some of the fastest-growing HIV epidemics. Network-based approaches represent a powerful tool for understanding and combating these epidemics; however, detailed social network studies are limited and pose analytical challenges. We collected longitudinal social (injection partners) and spatial (injection venues) network information from 2512 PWID in New Delhi, India. We leveraged network analysis and graph neural networks (GNNs) to uncover factors associated with HIV transmission and identify optimal intervention delivery points. Longitudinal HIV incidence was 21.3 per 100 person-years. Overlapping community detection using GNNs revealed seven communities, with HIV incidence concentrated within one community. The injection venue most strongly associated with incidence was found to overlap six of the seven communities, suggesting that an intervention deployed at this one location could reach the majority of the sample. These findings highlight the utility of network analysis and deep learning in HIV program design.