A connectivity model for assessment of HIV transmission risk in injection drug users (IDUs)

Flaer, Paul J.; Cistone, Peter J.; Younis, Mustafa Z; Parkash, Jyoti

doi:10.1016/j.evalprogplan.2013.03.004

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…Our findings respond to previous cross-sectional work which found increased HIV risk was associated with larger sized drug networks (Cepeda et al, 2011) and more severe drug use patterns (Flaer et al, 2013), suggesting such substance use dynamics might afford greater opportunities for networks to introduce safer injection practices. Frequency of contact with the index or greater interconnectivity (e.g., friendships) between network members did not predict changes in a network’s injection practices, despite previous cross-sectional work reporting associations between these network factors and HIV risk behaviors (Li et al, 2012; Flaer et al, 2013; Shahesmaeili et al, 2014).…”

Section: Discussionsupporting

confidence: 82%

“…Frequency of contact with the index or greater interconnectivity (e.g., friendships) between network members did not predict changes in a network’s injection practices, despite previous cross-sectional work reporting associations between these network factors and HIV risk behaviors (Li et al, 2012; Flaer et al, 2013; Shahesmaeili et al, 2014). This discrepancy may suggest a mediated network process where such social interaction factors indirectly promote changes in injection practices over time, as a mechanism through which new social norms lead to changes in injection practices (Latkin et al, 2013).…”

Section: Discussionmentioning

confidence: 64%

“…The available literature on injection networks have identified associations between individual network members’ injection risk, HIV testing, and HIV serostatus and network factors such as alcohol and poly-substance use (crack cocaine) patterns (Latkin et al, 1996), levels of social support (Latkin et al, 2011) and trust among network members (Cepeda et al, 2011), network size (Cepeda et al, 2011), connectivity between network members or proximity to the index (Li et al, 2012; Flaer et al, 2013; Shahesmaeili et al, 2014), and stability of network member composition (Hoffmann et al, 1997; Costenbader et al, 2006; Cepeda et al, 2011). To our knowledge, these network factors have not been studied as potential predictors of change in injection risk practices at the network-level.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating network-level predictors of behavior change among injection networks enrolled in the HPTN 037 randomized controlled trial

Smith

Strathdee

Metzger

et al. 2017

Drug and Alcohol Dependence

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Background Little is known about ways network-level factors that may influence the adoption of combination prevention behaviors among injection networks, or how network-oriented interventions might moderate this behavior change process. Methods A total of 232 unique injection risk networks in Philadelphia, PA, were randomized to a peer educator network-oriented intervention or standard of care control arm. Network-level aggregates reflecting the injection networks’ baseline substance use dynamics, social interactions, and the networks exposure to gender- and structural-related vulnerabilities were calculated and used to predict changes in the proportion of network members adopting safer injection practices at 6-month follow-up. Results At follow-up, safer injection practices were observed among 46.31% of a network’s members on average. In contrast, 25.7% of networks observed no change. Controlling for the effects of the intervention, significant network-level factors influencing network-level behavior change reflected larger sized injection networks (b=2.20, p=.013) with a greater proportion of members who shared needles (b=0.29, p<.001) and engaged in poly drug use at baseline (b=6.65, p=.021). Changes in a network’s safer injection practices were also observed for networks with fewer new network members (b=−0.31, p=.008), and for networks whose members were proportionally less likely to have experienced incarceration (b=−0.20, p=.012) or more likely to have been exposed to drug treatment (b=0.17, p=.034) in the 6-months prior to baseline. A significant interaction suggested the intervention uniquely facilitated change in safer injection practices among female-only networks (b=−0.32, p=.046). Conclusions Network-level factors offer insights into ways injection networks might be leveraged to promote combination prevention efforts.

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