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Community-, network-, and individual-level predictors of uptake of medication for opioid use disorder among young people who inject drugs and their networks: A multilevel analysis. | LitMetric

Background: Opioid use has been increasing at alarming rates over the past 15 years, yet uptake of medication for opioid use disorder (MOUD) remains low. Much of the research on individual characteristics predicting MOUD uptake is equivocal, and there is a dearth of research on setting-level and network-level characteristics that predict MOUD uptake. Towards a more holistic, multilevel understanding, we explore individual-level, network-level, and community-level characteristics associated with MOUD uptake.

Methods: Baseline data from a longitudinal study of young people who inject drugs and their injection and support network members living in Chicago (N = 165) was used to conduct cross-sectional multilevel logistic regression analyses to examine associations between MOUD uptake and a set of potential predictors at the individual-, network-, and community-levels that were chosen based on theoretical relevance or support from previous empirical studies.

Results: Stigma at both the individual and community levels was significantly associated with MOUD uptake (though in different directions). Greater individual-level stigma was associated with a higher likelihood of MOUD uptake, while having a more normatively stigmatizing community environment was associated with a lower likelihood of MOUD uptake. Using heroin and cocaine simultaneously and having a larger support network were associated with a greater likelihood of MOUD uptake.

Conclusions: The present study's holistic, multilevel approach identified three individual-level characteristics, one network-level characteristic, and one community-level characteristic associated with MOUD uptake. However, more research is needed examining multilevel predictors, to help with developing interventions addressing barriers to MOUD use at multiple levels of influence.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10041678PMC
http://dx.doi.org/10.1016/j.drugalcdep.2023.109782DOI Listing

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