The International journal on drug policy · 2025
Changing Trends in Nonmedical Gabapentin Use Among a Cohort of Rural Appalachian People Who Use Drugs: A Longitudinal Study Over the Time Period of Scheduling Changes in Kentucky.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Cook KaraLynne, Havens Jennifer R, Slavova Svetla, Thompson Katherine, Young April M, Vickers-Smith Rachel A
Аннотация
BACKGROUND: Recently, nonmedical gabapentin (Neurontin) use has increased in the United States, leading to its classification as schedule V substance in Kentucky in 2017. This paper examines patterns in nonmedical gabapentin (Neurontin) use in people who use drugs (PWUD) over the time of scheduling changes.
METHODS: Longitudinal data (2008-2020) from the Social Networks among Appalachian People (SNAP) study were analyzed. SNAP participants (n = 503) met criteria of being ≥ 18 years old, residing in Appalachian Kentucky, and reporting recent nonmedical prescription opioid, methamphetamine, cocaine, or heroin use. A mixed-effects logistic regression model assessed changes in nonmedical gabapentin (Neurontin) use over time. Independent variables included baseline demographics and time-varying measures of age, disability status, insurance, and recent nonmedical substance use. Changes in gabapentin (Neurontin) sourcing before and after scheduling were assessed using chi-square and McNemar's tests.
RESULTS: Nonmedical gabapentin (Neurontin) use increased significantly (aOR = 3.51, 95 % CI: 2.91-4.24), from 0 % in 2008 to 56 % in 2020. By 2020, gabapentin (Neurontin) use exceeded that of prescription opioids. Before scheduling, most (55 %) participants obtained gabapentin (Neurontin) from regulated sources; afterward, this dropped to one-third (p < 0.001). A within-person analysis confirmed a significant decline in regulated sources (p = 0.01).
CONCLUSIONS: Nonmedical gabapentin (Neurontin) use continued to rise post-scheduling, with sources shifting from regulated to unregulated. These findings highlight potential unintended consequences of scheduling policies on access and distribution.
METHODS: Longitudinal data (2008-2020) from the Social Networks among Appalachian People (SNAP) study were analyzed. SNAP participants (n = 503) met criteria of being ≥ 18 years old, residing in Appalachian Kentucky, and reporting recent nonmedical prescription opioid, methamphetamine, cocaine, or heroin use. A mixed-effects logistic regression model assessed changes in nonmedical gabapentin (Neurontin) use over time. Independent variables included baseline demographics and time-varying measures of age, disability status, insurance, and recent nonmedical substance use. Changes in gabapentin (Neurontin) sourcing before and after scheduling were assessed using chi-square and McNemar's tests.
RESULTS: Nonmedical gabapentin (Neurontin) use increased significantly (aOR = 3.51, 95 % CI: 2.91-4.24), from 0 % in 2008 to 56 % in 2020. By 2020, gabapentin (Neurontin) use exceeded that of prescription opioids. Before scheduling, most (55 %) participants obtained gabapentin (Neurontin) from regulated sources; afterward, this dropped to one-third (p < 0.001). A within-person analysis confirmed a significant decline in regulated sources (p = 0.01).
CONCLUSIONS: Nonmedical gabapentin (Neurontin) use continued to rise post-scheduling, with sources shifting from regulated to unregulated. These findings highlight potential unintended consequences of scheduling policies on access and distribution.