Rocky Mountain Poison and Drug Safety, Denver Health and Hospital Authority, Denver, CO, United States.
J Med Internet Res. 2023 Sep 20;25:e46742. doi: 10.2196/46742.
The availability of central nervous system stimulants has risen in recent years, along with increased dispensing of stimulants for treatment of, for example, parent-reported attention-deficit/hyperactivity disorder in children and new diagnoses during adulthood. Typologies of drug use, as has been done with opioids, fail to include a sufficient range of behavioral factors to contextualize person-centric circumstances surrounding drug use. Understanding these patterns across drug classes would bring public health and regulatory practices toward precision public health.
The objective of this study was to quantitatively delineate the unique behavioral profiles of adults who currently nonmedically use stimulants and opioids using a latent class analysis and to contrast the differences in findings by class. We further evaluated whether the subgroups identified were associated with an increased Drug Abuse Screening Test-10 (DAST-10) score, which is an indicator of average problematic drug use.
This study used a national cross-sectional web-based survey, using 3 survey launches from 2019 to 2020 (before the COVID-19 pandemic). Data from adults who reported nonmedical use of prescription stimulants (n=2083) or prescription opioids (n=6127) in the last 12 months were analyzed. A weighted latent class analysis was used to identify the patterns of use. Drug types, motivations, and behaviors were factors in the model, which characterized unique classes of behavior.
Five stimulant nonmedical use classes were identified: amphetamine self-medication, network-sourced stimulant for alertness, nonamphetamine performance use, recreational use, and nondiscriminatory behaviors. The drug used nonmedically, acquisition through a friend or family member, and use to get high were strong differentiators among the stimulant classes. The latter 4 classes had significantly higher DAST-10 scores than amphetamine self-medication (P<.001). In addition, 4 opioid nonmedical use classes were identified: moderate pain with low mental health burden, high pain with higher mental health burden, risky behaviors with diverse motivations, and nondiscriminatory behaviors. There was a progressive and significant increase in DAST-10 scores across classes (P<.001). The potency of the opioid, pain history, the routes of administration, and psychoactive effect behaviors were strong differentiators among the opioid classes.
A more precise understanding of how behaviors tend to co-occur would improve efficacy and efficiency in developing interventions and supporting the overall health of those who use drugs, and it would improve communication with, and connection to, those at risk for severe drug outcomes.
近年来，中枢神经系统兴奋剂的供应有所增加，同时也增加了兴奋剂的配给，例如用于治疗父母报告的儿童注意力缺陷/多动障碍和成年后新诊断的疾病。与阿片类药物一样，药物使用的类型学未能包含足够广泛的行为因素来使药物使用的以人为中心的情况具有背景意义。了解这些药物类别中的模式将使公共卫生和监管实践更趋向于精准公共卫生。
本研究旨在使用潜在类别分析定量描绘当前非医疗使用兴奋剂和阿片类药物的成年人的独特行为特征，并对比不同类别的差异。我们还评估了所确定的亚组是否与增加药物滥用筛查测试-10（DAST-10）评分相关，DAST-10 评分是平均药物使用问题的指标。
本研究使用了一项全国性的横断面网络调查，使用了 2019 年至 2020 年期间的 3 次调查发布（在 COVID-19 大流行之前）。分析了过去 12 个月内报告非医疗使用处方兴奋剂（n=2083）或处方阿片类药物（n=6127）的成年人的数据。使用加权潜在类别分析来确定使用模式。药物类型、动机和行为是模型中的因素，它们描述了独特的行为类别。
确定了 5 种非医疗使用兴奋剂的类别：安非他命自我治疗、网络来源的警觉兴奋剂、非安非他命性能使用、娱乐性使用和无差别行为。非医疗使用的药物、通过朋友或家人获得以及用于获得快感是兴奋剂类别之间的重要区别因素。后 4 类的 DAST-10 评分明显高于安非他命自我治疗（P<.001）。此外，还确定了 4 种非医疗使用阿片类药物的类别：中度疼痛伴低心理健康负担、高疼痛伴较高心理健康负担、具有不同动机的风险行为和无差别行为。各类别之间的 DAST-10 评分呈逐渐显著增加（P<.001）。阿片类药物的效力、疼痛史、给药途径和精神活性效应行为是阿片类药物类别之间的重要区别因素。
更准确地了解行为倾向如何共同发生，将提高制定干预措施的效果和效率，并支持那些使用药物的人的整体健康，并改善与那些有严重药物后果风险的人的沟通和联系。
