Typologies of prescription opioid use in a large sample of adults assessed for substance abuse treatment.
Typologies of prescription opioid use in a large sample of adults assessed for substance abuse treatment.
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DOI：
10.1371/journal.pone.0027244
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Butler SF
中科院分区：
文献类型：
--
作者：
Green TC;Black R;Grimes Serrano JM;Budman SH;Butler SF
作为一个群体，非医疗处方阿片类药物使用者的定义并不明确。我们的目的是通过对正在评估药物滥用治疗的成年人群进行潜在类别分析，得出并描述处方阿片类药物使用和非医疗使用的类型。潜在类别分析适用于来自 26,314 名年龄在 18-70 岁之间的独立受访者的数据，这些受访者自我报告了过去一个月使用处方阿片类药物的情况，而这些数据是由成瘾严重程度指数多媒体版本 (ASI-MV®)（用于近实时处方阿片类药物滥用监测的国家数据库）收集的总共 138,928 例 (18.9%) 中的数据。数据获取时间为 2005 年 11 月至 2009 年 12 月。美国的药物滥用治疗、刑事司法和公共援助项目向 ASI-MV 数据库提交了数据 (n = 538)。潜在类别的六个指标源自对 ASI-MV 的反应，ASI-MV 是 ASI 的一个版本，经过修改，用于收集处方阿片类药物滥用和慢性疼痛经历。潜在类别分析包括受访者家庭邮政编码的随机效应，以解释受访者在邮政编码内的嵌套情况。四类调整后的潜在类别模型最适合并定义了临床上可解释的相关亚组：按规定使用、规定滥用者、医疗健康滥用者和非法使用者。班级的关键变量各不相同，包括种族/民族、性别、同时滥用药物、处方阿片类药物滥用的持续时间、心理健康问题和 ASI 综合评分。四类中的三类（81％的受访者）表现出致命的阿片类药物过量的潜在高风险； 18.4% 表现出血源性感染的危险因素。检测到处方阿片类药物使用的多种且不同的情况，表明一系列使用类型具有不同的不良事件风险。研究结果可能有助于临床医生和政策制定者更好地关注过量用药和血源性感染的预防工作以及减少处方阿片类药物滥用的干预策略。
As a population, non-medical prescription opioid users are not well-defined. We aimed to derive and describe typologies of prescription opioid use and nonmedical use using latent class analysis in an adult population being assessed for substance abuse treatment. Latent class analysis was applied to data from 26,314 unique respondents, aged 18-70, self-reporting past month use of a prescription opioid out of a total of 138,928 cases (18.9%) collected by the Addiction Severity Index-Multimedia Version (ASI-MV®), a national database for near real-time prescription opioid abuse surveillance. Data were obtained from November 2005 through December 2009. Substance abuse treatment, criminal justice, and public assistance programs in the United States submitted data to the ASI-MV database (n = 538). Six indicators of the latent classes derived from responses to the ASI-MV, a version of the ASI modified to collect prescription opioid abuse and chronic pain experience. The latent class analysis included respondent home ZIP code random effects to account for nesting of respondents within ZIP code. A four-class adjusted latent class model fit best and defined clinically interpretable and relevant subgroups: Use as prescribed, Prescribed misusers, Medically healthy abusers, and Illicit users. Classes varied on key variables, including race/ethnicity, gender, concurrent substance abuse, duration of prescription opioid abuse, mental health problems, and ASI composite scores. Three of the four classes (81% of respondents) exhibited high potential risk for fatal opioid overdose; 18.4% exhibited risk factors for blood-borne infections. Multiple and distinct profiles of prescription opioid use were detected, suggesting a range of use typologies at differing risk for adverse events. Results may help clinicians and policy makers better focus overdose and blood-borne infection prevention efforts and intervention strategies for prescription opioid abuse reduction.
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