目的:本研究试图开发和评估一个探索性模型,和药物使用或获取行为相互作用,影响阿片类药物参与过量。
方法:我们进行了探索性和验证性因子分析(EFA/CFA),以确定十种药物获取和使用行为的因子结构。然后,我们评估了包含已识别因素的替代结构方程模型,增加人口统计学和社会心理属性作为过去一年阿片类药物过量的预测因素。
方法:我们使用了两项研究的访谈数据,这些研究招募了滥用阿片类药物的参与者,这些参与者接受了基于社区的注射器服务计划的服务。第一个调查了当前对药物检查的态度(N=150)。第二个是RCT评估远程医疗与现场医疗预约阿片类药物使用障碍治疗转诊(N=270)。
方法:人口统计包括性别,年龄,种族/民族,教育,和社会经济地位。心理社会措施是无家可归,心理困扰,和创伤。自我报告的药物相关危险行为包括单独使用,有了新的供应商,使用阿片类药物与苯二氮卓类药物/酒精,更喜欢芬太尼。过去一年涉及阿片类药物的过量被分为没有或任何经历。
结果:EFA/CFA揭示了双因素结构,其中一个因素反映了药物获取和第二个药物使用行为。所选择的模型(CFI=.984,TLI=.981,RMSEA=.024)占过量剂量概率方差的13.1%。代表心理社会属性的潜在变量与过去一年的过量用药概率的增加间接相关(β=.234,p=.001),由EFA/CFA介导,确定了潜在变量:药物获取(β=.683,p<.001)和药物使用(β=.567,p=.001)。药物使用行为(β=.287,p=.04)而非药物获取行为(β=.105,p=.461)也具有显著,对过去一年过量的积极直接影响。没有人口统计学特征是显着的直接或间接用药过量预测因素。
结论:心理社会属性,尤其是无家可归,通过与危险药物获取和药物使用行为的关联增加过量的可能性。需要进一步的研究来在阿片类药物相关过量的高风险人群中复制这些发现,以评估普遍性并完善用于评估心理社会特征的指标。
OBJECTIVE: This study sought to develop and assess an exploratory model of how demographic and psychosocial attributes, and drug use or acquisition behaviors interact to affect opioid-involved overdoses.
METHODS: We conducted exploratory and confirmatory factor analysis (EFA/CFA) to identify a factor structure for ten drug acquisition and use behaviors. We then evaluated alternative structural equation models incorporating the identified factors, adding demographic and psychosocial attributes as predictors of past-year opioid overdose.
METHODS: We used interview data collected for two studies recruiting opioid-misusing participants receiving services from a community-based syringe services program. The first investigated current attitudes toward drug-checking (N = 150). The second was an RCT assessing a telehealth versus in-person medical appointment for opioid use disorder treatment referral (N = 270).
METHODS: Demographics included gender, age, race/ethnicity, education, and socioeconomic status. Psychosocial measures were homelessness, psychological distress, and trauma. Self-reported drug-related risk behaviors included using alone, having a new supplier, using opioids with benzodiazepines/alcohol, and preferring fentanyl. Past-year opioid-involved overdoses were dichotomized into experiencing none or any.
RESULTS: The EFA/CFA revealed a two-factor structure with one factor reflecting drug acquisition and the second drug use behaviors. The selected model (CFI = .984, TLI = .981, RMSEA = .024) accounted for 13.1% of overdose probability variance. A latent variable representing psychosocial attributes was indirectly associated with an increase in past-year overdose probability (β = .234, p = .001), as mediated by the EFA/CFA identified latent variables: drug acquisition (β = .683, p < .001) and drug use (β = .567, p = .001). Drug use behaviors (β = .287, p = .04) but not drug acquisition (β = .105, p = .461) also had a significant, positive direct effect on past-year overdose. No demographic attributes were significant direct or indirect overdose predictors.
CONCLUSIONS: Psychosocial attributes, particularly homelessness, increase the probability of an overdose through associations with risky drug acquisition and drug-using behaviors. Further research is needed to replicate these findings with populations at high-risk of an opioid-related overdose to assess generalizability and refine the metrics used to assess psychosocial characteristics.