Abstract
AIMS: 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. DESIGN: 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. SETTING AND PARTICIPANTS: 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). MEASUREMENTS: 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. FINDINGS: 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.