Correlates of nonfatal overdose among treatment-seeking individuals with non-heroin opioid use disorder: Findings from a pragmatic, pan-Canadian, randomized control trial

Crepeault, Hannah; Ti, Lianping; Jutras-Aswad, Didier; Wood, Evan; Le Foll, Bernard; Lim, Ron; Bach, Paxton; Brar, Rupinder; Socias, M. Eugenia

doi:10.1016/j.josat.2023.209158

Cited by 5 publications

(4 citation statements)

References 45 publications

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“…A prior study of non-fatal overdose risks among opioid users seeking treatment also found that those with “markers of socio-structural marginalization” had a higher overdose risk [ 25 ]. Our results replicate this finding and suggest a mechanism for how being socially marginalized increases overdose risk.…”

Section: Discussionmentioning

confidence: 99%

“…The nature of how these factors interact to increase overdose risk, however, has largely been unexplored. Studies of multiple risk factors have typically examined them in the context of multiple regression models which, though illuminating, do not analytically consider how independent risk factors might cluster or influence each other to affect overdose probability [ 25 , 26 ]. Thus, there remains a question as to why one opioid user experiences one or more unintentional overdoses over time, whereas another user experiences no or fewer overdoses.…”

Section: Introductionmentioning

confidence: 99%

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Associations among drug acquisition and use behaviors, psychosocial attributes, and opioid-involved overdoses

Swartz,

Zhao,

Jacobucci

et al. 2024

BMC Public Health

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Associations among drug acquisition and use behaviors, psychosocial attributes, and opioid-involved overdoses

Swartz,

Zhao,

Jacobucci

et al. 2024

BMC Public Health

View full text Add to dashboard Cite

show abstract

“…The nature of how these factors interact to increase overdose risk, however, has largely been unexplored. Studies of multiple risk factors have typically examined them in the context of multiple regression models which, though illuminating, do not analytically consider how independent risk factors might cluster or influence each other to affect overdose probability ( 23 , 24 ). Thus, there remains a question as to why one opioid user experiences one or more unintentional overdoses over time, whereas another user experiences no or fewer overdoses.…”

Section: Introductionmentioning

confidence: 99%

“…Additional studies and systematic reviews have identi ed factors other than gender and drug use behaviors that are associated with increased opioid-involved overdose and fatality risk. Some of the identi ed categorical risk factors include: systemic (e.g., stigma); psychosocial (e.g., unstably housed or homeless, serious mental illness, trauma exposure); demographics (e.g., age, racial/ethnic minority, low socioeconomic status), and circumstantial (e.g., a particular dealer supplying a "hot dose", recent release from jail or prison affecting physical tolerance, uctuations in potency in the local illegal opioid supply) (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28).…”

mentioning

confidence: 99%

Associations among Drug Acquisition and Use Behaviors, Psychosocial Attributes, and Opioid-Involved Overdoses: A SEM Analysis

Swartz,

Zhao,

Jacobucci

et al. 2024

Preprint

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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. 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. We used interview data collected for two studies recruiting opioid-misusing participants receiving services from a community-based syringe service 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). 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. To increase effectiveness, prevention efforts might address the interacting overdose risks that span multiple functional domains.

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Characterising methamphetamine/amphetamine use among opioid agonist therapy‐seeking adults with prescription‐type opioid use disorder in Canada

Langlois,

Fairbairn,

Jutras‐Aswad

et al. 2024

Drug and Alcohol Review

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IntroductionThere has been a significant increase in methamphetamine/amphetamine use in North America, particularly among people who use opioids. Despite its association with several negative health consequences, the population of people who use methamphetamine/amphetamine with opioids is not well characterised. The aim of this study was to investigate correlates of methamphetamine/amphetamine use among adults with prescription‐type opioid use disorder (POUD) starting methadone or buprenorphine/naloxone as part of a pragmatic randomised treatment trial in Canada.MethodsMultivariable logistic regression analyses were used to determine factors associated with baseline methamphetamine/amphetamine use (measured by urine drug test [UDT]) among participants of a pan‐Canadian pragmatic trial conducted between 2017 and 2020 comparing supervised methadone versus flexible take‐home dosing buprenorphine/naloxone models of care in people with POUD (e.g., licit or illicit, including fentanyl, prescribed or not).ResultsThe sample included 269 participants, of which 142 (52.8%) had positive baseline methamphetamine/amphetamine UDT. In the multivariable model, positive fentanyl UDT (adjusted odds ratio [AOR] 13.21, 95% confidence interval [CI] 6.45, 28.30), non‐fatal overdose in the last 6 months (AOR 2.26, CI 1.01, 5.17) and a lifetime history of opioid agonist therapy exposure prior to study entry (AOR 2.30, CI 1.09, 4.87) remained positively associated with baseline methamphetamine/amphetamine use.Discussion and ConclusionsIn this sample of people with POUD, methamphetamine/amphetamine use was associated with markers of complex and severe OUD, including overdose risk. This suggests the need for targeted interventions to optimise treatment outcomes and prevent future overdoses in this population.Clinical Trial Registration: Available at: ClinicalTrials.gov NCT03033732.

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Correlates of nonfatal overdose among treatment-seeking individuals with non-heroin opioid use disorder: Findings from a pragmatic, pan-Canadian, randomized control trial

Cited by 5 publications

References 45 publications

Associations among drug acquisition and use behaviors, psychosocial attributes, and opioid-involved overdoses

Associations among drug acquisition and use behaviors, psychosocial attributes, and opioid-involved overdoses

Associations among Drug Acquisition and Use Behaviors, Psychosocial Attributes, and Opioid-Involved Overdoses: A SEM Analysis

Characterising methamphetamine/amphetamine use among opioid agonist therapy‐seeking adults with prescription‐type opioid use disorder in Canada

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