Factors associated with concurrent illicit use of opiates and crack/cocaine among opiate-users in treatment: implications for treatment services in England

Abstract

Background: The aim of this study was to identify factors associated with concurrent illicit drug use of opiates and crack/cocaine use among individuals receiving of opioid medication-assisted treatment (MAT) in one English rural/urban County Council area.

Methods: 776 opiate users in treatment were assessed using the Addiction Dimensions for Assessment and Personalised Treatment (ADAPT) assessment tool. The tool encompasses three domains and 14 subdomains covering addiction severity, recovery strengths and coexisting health and social needs. Data were opportunistically matched to the National Drug Treatment Monitoring System (NDTMS) and the Treatment Outcome Profile (TOP). Two backward stepwise logistic regression models were run to discern the nature of concurrent illicit drug use.

Results: Addiction severity (Odds Ratio [OR] 12.55, Confidence interval [CI] 6.49–24.27), low recovery strengths (OR 2.30, CI 1.30–4.07) and no ‘urge/control’ (OR 27.45, 13.18–57.16) were strongly associated with concurrent use. Individuals with moderate psychological needs were more likely to be abstinent (OR 2.97, CI 1.67–5.29) compared to those with no need. Abstaining from injecting (OR 2.38, CI 1.15–4.93), alcohol consumption (OR 1.55, CI 1.05–2.30), increasing age (OR 1.03, CI 1.01-1.06) and increased quality-of-life (OR 1.05, CI 1.00–1.10) were associated with abstinence from concurrent use.

Conclusion: Practitioner assessments with self-report data offer unique perspectives on service users’ holistic needs. Interventions addressing concurrent use during MAT should consider managing urges and control of illicit Class A use, injecting and alcohol consumption within a stepped-care approach. Packages for developing recovery strengths supporting psychological need and enhancing quality-of-life is recommended.

Keywords:

• Treatment
• abstinence
• concurrent drug use

Acknowledgements

Thanks to Dr Tim Millar from Manchester University for comments on drafts of the paper, James Anderton for preparing and matching the three datasets and to Terry Pearson for ongoing support throughout the study.

Disclosure statement

The authors report no conflict of interest.