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Algorithm-Driven Substance Use Disorder Treatment for Inner-City Clients With Serious Mental Illness and Multiple
Gregory J McHugo1, Robert E Drake2, William R Haslett3
1The Dartmouth Institute for Health Policy and Clinical Practice.
The Journal of Nervous and Mental Disease
|January 27, 2021
Summary
Algorithm-driven substance use disorder treatments show promise for urban mental health clients facing multiple challenges. While many clients improve, recovery is often complicated by co-occurring conditions.
Area of Science:
- Psychiatry
- Addiction Medicine
- Public Health
Background:
- Urban mental health clients with serious mental illness face numerous chronic stressors.
- Substance use disorder (SUD) significantly complicates recovery for this population.
- Co-occurring conditions include poverty, homelessness, incarceration, and trauma.
Purpose of the Study:
- To examine the efficacy of algorithm-driven SUD treatments.
- To assess treatment outcomes for inner-city clients with multiple challenges.
- To identify factors influencing recovery in this complex demographic.
Main Methods:
- A cohort of 305 inner-city mental health clients with SUD was studied.
- Algorithm-driven SUD treatments were implemented.
- Substance use was assessed quarterly via self-reports and case manager ratings over 2 years.
Main Results:
- 66% (n=200) of clients completed 2 years of treatment.
- One-fourth of completers achieved abstinence and improved function (responders).
- Half were partial responders (reduced use), and one-fourth were nonresponders.
Conclusions:
- Evidence-based SUD interventions can be effective for multiply impaired, inner-city clients.
- Numerous complications can hinder successful recovery.
- Algorithm-driven approaches offer a viable treatment pathway, though challenges remain.
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