Algorithm-Driven Substance Use Disorder Treatment for Inner-City Clients With Serious Mental Illness and Multiple Impairments

Abstract

Mental health clients with serious mental illness in urban settings experience multiple chronic stresses related to poverty, unemployment, discrimination, homelessness, incarceration, hospitalization, posttraumatic stress disorder, pain syndromes, traumatic brain injury, and other problems. Substance use disorder exacerbates these difficulties. This study examined the efficacy of algorithm-driven substance use disorder treatments for 305 inner-city mental health clients with multiple challenges. Researchers assessed substance use quarterly using a combination of standardized self-reports and case manager ratings. Of the 305 multiply impaired clients who began treatment, 200 (66%) completed 2 years of treatment. One fourth (n = 53) of the completers were responders who developed abstinence and improved community function; one half (n = 97) were partial responders, who reduced substance use but did not become abstinent; and one fourth (n = 50) were nonresponders. Evidence-based interventions for substance use disorder can be effective for multiply impaired, inner-city clients, but numerous complications may hinder recovery.