A dimensional approach to understanding severity estimates and risk correlates of marijuana abuse and dependence in adults.

While item response theory (IRT) research shows a latent severity trait underlying response patterns of substance abuse and dependence symptoms, little is known about IRT-based severity estimates in relation to clinically relevant measures. In response to increased prevalences of marijuana-related treatment admissions, an elevated level of marijuana potency, and the debate on medical marijuana use, we applied dimensional approaches to understand IRT-based severity estimates for marijuana use disorders (MUDs) and their correlates while simultaneously considering gender- and race/ethnicity-related differential item functioning (DIF). Using adult data from the 2008 National Survey on Drug Use and Health (N = 37,897), Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria for MUDs among past-year marijuana users were examined by IRT, logistic regression, and multiple indicators-multiple causes (MIMIC) approaches. Among 6917 marijuana users, 15% met criteria for a MUD; another 24% exhibited subthreshold dependence. Abuse criteria were highly correlated with dependence criteria (correlation = 0.90), indicating unidimensionality; item information curves revealed redundancy in multiple criteria. MIMIC analyses showed that MUD criteria were positively associated with weekly marijuana use, early marijuana use, other substance use disorders, substance abuse treatment, and serious psychological distress. African Americans and Hispanics showed higher levels of MUDs than Whites, even after adjusting for race/ethnicity-related DIF. The redundancy in multiple criteria suggests an opportunity to improve efficiency in measuring symptom-level manifestations by removing low-informative criteria. Elevated rates of MUDs among African Americans and Hispanics require research to elucidate risk factors and improve assessments of MUDs for different racial/ethnic groups.

Duke Scholars

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