No abstract

Methodological issues involved in assessing the prevalence of substance abuse in schizophrenia are discussed, and previous research in this area is comprehensively reviewed. Many studies suffer from methodological shortcomings, including the lack of diagnostic rigor, adequate sample sizes, and simultaneous assessment of different types of substance abuse (e.g., stimulants, sedatives). In general, the evidence suggests that the prevalence of substance abuse in schizophrenia is comparable to that in the general population, with the possible exceptions of stimulant and hallucinogen abuse, which may be greater in patients with schizophrenia. Data are presented on the association of substance abuse with demographics, diagnosis, history of illness, and symptoms in 149 recently hospitalized DSM-III-R schizophrenic, schizophreniform, and schizoaffective disorder patients. Demographic characteristics were strong predictors of substance abuse, with gender, age, race, and socioeconomic status being most important. Stimulant abusers tended to have their first hospitalization at an earlier age and were more often diagnosed as having schizophrenia, but did not differ in their symptoms from nonabusers. A history of cannabis abuse was related to fewer symptoms and previous hospitalizations, suggesting that more socially competent patients were prone to cannabis use. The findings show that environmental factors may be important determinants of substance abuse among schizophrenic-spectrum patients and that clinical differences related to abuse vary with different types of drugs.

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The prevalence and demographic and clinical correlates of lifetime substance use disorders were examined in a cohort of 325 recently hospitalized psychiatric patients (53% schizophrenia or schizoaffective disorder). Alcohol use was the most common type of substance use disorder, followed by cannabis and cocaine use. Univariate analyses indicated that gender (male), age (younger), education (less), history of time in jail, conduct disorder symptoms, and antisocial personality disorder symptoms were predictive of substance use disorders. Lifetime cannabis use disorder was uniquely predicted by marital status (never married) and fewer psychiatric hospitalizations during the previous 6 months. Optimal classification tree analysis, an exploratory, nonlinear method of identifying patient subgroups, was successful in predicting 74 percent to 86 percent of the alcohol, cannabis, and cocaine use disorders. The implications of this method for identifying specific patient subgroups and service needs are discussed.