Based on the premise that treatment changes people in ways that are consequential for subsequent treatment-seeking, we question the validity of an unrecognized and apparently inadvertent assumption in mental health services research conducted within a psychiatric epidemiology paradigm. This homogeneity assumption statistically constrains the effects of potential determinants of recent treatment to be identical for former patients and previously untreated persons by omitting treatment history or modeling only main effects. We test this assumption with data from the 2001–2003 Collaborative Psychiatric Epidemiology Surveys; the weighted pooled sample is representative of noninstitutionalized U.S. adults (18+; analytic n = 19,227). Contrary to the homogeneity assumption, some associations with recent treatment are conditional on past treatment, including psychiatric disorder and race-ethnicity—measures of need and treatment disparities, respectively. We conclude that the widespread application of the homogeneity assumption probably masks differences in the determinants of recent use between previously untreated persons and former patients.
Machine Learning, Natural Language Processing, and the Electronic Health Record: Innovations in Mental Health Services Research
