Logistic Regression With Machine Learning Sheds Light on the Problematic Sexual Behavior Phenotype

Objectives: There has been a longstanding debate about whether the mechanisms involved in problematic sexual behavior (PSB) are similar to those observed in addictive disorders, or related to impulse control or to compulsivity. The aim of this report was to contribute to this debate by investigating the association between PSB, addictive disorders (internet addiction, compulsive buying), measures associated with the construct known as reward deficiency (RDS), and obsessive-compulsive disorder (OCD). Methods: A Canadian university Office of the Registrar invited 68,846 eligible students and postdoctoral fellows. Of 4710 expressing interest in participating, 3359 completed online questionnaires, and 1801 completed the Mini-International Neuropsychiatric Interview. PSB was measured by combining those screening positive (score at least 6) on the Sexual Addiction Screening Test—Revised Core with those self-reporting PSB. Current mental health condition(s) and childhood trauma were measured by self-report. OCD was assessed by a combination of self-report and Mini-International Neuropsychiatric Interview data. Results: Of 3341 participants, 407 (12.18%) screened positive on the Sexual Addiction Screening Test—Revised Core. On logistic regression, OCD, attention deficit, internet addiction, a family history of PSB, childhood trauma, compulsive buying, and male gender were associated with PSB. On multiple correspondence analysis, OCD appeared to cluster separately from the other measures, and the pattern of data differed by gender. Conclusions: In our sample, factors that have previously been associated with RDS and OCD are both associated with increased odds of PSB. The factors associated with RDS appear to contribute to a separate data cluster from OCD and to lie closer to PSB