The study aimed to examine the clinical correlates of polysubstance dependence.

Seven hundred and fifty two substance-dependent subjects were interviewed with the Mini-International Neuropsychiatric Interview, the Brown–Goodwin Assessment for Lifetime History of Aggression (BGLHA), and the Hamilton Depression Rating Scale (HDRS). Subjects completed the Childhood Trauma Questionnaire (CTQ), Eysenck Personality Questionnaire (EPQ), and Barratt Impulsivity Scale (BIS). Subjects found to have polysubstance dependence were compared with subjects with monosubstance dependence.

Polysubstance dependence was found in 48.3% of the subjects. Subjects with polysubstance dependence were significantly younger, more were separated/divorced and unemployed, and they had significantly higher CTQ scores for childhood emotional and physical neglect, higher EPQ psychoticism scores, higher BGLHA aggression scores, and higher BIS impulsivity scores. Significantly more of the polysubstance dependent subjects had attempted suicide, self-mutilated, and exhibited aggressive behavior. Significantly more monosubstance dependent subjects had an Axis I psychiatric disorder and they had higher HDRS depression scores.

Polysubstance dependence is common among the groups studied and may be associated with certain socio-demographic, developmental, and personality factors.

A powerful technique for inference concerning spatial dependence in a random field is to use spectral methods based on frequency domain analysis. Here we develop a nonparametric Bayesian approach to statistical inference for the spectral density of a random field. We construct a multi-dimensional Bernstein polynomial prior for the spectral density and devise a Markov chain Monte Carlo algorithm to simulate from the posterior of the spectral density. The posterior sampling enables us to obtain a smoothed estimate of the spectral density as well as credible bands at desired levels. Simulation shows that our proposed method is more robust than a parametric approach. For illustration, we analyse a soil data example.