Exploring specific predictors of psychosis onset over a 2-year period: A decision-tree model.

AIM: The fluctuating symptoms of clinical high risk for psychosis hamper conversion prediction models. Exploring specific symptoms using machine-learning has proven fruitful in accommodating this challenge. The aim of this study is to explore specific predictors and generate atheoretical hypotheses of onset using a close-monitoring, machine-learning approach. METHODS: Study participants, N = 96, mean age 16.55 years, male to female ratio 46:54%, were recruited from the Prevention of Psychosis Study in Rogaland, Norway. Participants were assessed using the Structured Interview for Psychosis Risk Syndromes (SIPS) at 13 separate assessment time points across 2 years, yielding 247 specific scores. A machine-learning decision-tree analysis (i) examined potential SIPS predictors of psychosis conversion and (ii) hierarchically ranked predictors of psychosis conversion. RESULTS: Four out of 247 specific SIPS symptom scores were significant: (i) reduced expression of emotion at baseline, (ii) experience of emotions and self at 5 months, (iii) perceptual abnormalities/hallucinations at 3 months and (iv) ideational richness at 6 months. No SIPS symptom scores obtained after 6 months of follow-up predicted psychosis. CONCLUSIONS: Study findings suggest that early negative symptoms, particularly those observable by peers and arguably a risk factor for social exclusion, were predictive of psychosis. Self-expression and social behaviour might prove relevant entry points for early intervention in psychosis and psychosis risk. Testing study results in larger samples and at other sites is warranted.

Late-onset smokers: how many, and associations with health behaviours and socioeconomic status.

AIMS: Adolescence is considered a critical phase for smoking initiation, while smoking initiation in adulthood has received less attention. In the present study, we investigated smoking initiation in early adulthood, with an additional focus on how socioeconomic status (SES) relates to late-onset smoking. METHODS: The Norwegian Longitudinal Health Behaviour Study followed a representative sample of adolescents (n=530) from age 13 (7th grade) to age 30 (years 1990-2007) through 9 waves of data collection. Information was collected on the adolescents' gender, smoking, alcohol use, cannabis use, physical activity, and SES. Those reporting to be smokers at age 30, but not at age 18, were defined as "late-onset smokers". RESULTS: At age 30, 21% were daily smokers, of which 45% met our definition of late-onset smoking. Participants with a low SES had a higher odds ratio for late-onset smoking (OR=3.42) and a lower odds ratio for quitting smoking (OR=0.40) after adjusting for confounders. CONCLUSIONS: Early adulthood is an additional critical phase for daily smoking initiation. The clear and consistent negative association between SES and late-onset smoking, as well as the positive association between SES and smoking cessation, contribute to the association between SES and smoking in the general adult population.