Depression is a common mental health disorder that often starts during adolescence, with potentially important future consequences including ‘Not in Education, Employment or Training’ (NEET) status.

We took a structured life course modeling approach to examine how depressive symptoms during adolescence might be associated with later NEET status, using a high-quality longitudinal data resource. We considered four plausible life course models: (1) an early adolescent sensitive period model where depressive symptoms in early adolescence are more associated with later NEET status relative to exposure at other stages; (2) a mid adolescent sensitive period model where depressive symptoms during the transition from compulsory education to adult life might be more deleterious regarding NEET status; (3) a late adolescent sensitive period model, meaning that depressive symptoms around the time when most adults have completed their education and started their careers are the most strongly associated with NEET status; and (4) an accumulation of risk model which highlights the importance of chronicity of symptoms.

Our analysis sample included participants with full information on NEET status (N = 3951), and the results supported the accumulation of risk model, showing that the odds of NEET increase by 1.015 (95% CI 1.012–1.019) for an increase of 1 unit in depression at any age between 11 and 24 years.

Given the adverse implications of NEET status, our results emphasize the importance of supporting mental health during adolescence and early adulthood, as well as considering specific needs of young people with re-occurring depressed mood.

Young adults who are not in employment, education, or training (NEET) are at risk of long-term economic disadvantage and social exclusion. Knowledge about risk factors for being NEET largely comes from cross-sectional studies of vulnerable individuals. Using data collected over a 10-year period, we examined adolescent predictors of being NEET in young adulthood.

We used data on 1938 participants from the Victorian Adolescent Health Cohort Study, a community-based longitudinal study of adolescents in Victoria, Australia. Associations between common mental disorders, disruptive behaviour, cannabis use and drinking behaviour in adolescence, and NEET status at two waves of follow-up in young adulthood (mean ages of 20.7 and 24.1 years) were investigated using logistic regression, with generalised estimating equations used to account for the repeated outcome measure.

Overall, 8.5% of the participants were NEET at age 20.7 years and 8.2% at 24.1 years. After adjusting for potential confounders, we found evidence of increased risk of being NEET among frequent adolescent cannabis users [adjusted odds ratio (ORadj) = 1.74; 95% confidence interval (CI) 1.10–2.75] and those who reported repeated disruptive behaviours (ORadj = 1.71; 95% CI 1.15–2.55) or persistent common mental disorders in adolescence (ORadj = 1.60; 95% CI 1.07–2.40). Similar associations were present when participants with children were included in the same category as those in employment, education, or training.

Young people with an early onset of mental health and behavioural problems are at risk of failing to make the transition from school to employment. This finding reinforces the importance of integrated employment and mental health support programmes.

Optimizing functional recovery in young individuals with severe mental illness constitutes a major healthcare priority. The current study sought to quantify the cognitive and clinical factors underpinning academic and vocational engagement in a transdiagnostic and prospective youth mental health cohort. The primary outcome measure was ‘not in education, employment or training’ (‘NEET’) status.

A clinical sample of psychiatric out-patients aged 15–25 years (n = 163) was assessed at two time points, on average, 24 months apart. Functional status, and clinical and neuropsychological data were collected. Bayesian structural equation modelling was used to confirm the factor structure of predictors and cross-lagged effects at follow-up.

Individually, NEET status, cognitive dysfunction and negative symptoms at baseline were predictive of NEET status at follow-up (p < 0.05). Baseline cognitive functioning was the only predictor of follow-up NEET status in the multivariate Bayesian model, while controlling for baseline NEET status. For every 1 s.d. deficit in cognition, the probability of being disengaged at follow-up increased by 40% (95% credible interval 19–58%). Baseline NEET status predicted poorer negative symptoms at follow-up (β = 0.24, 95% credible interval 0.04–0.43).

Disengagement with education, employment or training (i.e. being NEET) was reported in about one in four members of this cohort. The initial level of cognitive functioning was the strongest determinant of future NEET status, whereas being academically or vocationally engaged had an impact on future negative symptomatology. If replicated, these findings support the need to develop early interventions that target cognitive phenotypes transdiagnostically.